U.S. patent application number 14/783522 was filed with the patent office on 2016-11-03 for system and methods for cross-sensor linearization.
This patent application is currently assigned to Halliburton Energy Services, Inc.. The applicant listed for this patent is HALLIBURTON ENERGY SERVICES, INC.. Invention is credited to Dingding Chen, Bin Dai, Darren Gascooke, Tian He, Christopher M. Jones.
Application Number | 20160320527 14/783522 |
Document ID | / |
Family ID | 56284769 |
Filed Date | 2016-11-03 |
United States Patent
Application |
20160320527 |
Kind Code |
A1 |
Chen; Dingding ; et
al. |
November 3, 2016 |
SYSTEM AND METHODS FOR CROSS-SENSOR LINEARIZATION
Abstract
A method includes obtaining a plurality of master sensor
responses with a master sensor in a set of training fluids and
obtaining node sensor responses in the set of training fluids. A
linear correlation between a compensated master data set and a node
data set is then found for a set of training fluids and generating
node sensor responses in a tool parameter space from the
compensated master data set on a set of application fluids. A
reverse transformation is obtained based on the node sensor
responses in a complete set of calibration fluids. The reverse
transformation converts each node sensor response from a tool
parameter space to the synthetic parameter space, and uses
transformed data as inputs of various fluid predictive models to
obtain fluid characteristics. The method includes modifying
operation parameters of a drilling or a well testing and sampling
system according to the fluid characteristics.
Inventors: |
Chen; Dingding; (Tomball,
TX) ; Dai; Bin; (Spring, TX) ; Jones;
Christopher M.; (Houston, TX) ; Gascooke; Darren;
(Houston, TX) ; He; Tian; (Houston, TX) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
HALLIBURTON ENERGY SERVICES, INC. |
Houston |
TX |
US |
|
|
Assignee: |
Halliburton Energy Services,
Inc.
Houston
TX
|
Family ID: |
56284769 |
Appl. No.: |
14/783522 |
Filed: |
December 29, 2014 |
PCT Filed: |
December 29, 2014 |
PCT NO: |
PCT/US2014/072489 |
371 Date: |
October 9, 2015 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
E21B 47/07 20200501;
G01V 8/10 20130101; E21B 49/08 20130101; E21B 47/06 20130101; G01V
13/00 20130101; E21B 7/00 20130101; E21B 47/135 20200501 |
International
Class: |
G01V 13/00 20060101
G01V013/00; E21B 47/06 20060101 E21B047/06; E21B 49/08 20060101
E21B049/08; G01V 8/10 20060101 G01V008/10; E21B 7/00 20060101
E21B007/00 |
Claims
1. A method, comprising: obtaining a plurality of master sensor
responses with a master sensor in a set of training fluids;
obtaining a plurality of node sensor responses with a plurality of
node sensors in the set of training fluids; finding a linear
correlation between a compensated master data set and a node data
set for the set of training fluids; generating a plurality of node
sensor responses in a tool parameter space from the compensated
master data set on a set of application fluids; obtaining a reverse
transformation based on the plurality of node sensor responses in a
complete set of calibration fluids, the reverse transformation
transforming each node sensor response from a tool parameter space
to a synthetic parameter space; obtaining fluid characteristics
with synthetic fluid predictive models using reverse-transformed
inputs from at least one of the node sensor responses to a fluid
measurement; and modifying operation parameters of a drilling or a
well testing and sampling system according to the fluid
characteristics, wherein the complete set of calibration fluids
comprises the set of training fluids and the set of application
fluids.
2. The method of claim 1, further comprising: selecting a weighting
factor to compensate the master sensor responses with the node
sensor responses according to the linear correlation between the
compensated master data set and the node data set.
3. The method of claim 2, wherein selecting the weighting factor
comprises selecting a weighting factor that results in a high
linear correlation between the compensated master data set and the
node data set, wherein the high linear correlation indicates a low
data transformation error.
4. The method of claim 2, wherein selecting the weighting factor
comprises determining a set of cross-sensor linearized coefficients
comprising the weighting factor for each channel pair, using an
exhaustive searching loop through multi-iteration training.
5. The method of claim 1, further comprising selecting the master
sensor and at least one of the plurality of node sensors from two
sensors having a same design, two sensors having a same
configuration, and two sensors from a fabrication batch.
6. The method of claim 1, further comprising selecting the master
sensor and at least one of the plurality of node sensors from
different fabrication batches, and from two sensors having a same
design and having a same configuration.
7. The method of claim 1, further comprising selecting the master
sensor and at least one of the plurality of node sensors from
different fabrication batches having a different design, and with a
same number of elements having the same denomination.
8. The method of claim 1, further comprising selecting the master
sensor and at least one of the plurality of node sensors from
different designs, from different configurations, and originating
from different fabrication batches.
9. The method of claim 1, further comprising truncating a master
data set to a same number of samples as a node data set, wherein
each sample in the master data set comprises a measurement having a
temperature setting and pressure setting similar to a temperature
setting and a pressure setting of at least one measurement in the
node data set.
10. The method of claim 1, wherein finding a linear correlation
between a compensated master data and the node data set comprises
applying a weight factor to a difference between a synthetic node
sensor response and a synthetic master sensor response for a
reference fluid selected from the set of training fluids, and
adding the weighted difference to a master sensor response measured
from the reference fluid.
11. The method of claim 1, further comprising determining at least
one of a node sensor response and a master sensor response in the
synthetic parameter space with a dot product of a fluid spectral
response vector and a convolved spectral response vector for one of
the at least one node sensor or the master sensor.
12. The method of claim 1, further comprising obtaining fluid
characteristics with a synthetic fluid predictive model using the
reverse transformed inputs from at least one of the node sensor
responses to a fluid measurement.
13. The method of claim 1, wherein obtaining a plurality of node
sensor responses with a plurality of node sensors on the set of
training fluids comprises measured and simulated node sensor
responses from reference fluids at broad ranges of temperature
settings and pressure settings.
14. The method of claim 1, further comprising: collecting optical
responses from a plurality of petroleum fluids with known
characteristics using the selected master sensor; generating a
plurality of synthetic node sensor responses associated with the
plurality of petroleum fluids using the reverse transformation with
cross-sensor linearized node sensor inputs in tool parameter space;
and calibrating a fluid characterization model using the plurality
of synthetic node sensor responses.
15. A method, comprising: determining a value for a node sensor
response in synthetic parameter space using a two-dimensional
temperature and pressure interpolation with given node sensor
responses at specified temperatures and pressures; determining a
value for a master sensor response in synthetic parameter space
using the two-dimensional temperature and pressure interpolation
with given master sensor response at specified temperatures and
pressures; determining a difference between the value for a node
sensor response and the value for a master sensor response;
determining a set of cross-sensor linearized model coefficients in
an optimization loop; adjusting a master sensor channel selection
to simulate a channel response of the node sensor; obtaining a
reverse transformation using the simulated channel responses of the
node sensor; and modifying operation parameters of a drilling or a
well testing and sampling system according to a fluid
characteristic obtained with a synthetic fluid predictive model
using node sensor responses as input to the reverse
transformation.
16. The method of claim 15, wherein determining the value for a
node sensor response and determining the value for a master sensor
response comprises varying temperature and pressure conditions for
a plurality of node sensor responses and master sensor
responses.
17. The method of claim 15, wherein adjusting the master sensor
channel selection to simulate a channel response of the node sensor
comprises: identifying a set of cross-sensor linearized
coefficients for a node sensor channel based on a plurality of
training fluids; and obtaining the channel response of the node
sensor on a plurality of application fluids with the set of
cross-sensor linearized model coefficients.
18. The method of claim 15, wherein obtaining a reverse
transformation using the simulated channel responses of the node
sensor comprises combining the node sensor response on a plurality
of training fluids and a plurality of application fluids to develop
the reverse transformation model with a neural network.
19. The method of claim 15, further comprising adjusting a master
sensor channel selection from a master sensor to simulate a node
sensor channel response from a node sensor.
20. The method of claim 19, wherein the master sensor channel has
the same or different nominal element as the node sensor
channel.
21. A method, comprising: introducing a tool into a wellbore
drilled into one or more subterranean formations, the tool having
been previously calibrated for operation by: obtaining a plurality
of master sensor responses with a master sensor in a set of
training fluids; obtaining a plurality of node sensor responses
with a plurality of node sensors in the set of training fluids,
each of the plurality of node sensors and the master sensor
including an optical element; finding a linear correlation between
a compensated master data set and a node data set for the set of
training fluids; generating a plurality of node sensor responses in
a tool parameter space from the compensated master data set on a
set of application fluids; and obtaining a reverse transformation
based on the plurality of node sensor responses in a complete set
of calibration fluids, wherein the complete set of calibration
fluids comprises the set of training fluids and the set of
application fluids; determining a fluid characteristic from the
plurality of node sensor responses in the synthetic parameter space
using the reverse transformation and a synthetic fluid predictive
model; and modifying operation parameters of a drilling or a well
testing and sampling according to the fluid characteristic.
22. The method of claim 21, wherein obtaining a plurality of node
sensor responses with a plurality of node sensors in the set of
training fluids during the tool calibration comprises determining a
temperature setting and a pressure setting of the reduced set of
training fluids according to a temperature setting and a pressure
setting of the master set of training fluids.
23. The method of claim 21, further comprising, during the tool
calibration, selecting one of the plurality of master sensors and
one of the plurality of node sensors from two sensors having at
least one of different designs, different configurations, or
different fabrication batches.
Description
BACKGROUND
[0001] Current practice in optical sensor calibration is typically
performed on a sensor-by-sensor basis, applying a standard
procedure to each optical sensor and using the same number of
reference fluids for all optical sensors involved. Attempts to
reduce the number of reference fluids in a calibration procedure
may over-fit the selected data sets and fail to adequately
generalize results for downhole application fluids. This is
especially problematic for calibration techniques adopting a
non-linear mapping algorithm. On the other hand, increasing the
number of calibration fluids for each optical sensor has the
drawback of low safety and high cost.
[0002] Adding to the complexity of current strategies for
standardizing optical sensor response is the desire to perform
reference measurements of calibration fluids at multiple
temperature settings, multiple pressure settings, and different
combinations of temperature and pressure settings. Combining
measured and simulated optical sensor responses on reference fluids
has been an attractive technique to solve the calibration and
standardization problem. However, cross-correlation of optical
sensor data from multiple reference fluids may be non-linear when
no spectral correction is available for each optical sensor. When
the cross-correlation is non-linear, it is difficult to develop a
method applicable to additional reference fluids based on the
measurements of a reduced number of reference fluids. What is
missing in optical sensor calibration and standardization
techniques is a cross-sensor data linearization method applicable
over different optical element designs and different optical sensor
configurations in optical tool parameter space.
BRIEF DESCRIPTION OF THE DRAWINGS
[0003] The following figures are included to illustrate certain
aspects of the present disclosure, and should not be viewed as
exclusive embodiments. The subject matter disclosed is capable of
considerable modifications, alterations, combinations, and
equivalents in form and function, without departing from the scope
of this disclosure.
[0004] FIG. 1 illustrates a calibration system used to calibrate
one or more optical elements.
[0005] FIG. 2 illustrates a general transformation model framework
including a forward transformation and a reverse transformation
with neural networks.
[0006] FIG. 3 depicts a hierarchical structure for reverse
transformation models.
[0007] FIG. 4 illustrates a schematic flowchart of a general method
for cross-sensor linearization, and its application for improving
reverse transformation and fluid characterization.
[0008] FIGS. 5A-5C illustrate a cross-sensor linearization
procedure for a sensor channel including an integrated
computational element (ICE) for measuring methane (`methane
ICE`).
[0009] FIGS. 6A-6C illustrate a cross-sensor linearization
procedure for a sensor channel including a Gas-Oil-Ratio (GOR)
ICE.
[0010] FIGS. 7A-7C illustrate a cross-sensor linearization
procedure for a sensor channel including an Aromatics (ARO)
ICE.
[0011] FIGS. 8A-8C illustrate a cross-sensor linearization
procedure for a sensor channel including a Saturates (SAT) ICE.
[0012] FIG. 9 illustrates a schematic flowchart of a method to be
implemented for cross-sensor linearization, and its application for
improving reverse transformation and fluid characterization.
[0013] FIG. 10 illustrates a schematic flowchart of a method for
cross-sensor linearization with use of a pre-processing procedure
to prepare matched data pairs for master sensor and node
sensor.
[0014] FIG. 11 illustrates a schematic flowchart of a method for
cross-sensor linearization with use of a two-dimensional
temperature and pressure interpolation scheme for correcting
synthetic sensor response and generating optimized model
coefficients on the training fluids.
[0015] FIG. 12 illustrates a schematic flowchart of a method for
cross-sensor linearization by estimating and including a node
sensor response for an extended set of reference fluids for robust
reverse transformation.
[0016] FIG. 13 is a drilling system configured to use a calibrated
optical sensor for modifying a drilling parameter or configuration
in a measurement-while-drilling (MWD) and a logging-while-drilling
(LWD) operations.
[0017] FIG. 14 is a wireline system configured to use a calibrated
optical sensor during formation testing and sampling.
DETAILED DESCRIPTION
[0018] The present disclosure relates to calibration of optical
sensors for downhole optical fluid analysis and enhancement of
optical sensor technology. More specifically, the present
disclosure provides a method for cross-sensor linearization to
improve tool standardization.
[0019] Optical computing devices, also commonly referred to as
"opticoanalytical devices," can be used to analyze and monitor a
substance in real time. Such optical computing devices will often
employ an optical element or optical processing element that
optically interacts with the substance or a sample thereof to
determine quantitative and/or qualitative values of one or more
physical or chemical properties of the substance. The optical
element may be, for example, an integrated computational element
(ICE), also known as a multivariate optical element (MOE), which is
essentially an optical interference-based device that can be
designed to operate over a continuum of wavelengths in the
electromagnetic spectrum from the UV to mid-infrared (MIR) ranges,
or any sub-set of that region. Electromagnetic radiation that
optically interacts with a substance is changed and processed by
the ICE so as to be readable by a detector, such that an output of
the detector can be correlated to the physical or chemical property
of the substance being analyzed. Other examples of optical elements
may include band-pass filters, notch filters, neutral density
filters, beam-splitters, polarizing beamsplitters, prisms,
diffraction gratings, Fresnel lenses, and the like.
[0020] An ICE (hereafter "ICE core") typically includes a plurality
of optical layers consisting of various materials whose index of
refraction and size (e.g., thickness) may vary between each layer.
An ICE core design refers to the number and thickness of the
respective layers of the ICE core. The layers may be strategically
deposited and sized to selectively pass predetermined fractions of
electromagnetic radiation at different wavelengths configured to
substantially mimic a regression vector corresponding to a
particular physical or chemical property of interest of a
substance. Accordingly, an ICE core design will exhibit a
transmission function that is weighted with respect to wavelength.
As a result, the output light intensity from the ICE core conveyed
to a detector may be related to the physical or chemical property
of interest for the substance.
[0021] The terms "optical computing device" and "optical sensor"
are used herein interchangeably and refer generally to a sensor
configured to receive an input of electromagnetic radiation that
has interacted with a substance and produced an output of
electromagnetic radiation from an optical element arranged within
or otherwise forming part of the optical computing device. The
processing element may be, for example, an ICE core as described
above. Prior to field use, the optical computing device, and each
optical element employed therein, is calibrated such that each is
able to operate effectively upon being exposed to downhole
conditions. When an optical computing device is not properly
calibrated, the resulting transmission functions derived from each
optical element may provide well operators with inaccurate
measurements upon deployment.
[0022] After manufacture, and before being placed in downhole use,
each optical computing device is carefully calibrated against known
reference fluids for temperature and pressure ranges expected to be
encountered in the field. The calibrated optical computing devices
are then installed as part of a downhole tool and re-tested to
validate the optical responses from the optical element.
[0023] Embodiments described herein provide methods and systems for
calibrating and standardizing optical sensor response during tool
validation testing and field testing. Moreover, the presently
described methods improve tool standardization by using a
cross-sensor linearization of optical channel responses associated
with a plurality of optical sensors. More particularly, a method is
provided for optical sensor standardization, which may be
applicable to pressure-volume-temperature (PVT) characterization of
a downhole fluid, for optical sensor manufacturing calibration, for
tool validation testing, and for field data post-processing.
[0024] Downhole optical fluid processing often starts with data
mapping to convert optical sensor measurements into compatible
inputs of various pre-determined fluid characterization models. A
fluid characterization model may include fluid composition models
to determine the different components of a fluid, such as a
Gas-Oil-Ratio (GOR), an aromatics content, or a saturated
hydrocarbon content. In some embodiments, fluid composition models
include property predictive models of the fluid, such as viscosity,
density, or phase (solid, liquid, gas, or a combination). A data
mapping algorithm, also called `reverse transformation` or
`instrument standardization` algorithm, is built on a set of
reference fluids through manufacturing calibration. To ruggedize
downhole fluid analysis, quality data transformation through a
reasonable selection of reference fluids is desirable to develop a
reliable reverse transformation. Candidate reference fluids in
general are chosen from good representatives of formation/petroleum
fluids. Furthermore, reference fluids are measured over a wide
range of temperature and pressure settings to better simulate
downhole conditions while maintaining safe laboratory operating
conditions. It is desirable to obtain a wide dynamic range in
optical sensor responses during the calibration measurements.
Embodiments disclosed herein allow a suitable trade-off between
using adequate number of reference fluids for robust calibration
and minimizing total operational cost, according to the specific
measurement conditions.
[0025] Embodiments consistent with the present disclosure include
linearizing a cross-correlation of data from multiple sensors and
for multiple reference fluids. In some embodiments, the data is
obtained with optical sensors selected within the same
fabrication/calibration batch. In some embodiments, the data is
obtained from optical sensors selected among different fabrication
batches and with the same design. In other embodiments, the data
may be obtained from optical sensors selected among different
designs having the same optical sensor configuration. In yet other
embodiments, the data is obtained from optical sensors selected
from different designs, having different sensor configurations.
[0026] In some embodiments, calibration against a full-set of
reference fluids is performed on a selected number of `master
sensors` during manufacturing. Other sensors in the manufacturing
batch, or in a different batch, are calibrated against a
reduced-set of reference fluids. These sensors are referred to as
`node sensors.` The reduced-set of reference fluids may be a
plurality of reference fluids that are safe and cost-effective to
operate in laboratory conditions. A reverse transformation
algorithm is built using data collected from the full-set of
reference fluids. To obtain the reverse transformation, master
sensor data may include only measured optical responses on the
full-set of reference fluids. On the other hand, node sensor data
may include a hybrid set having measured optical responses from the
reduced-set of reference fluids, and simulated optical responses
from reference fluids in the full-set of reference fluids that are
not included in the reduced-set of reference fluids. In that
regard, some embodiments use a linear or near-linear transformation
algorithm using compensated master sensor responses as input for
simulating the optical responses for node sensors against the
reference fluids in the full-set of reference fluids. The
transformation algorithm is based on a channel-by-channel data
correlation on the reduced-set of reference fluids between the
master sensor and the node sensor. The specific type of optical
element used in optical sensors according to embodiments disclosed
herein may include ICE cores or other types of optical devices or
elements, such as narrow band-pass (NBP) filters and the like. In
general, embodiments disclosed herein are consistent with any type
of optical device or element used in an optical sensor. Embodiments
of the present disclosure are particularly desirable when a
non-linear correlation between a master sensor response and a node
sensor response is observed for a reduced-set of reference
fluids.
[0027] In some embodiments, a method includes obtaining a plurality
of master sensor responses with a master sensor in a set of
training fluids and obtaining node sensor responses in the set of
training fluids. The method further includes finding a linear
correlation between a compensated master data set and a node data
set for a set of training fluids and generating node sensor
responses in a tool parameter space from the compensated master
data set on a set of application fluids. Further, the method
includes obtaining a reverse transformation based on the node
sensor responses in a complete set of calibration fluids, the
reverse transformation transforming each node sensor response from
a tool parameter space to the synthetic parameter space and
obtaining fluid characteristics using reverse-transformed inputs.
The method includes modifying operation parameters of a drilling or
a well testing and sampling according to the fluid
characteristics.
[0028] In some embodiments, a method includes determining an
estimated value for a node sensor response in synthetic parameter
space using a two-dimensional interpolation for temperature and
pressure, and determining an estimated value for a master sensor
response in synthetic parameter space using the two dimensional
interpolation for temperature and pressure. The method further
includes determining a difference between the estimated value for a
node sensor response and the estimated value for a master sensor
response, determining a set of cross-sensor linearized model
coefficients in an optimization loop, and adjusting a master sensor
channel selection to simulate a particular channel response of the
node sensor. Further, the method includes storing a set of
cross-sensor linearized model coefficients for a node sensor
channel, and modifying operation parameters of a drilling or a well
testing and sampling according to a fluid characteristic obtained
with a reverse transformation and a synthetic fluid predictive
model. The reverse transformation is obtained using the stored set
of cross-sensor linearized model coefficients.
[0029] In yet other embodiments, a method, includes introducing a
tool into a wellbore drilled into one or more subterranean
formations, the tool having been previously calibrated for
operation by performing a number of steps. The steps for
calibrating the tool may include: obtaining a plurality of master
sensor responses with a master sensor in a set of training fluids,
obtaining a plurality of node sensor responses with a plurality of
node sensors in the set of training fluids, each of the plurality
of node sensors and the master sensor including an optical element,
finding a linear correlation between a compensated master data set
and a node data set for a set of training fluids, generating a
plurality of node sensor responses in a tool parameter space from
the compensated master data set on a set of application fluids, and
obtaining a reverse transformation based on the plurality of node
sensor responses in a complete set of calibration fluids, wherein
the complete set of calibration fluids comprises the set of
training fluids and the set of application fluids. The method
further includes determining a fluid characteristic from the
plurality of node sensor responses in the synthetic parameter space
using the reverse transformation and a synthetic fluid predictive
model, and modifying operation parameters of a drilling or a well
testing and sampling according to the fluid characteristic.
[0030] FIG. 1 illustrates an exemplary manufacturing calibration
system 100 that may be used to calibrate one or more optical
elements used in an optical sensor. As illustrated, system 100 may
include a measurement system 102 in optical communication with one
or more optical elements 104 (shown as 104a, 104b, 104c . . . 104n)
that are to be calibrated. Each optical element 104a-n may be
either an optical band-pass filter or a multivariate optical
element/integrated computational element (e.g., an ICE core). The
measurement system 102 may circulate one or more reference fluids
with different chemical compositions and properties (i.e., methane
concentration, aromatics concentration, saturates concentration,
GOR, etc.) through an optic cell 106 over widely varying
calibration conditions of temperature, pressure, and density, such
that optical transmission and/or reflection measurements of each
reference fluid in conjunction with each optical element 104a-n may
be made at such conditions.
[0031] The measurement system 102 may comprise an optical
pressure-volume-temperature (PVT) instrument, and the reference
fluids circulated in the measurement system 102 may comprise
representative fluids commonly encountered in downhole
applications. System 100 may collect output signals from each
optical element 104a-n for each specified reference fluid at
varying calibration conditions. In some cases, the reference fluids
may comprise seven representative fluids that are easy to operate
for manufacturing calibration, namely, dodecane, nitrogen, water,
toluene, 1-5 pentanediol, and two liquid crude oils or fluids with
no gas concentration (e.g., dead oil). The crude reservoir oils
used as reference fluids may be, for example, global oil library 13
(or "GOL13"), and global oil library 33 (or "GOL33"). In other
cases, the reference fluids may include samples of live oils mixed
with dead oil and hydrocarbon gas, such as methane for example, and
the samples of hydrocarbon gases and/or CO.sub.2. Manufacturing
calibration of the optical sensor may serve the need of detector
output re-scaling or instrument standardization.
[0032] The measurement system 102 may vary each reference fluid
over several set points spanning varying calibration conditions. To
accomplish this, as illustrated, the measurement system 102 may
include a liquid charging system 108, a gas charging system 110, a
temperature control system 112, and a pressure control system 114.
The liquid charging system 108 injects reference fluids into the
fluid circuit to introduce fluid varying perturbations such that
calibrating the optical elements 104a-n will incorporate all the
expected compounds found in the particular reference fluid. The gas
charging system 110 may inject known gases (e.g., N.sub.2,
CO.sub.2, H.sub.2S, methane, propane, ethane, butane, combinations
thereof, and the like) into the circulating reference fluids. The
temperature control system 112 may vary the temperature of the
reference fluid to simulate several temperature set points that the
optical elements 104a-n may encounter downhole. Lastly, the
pressure control system 114 may vary the pressure of the reference
fluid to simulate several pressure set points that the optical
elements 104a-n may encounter downhole.
[0033] The optic cell 106 is fluidly coupled to each system 108,
110, 112, and 114 to allow the reference fluids to flow
therethrough and recirculate back to each of the systems 108, 110,
112, and 114 in a continuous, closed-loop fluid circuit. While
circulating through the optic cell 106, a light source 116 emits
electromagnetic radiation 118 that passes through the optic cell
106 and the reference fluid flowing therethrough. As the
electromagnetic radiation 118 passes through the optic cell 106 it
optically interacts with the reference fluid and generates sample
interacted light 120, which includes spectral data for the
particular reference fluid circulating through the measurement
system 102 at the given calibration conditions or set points. The
sample interacted light 120 may be directed toward optical elements
104a-n which, as illustrated, may be arranged or otherwise disposed
on a sensor wheel 122 configured to rotate in the direction A.
While shown as arranged in a single ring on the sensor wheel 122,
optical elements 104a-n may alternatively be arranged in two or
more rings on the sensor wheel 122.
[0034] During calibration, the sensor wheel 122 may be rotated at a
predetermined frequency such that each optical element 104a-n may
optically interact with the sample interacted light 120 for a brief
period and sequentially produce optically interacted light 124 that
is conveyed to a detector 126. The detector 126 may be generally
characterized as an optical transducer and may comprise, but is not
limited to, a thermal detector (e.g., a thermopile), a
photoacoustic detector, a semiconductor detector, a piezo-electric
detector, a charge coupled device (CCD) detector, a video or array
detector, a split detector, a photon detector (e.g., a
photomultiplier tube), photodiodes, and any combination thereof.
Upon receiving individually-detected beams of optically interacted
light 124 from each optical element 104a-n, the detector 126 may
generate or otherwise convey corresponding response signals 128 to
a data acquisition system 130. The data acquisition system 130 may
time multiplex each response signal 128 received from the detector
126 corresponding to each optical element 104a-n. A corresponding
set of resulting output signals 132 is subsequently generated and
conveyed to a data analysis system 134 for processing and providing
input parameters for various fluid predictive models with use of
outputs from each optical element 104a-n as a candidate variable.
Data analysis system 134 may be coupled to a computer 140, which
may include a memory 142 and a processor 144. Memory 142 may store
commands which, when executed by processor 144, cause computer 140
to perform at least some of the steps in the methods described
herein and otherwise consistent with the present disclosure.
[0035] Once the sensor wheel 122 is calibrated, one or more
calibrated sensor wheels 122 may then be installed on an optical
tool with other system components and otherwise placed in an
optical computing device for assembly validation testing. To
validate the optical response of the tool assembly, the optical
tool may be placed in an oven that regulates the ambient
temperature and pressure. The reference fluids used to calibrate
the sensor wheel 122 may then be selectively circulated through the
optical tool at similar set points used to calibrate the optical
elements 104a-n. More particularly, the reference fluids may be
circulated through the optical tool at various set point downhole
conditions (i.e., elevated pressures and temperatures) to obtain
measured optical responses.
[0036] While manufacturing calibration of the sensor wheel 104a-n
using reference fluids is performed in a tool parameter space,
fluid spectroscopic analysis and fluid predictive model calibration
using a large amount of data in a standard oil library is performed
in a synthetic parameter space (also called Optical-PVT data
space). Synthetic sensor responses of each element are calculated
as a dot product of full-wavelength-range of fluid spectrometry and
sensor element spectrum excited by a light source, which might
nonlinearly or linearly vary in scale compared to the actual sensor
response due to the difference between the mathematic approximation
used in calculating synthetic sensor response and the real system
implementation. To compensate for the difference above, the
measurement data from the optical tool can be transformed from the
tool parameter space to the synthetic parameter space first through
a reverse transformation algorithm before applying fluid predictive
models. Also, fluid predictive models can be calibrated with
different synthetic optical inputs and saved in an optical fluid
model base to provide sufficiency and adaptation in dealing with
uncertainty of data transformation and improving formation fluid
compositional analysis and field data interpretation.
[0037] In current practice, an optical fluid model is sensor
dependent, including data transformation (i.e., standardization)
models and property predictive models. To provide adequate
flexibility for optical data processing and interpretation, an
optical fluid model includes the following candidate constituents:
transformation models calibrated on selected reference fluids
through reverse transformation, transformation models calibrated on
selected reference fluids through forward transformation, and
predictive models calibrated on both Optical-PVT database and
sensor wheel 122 data spaces.
[0038] Transformation model development using selected reference
fluids requires matched calibration data pairs of optical sensor
responses simulated in the synthetic parameter space and measured
in a tool data space of sensor wheel 122. In synthetic parameter
space, simulated sensor responses on reference fluids are available
at the ideal temperature and pressure setting points. Measured
optical responses of the sensor wheel 122 may endure slight
temperature and pressure variation during manufacturing
calibration. In some embodiments, matched transformation data pairs
are obtained through two-dimensional interpolation by using actual
temperatures and pressures as inputs to generate simulated sensor
responses at the corresponding measurement conditions. Depending on
the data space in which the fluid property predictive models are
calibrated, data transformation models convert measured or
simulated optical sensor output between a tool data space and a
synthetic parameter space. FIG. 2 illustrates one such
transformation.
[0039] More particularly, FIG. 2 illustrates an embodiment of a
general transformation model framework with a multi-input,
multi-output neural network that may be applied by the data
analysis system 134 of FIG. 1 to optical responses. The model that
converts the actual optical sensor response channels (SW/Ch01-Ch0n)
from tool parameter space 201 to synthetic parameter space 202
(PVT/Ch01-Ch0n) is a reverse transformation 203. The model that
converts data from synthetic parameter space 202 into tool
parameter space 201 is a forward transformation 205. Although the
illustrated general transformation model framework in FIG. 2 is
configured with multi-input/multi-output non-linear neural
networks, there is no limitation in using other non-linear and
linear transformation algorithms with single-input/single-output
and multi-input/single-output configurations.
[0040] FIG. 3 illustrates an embodiment of a hierarchical structure
for reverse transformation models 302. The variations of
transformation models 302 may include converting optical channels
304 for each optical sensor in a single model, converting the
disjoined optical channels in several detector-based models 306, or
converting only selected channels 308 of interest each time in
different individual models. Compared to a single model
implementation, multi-model options can improve the reliability of
data construction in the output (i.e., transformed) parameter
domain (e.g., synthetic parameter space 202, cf. FIG. 2) if one or
more of the optical channels (e.g., tool parameter space 201, cf.
FIG. 2), as a transformation input, experience a problem. A
plurality of reference fluid blocks 310-320, at the bottom of the
hierarchical structure and coupled to the various channels 304-308,
represent the transformation models that can be built based on
different reference fluids (e g., minimum number of reference
fluids 310, 314, 318 and extended reference fluids 312, 316, 320).
The minimum number of reference fluids may refer to the seven
representative fluids discussed above. These reference fluids are
safe to use in a laboratory configuration and easy to clean for
testing purposes. Optical sensor responses (e.g., tool parameter
space 201) generally have a wide dynamic range as a representation
of diverse fluids in an existing Optical-PVT database. Extended
reference fluids often include one or more fluids such as live oil
and/or gas to cover a wider dynamic range and provide a more robust
transformation model.
[0041] In some embodiments, reverse transformation 203 converts
tool measurements from tool parameter space 201 into synthetic
parameter space 202 prior to applying fluid characterization
models. Accordingly, fluid characterization models use data from
synthetic parameter space 202 as input to obtain information such
as fluid composition, and physical properties of the fluid. A
forward transformation 205 can be used to convert a whole set of
simulated optical sensor responses from synthetic parameter space
202 to tool parameter space 201 prior to developing predictive
models on tool parameter space 201. As seen in FIG. 2, forward
transformation 205 can be created by switching the input and the
output of a neural network model. In other words, using a
synthetic-channel response as an input, and a measured sensor wheel
channel response as an output a neural network can then be trained
to calibrate forward transformation algorithms.
[0042] As will be appreciated, a hierarchical structure for the
reverse transformation models 302, as illustrated in FIG. 3, can
also be applied to forward transformation models. After forward
transformation 205 is developed, it can be used to convert the
synthetic sensor responses of the global samples in synthetic
parameter space 202 into tool parameter space 201. Then the fluid
property predictive models can be calibrated in tool parameter
space 201, and the further transformation is not needed in field
data processing because measured optical responses from the tool
can be used as model inputs directly for fluid compositional
analysis. Compared to the reverse transformation, which applies
on-line tool data conversion each time before making a fluid
prediction, forward transformation usually only applies one time
off-line to convert synthetic sensor responses for fluid prediction
model development.
[0043] By applying a transformation model to the optical responses
derived from the optical sensor, the optical sensor is calibrated
for use and ready for validation testing in any number of downhole
tools. Such a sensor, calibrated over a full set of reference
fluids, may be characterized and otherwise referred to herein as a
`master sensor`. During tool validation testing, one or more master
sensors may be installed in a tool that is to be introduced
downhole to obtain wellbore measurements. In some embodiments, as
described below, the downhole tool may form part of a bottom hole
assembly used in a drilling operation. In such embodiments, the
tool may comprise any of a number of different types of tools
including MWD (measurement-while-drilling) tools, LWD
(logging-while-drilling) tools, and others. In other embodiments,
however, the tool may be used in a wireline operation and otherwise
form part of a wireline logging tool, such as a probe or sonde, to
be lowered by wireline or logging cable into a borehole to obtain
measurements.
[0044] In some embodiments, an optical sensor installed in a tool
may be characterized as a `node sensor,` and at least some
reference fluids that were used to calibrate the master sensor may
be run through the node sensor at the same or similar set points
(i.e., elevated pressures and temperatures). In some embodiments,
the tool validation testing is undertaken at a laboratory facility.
In such cases, the same reference fluids used to calibrate the
optical sensor may be used. In other cases, however, or in addition
to laboratory testing, tool validation testing may be undertaken
on-site, such as at a drill rig or wellhead installation where the
tool is to be used in a wellbore operation. In such cases, a
limited number of reference fluids may be used, such as water and
nitrogen. Optical responses derived from the tool during validation
testing may be normalized by using the transformation model (e.g.,
reverse transformation 203, forward transformation 205) to adjust
the output of the tool validation process. The optical responses
may then be compared against the optical responses of the master
sensor.
[0045] FIG. 4 illustrates a flowchart including steps in a general
method 400 for cross-sensor linearization and its application for
improving reverse transformation and fluid characterization,
according to some embodiments. Method 400 may be performed by a
computer device having a memory and a processor (e.g., computer
140, memory 142, and processor 144, cf. FIG. 1). The memory may
store commands that, when executed by the processor, cause the
computer to perform at least some of the steps in method 400.
Methods consistent with method 400 may include at least one but not
all of the steps in method 400, performed in any order.
Furthermore, methods consistent with the scope of method 400 may
include at least some of the steps in method 400 performed
overlapping in time, or even simultaneously. Methods consistent
with method 400 may include measuring reference fluids with a
measurement system using an optical sensor having a plurality of
optical elements (e.g., measurement system 102 and optical elements
104a-n, cf. FIG. 1). In some embodiments, the plurality of optical
elements in method 400 may correspond to the same optical sensor,
or may belong to different optical sensors. Accordingly, the
plurality of optical elements in method 400 may be selected within
the same fabrication batch, among the different fabrication
batches, within the same optical element design, among different
optical element designs, within the same sensor configuration or
among different sensor configurations. In some embodiments, the
measurement system in method 400 may include a reverse
transformation or a forward transformation between sensor responses
in tool parameter space and sensor responses in synthetic parameter
space (e.g., tool parameter space 201, synthetic parameter space
202, reverse transformation 203, and forward transformation
205).
[0046] Step 402 includes obtaining a plurality of master sensor
responses, P.sub.m, with a master sensor on a set of training
fluids. In some embodiments, the set of training fluids is master
set of reference fluids including a representative or comprehensive
set of reference fluids for a downhole oil and gas exploration
measurement. Step 404 includes obtaining a plurality of node sensor
responses, P.sub.n, with a plurality of node sensors on the set of
training fluids. In some embodiments, the set of training fluids is
a reduced set of reference fluids such as a sub-set of the master
set of reference fluids. Accordingly, in some embodiments the
master sensor and the node sensors each measure the reduced set of
reference fluids.
[0047] Step 406 includes selecting a weighting factor, C, to
calculate a plurality of compensated master sensor responses with
the plurality of node sensor responses. In some embodiments, step
406 includes master and node sensor responses for the reduced set
of reference fluids, which is common to both master and node
sensors. Accordingly, step 406 includes obtaining compensated
master sensor responses by adding the actual measured master sensor
responses to the reduced set of reference fluids to a weighted
difference between a node sensor response in synthetic parameter
space and a master sensor response in synthetic parameter space.
Sensor responses in synthetic parameter space are the Optical-PVT
responses used for fluid product calibration (cf. synthetic
parameter space 202, FIG. 2). In some embodiments, step 406 may
include determining the spectral response of at least one optical
element (e.g., an ICE) in the optical sensor at either elevated
temperature and pressure, or room temperature and pressure.
[0048] The weighted difference between responses from the master
sensor and the node sensors in synthetic parameter space
incorporates cross-sensor variations in optical signal intensity
induced by a variation of the optical elements. The variation
between optical elements may be a result of manufacturing
fluctuations within the same fabrication batch. In some
embodiments, the variation between optical elements may be a result
of variations between different designs of the optical elements. In
yet other embodiments, the variations between optical elements may
be a result of different optical sensor configuration. In some
embodiments, step 406 includes selecting a weighting factor, C, in
a mathematical equation as shown below:
P.sub.cm=P.sub.m+C(S.sub.n-S.sub.m) (1)
[0049] where P.sub.cm is the compensated master sensor response on
the reduced set of reference fluids in tool parameter space; and
S.sub.n and S.sub.m are the node sensor and master sensor
responses, respectively, in the synthetic parameter space on the
same reduced set of reference fluids. The sensor response in
synthetic parameter space at each channel is a dot product (scalar
product) of a fluid spectroscopic response vector (R.sub.fl) and a
convolved spectroscopic response vector of a particular optical
element (R.sub.oe). In some embodiments, R.sub.fl and R.sub.oe are
vectors in a wavelength parameter space. In that regard, the
convolved spectroscopic response may include a spectral emissivity
of light source 116 (FIG. 1), a band pass filter transmittance, or
any other spectroscopic response of the optical element.
Accordingly, in some embodiments step 406 includes performing
mathematical operations in the following equation to find the
synthetic node sensor and master sensor response (S.sub.n,m):
S.sub.n,m=R.sub.flR.sub.n,m (2)
[0050] Since the fluid spectroscopy (R.sub.fl) is the same for
S.sub.n and for S.sub.m, the difference of (S.sub.n-S.sub.m)
indicates the variation of convolved transmittance spectrum of node
and master sensor elements (R.sub.n,m). The weighting factor C in
Eq. (1) is channel dependent. In some embodiments, step 406
includes selecting a weighting factor C in Eq. (1) from a validated
range (between -3 to +3 with an increment step of 0.1 for example)
to optimize a linear correlation between the compensated master
sensor responses obtained from Eq.(1) and the node sensor responses
associated with the reduced set of reference fluids. Using the
selected weighting factor C for each optical channel, a linear
model correlating master sensor responses with node sensor
responses for the reduced set of reference fluids is obtained in
step 408.
[0051] Step 408 includes finding a linear correlation between the
compensated master data set and the node data set for a set of
training fluids. More particularly, step 408 includes determining a
linear correlation between the compensated master sensor responses
and a plurality of measured node sensor responses, as follows:
P.sub.n=kP.sub.cm+b (3)
[0052] In some embodiments, the linear correlation expressed in Eq.
(3) involves node sensor responses P.sub.n in tool parameter space.
In some embodiments, step 408 includes testing the linear
correlation in Eq. 3 with a selected value of C, according to step
406. When a new value of C in step 406 renders a better
linearization according to step 408, the new value of C is
preferred over the old value. When a selected value of C optimizes
the linear Eq. (3) on the reduced reference fluids, node sensor
responses on additional reference fluids would be robust against
different variations of the optical elements. In some embodiments,
step 408 includes constructing synthetic node sensor responses over
the master set of reference fluids, using Eq. (2). The synthetic
response set may be denoted as:
S.sub.nf=[S.sub.nS.sub.nr]
[0053] where S.sub.nf is the full set of synthetic node sensor
responses, S.sub.n includes the synthetic node sensor responses
corresponding to the reduced set of reference fluids, and S.sub.nr
includes the node sensor responses in synthetic parameter space
corresponding to the reference fluids in the master set that are
not included in the reduced set. In some embodiments, obtaining the
response set S.sub.nr in synthetic parameter space as illustrated
above may include performing mathematical operations including
vectors R.sub.fl and R.sub.n (cf. Eq. 2), which are known from
laboratory measurements or which may be retrieved from library
databases.
[0054] Step 410 includes generating a plurality of node sensor
responses in a tool parameter space from the compensated master
data set on a set of application fluids. Step 410 includes
calculating node sensor responses on additional fluids to complete
the master set of reference fluids. After parameters C, k and b are
determined on the reduced reference fluids (training fluids),
embodiments consistent with the present disclosure simulate node
sensor responses on additional reference fluids (application
fluids) using the parameters C, k, and b (cf. Eqs. (1) and (3)).
Step 410 includes obtaining a hybrid set of node sensor responses
in the tool parameter space for the master set of reference fluids.
The hybrid set may be denoted as:
P.sub.nf=[P.sub.n P.sub.nr]
[0055] where P.sub.n are the node sensor responses in tool
parameter space for reference fluids in the reduced set, and
P.sub.nr are estimated node sensor responses for reference fluids
in the master set that are not included in the reduced set. To
obtain the set P.sub.nr, in some embodiments, step 410 may include
using the set S.sub.nr and S.sub.mr obtained in step 408 to
determine a set of compensated master responses according to Eq.
(1), where the value of C is already selected. Once the compensated
master sensor responses are known, step 410 may include using the
linear coefficients (k and b, cf. Eq. 3) found in step 408, to
obtain the node sensor responses in tool parameter space for the
full set of reference fluids.
[0056] This application is especially useful for non-linear neural
network based reverse transformation, in which multi-input and
multi-output mapping can be better implemented with wider range of
data to robustly convert tool measurement data to the synthetic
parameter space prior applying fluid composition and property
predictive models.
[0057] Step 412 includes obtaining a reverse transformation based
on the plurality of node sensor responses on a complete set of
calibration fluids. According to some embodiments, the complete set
of calibration fluids comprises the set of training fluids and the
set of application fluids. Step 412 includes obtaining a reverse
transformation, f, between a plurality of node sensor responses in
tool parameter space P.sub.nf and a plurality node sensor responses
S.sub.nf in synthetic parameter space. After simulated optical
responses on additional reference fluids for node sensors are
calculated, the reverse transformation algorithm converting optical
responses from tool parameter space to synthetic parameter space
for the full-set of reference fluids can be built. Step 412 may
include performing non-linear (as shown in FIG. 2) or linear
regression analysis. Accordingly, a calibrated node sensor response
may be obtained using the reverse transformation, f, as
follows:
S.sub.nf=f(P.sub.nf) (4)
[0058] Step 414 includes obtaining fluid characteristics with
synthetic fluid predictive models using reverse-transformed inputs
from at least one of the node sensor responses to a fluid
measurement. In some embodiments, step 414 includes implementing
improved reverse transformation algorithms and existing fluid
characterization models on real-time data processing software for
downhole optical fluid analysis. When optical data associated with
formation fluids is collected with downhole optical tool, it is
input to reverse transformation algorithm (e.g., function fin Eq.
(4)), then applied to pre-calibrated various fluid predictive
models in determining fluid characteristics from the plurality of
transformed optical responses. Fluid predictive models are
typically calibrated with synthetic optical sensor responses on
large number of fluid samples and diverse analyte data. In some
embodiments, step 414 may include at least one of developing,
modifying, or using a fluid characterization algorithm in tool
parameter space when actual optical responses of adequate number of
petroleum fluids is collected on a typical master sensor or
multiple sensors. Simulated node sensor responses would include
more petroleum fluids by using a cross-sensor data correlation
analysis as described in steps 402-414. Moreover, step 414 may
include calibrating fluid composition and property predictive
models using data in the tool parameter space directly with no need
of reverse transformation during field data processing.
Accordingly, in some embodiments the reverse transformation `f` of
Eq. (4) may be used to convert validated field data from the tool
parameter space to synthetic parameter space. The transformed field
data may then be combined with existing lab data in synthetic
parameter space, and directly incorporated into the development of
fluid characterization models, including fluid composition and
property predictive models.
[0059] Step 416 includes modifying operation parameters for
drilling or well testing and sampling system according to the
estimated fluid characteristics. A drilling parameter may be a
drilling speed, a drilling configuration, such as steering the
drill bit between a vertical or quasi-vertical drilling
configuration and a horizontal or quasi-horizontal drilling
configuration. Further according to some embodiments, step 416 may
include adjusting the port, rate and direction of pump-out during
well testing after the drilling is completed to improve the fluid
sampling and contamination analysis.
[0060] A downhole optical tool according to embodiments disclosed
herein is used with formation testing and sampling after drilling
is complete. Accordingly, in some embodiments step 418 includes
processing optical sensor measurements with optional data
correction and standardization algorithms to minimize the
uncertainty of reverse transformation, and obtaining a robust
estimation of the fluid characteristics to help
decision-making.
[0061] FIGS. 5A-5C, FIGS. 6A-6C, FIGS. 7A-7C, and FIGS. 8A-8C
demonstrate the applications of the disclosed methods in four
different scenarios, respectively. There are three subplots in each
set of figures. The first subplot (FIGS. 5A, 6A, 7A, and 8A)
compares the difference of the convolved transmittance spectrum of
ICE element between the master and node sensor. The second subplot
(FIGS. 5B, 6B, 7B, and 8B) shows the master-node data correlation
without spectra correction. The third subplot (FIGS. 5C, 6C, 7C,
and 8C) shows the linearized master-node data correlation using the
invented method.
[0062] FIGS. 5A-5C illustrate a cross-sensor linearization
procedure for a sensor channel including a methane ICE, according
to some embodiments. More particularly, FIGS. 5A-5C illustrate a
scenario in which the master sensor and node sensor are selected
from the same batch of manufacturing calibration with little
temperature and pressure variation for each data pair. The sensor
configurations are the same, and the optical element design is also
the same. Without limitation, the optical element in FIGS. 5A-5C is
a methane ICE on the master and node sensors.
[0063] FIG. 5A illustrates chart 500A with a master sensor spectral
response 502 and a node sensor spectral response 504. Chart 500A
includes wavelength in the abscissae and transmission in the
ordinates. Spectral responses 502 and 504 may be used in R.sub.n,m
for the calculation of the sensor response in synthetic parameter
space (cf. Eq. (2) above).
[0064] FIG. 5B illustrates chart 500B with a data cross-correlation
between the master sensor and the node sensor illustrated in FIG.
5A. Accordingly, the abscissae 551 in chart 500B correspond to the
master sensor responses in tool parameter space. The ordinates 552
in chart 500B correspond to the node sensor responses in tool
parameter space. The multiple data points in chart 500B correspond
to a reduced set of reference fluids, and are clustered as follows:
data clusters 501a-c correspond to GOL33; data clusters 503a-c
correspond to GOL13; data clusters 505a-c correspond to H.sub.2O
(water); data clusters 507a-c correspond to toluene; data clusters
509a-c correspond to 1-5 pentanediol (PEN); data clusters 511a-c
correspond to dodecane (DOD); and data clusters 513a-c correspond
to N.sub.2 (nitrogen). For each set a-c of data clusters, a
different combination of temperature and pressure settings was used
to measure the specific reference fluid. The typical temperature
and pressure setting points may include combinations of three
temperatures (150, 200, 250 Fahrenheit) and four pressures (3000,
6000, 9000 and 12000 PSI). Furthermore, within each data cluster,
the individual data points correspond to different data collection
events for the given reference fluid at the given temperature and
pressure setting.
[0065] The similarity between master sensor and node sensors in the
specific case of the methane ICE illustrated in FIGS. 5A-5B results
in rapid convergence between master sensor and node sensor data in
tool parameter space even prior to applying a weighting factor to
perform any compensation on the master sensor responses (cf. step
406 in method 400).
[0066] FIG. 5C illustrates a chart 500C with a data
cross-correlation between the master sensor and the node sensor
illustrated in FIG. 5A. Accordingly, the abscissae 553 in chart
500C correspond to the compensated master sensor responses in tool
parameter space. The compensated master sensor responses are
obtained using a method such as method 400, including step 406.
More specifically, data in the abscissae of FIG. 5C may be obtained
by performing mathematical operations such as included in Eq. 1
with a suitable weighting factor, C. The ordinates 552 in chart
500C correspond to the node sensor responses in tool parameter
space.
[0067] FIGS. 6A-6C illustrate a cross-sensor linearization
procedure for a sensor channel including a Gas-Oil-Ratio (GOR) ICE,
according to some embodiments. FIGS. 6A-6C illustrate a scenario in
which the master sensor and node sensor are among different
calibration batches. The sensors are configured in the same way,
and the selected ICE cores (GOR ICE in this case) are fabricated
with the same design.
[0068] FIG. 6A illustrates chart 600A with a master sensor spectral
response 602 and a node sensor spectral response 604. Chart 600A
includes wavelength in the abscissae and transmission in the
ordinates. Spectral responses 602 and 604 may be used in R.sub.n,m
for the calculation of the sensor response in synthetic parameter
space (cf. Eq. (2) above).
[0069] FIG. 6B illustrates chart 600B with a data cross-correlation
between the master sensor and the node sensor illustrated in FIG.
6A. Accordingly, the abscissae 651 in chart 600B correspond to the
master sensor responses in tool parameter space. The ordinates 652
in chart 600B correspond to the node sensor responses in tool
parameter space. The multiple data points in chart 600B correspond
to a reduced set of reference fluids, and are clustered as detailed
above in reference to FIGS. 5A-5C. Accordingly, data clusters
601a-c correspond to GOL33; data clusters 603a-c correspond to
GOL13; data clusters 605a-c correspond to H.sub.2O (water); data
clusters 607a-c correspond to toluene; data clusters 609a-c
correspond to 1-5 pentanediol (PEN); data clusters 611a-c
correspond to dodecane (DOD); and data clusters 613a-c correspond
to N.sub.2 (nitrogen). FIG. 6B Cross-sensor ICE data correlation
cannot be modeled with a single linear function without
compensation.
[0070] FIG. 6C illustrates a chart 600C with a data
cross-correlation between the master sensor and the node sensor
illustrated in FIG. 6A. Accordingly, the abscissae 653 in chart
600C correspond to the compensated master sensor responses in tool
parameter space. The ordinates 652 in chart 600C correspond to the
node sensor responses in tool parameter space. In chart 600C, the
compensated master sensor response has demonstrated better linear
correlation with node sensor response in this scenario, as compared
to chart 600B.
[0071] FIGS. 7A-7C illustrate a cross-sensor linearization
procedure for a sensor channel including an Aromatics (ARO) ICE,
according to some embodiments. FIGS. 7A-7C illustrate a scenario in
which the selected ICE elements (e.g., an ICE for measuring
aromatics--`aromatics ICE`--in this case) on master sensor and node
sensors are not only from different calibration batches, but also
from the different designs.
[0072] FIG. 7A illustrates chart 700A with a master sensor spectral
response 702 and a node sensor spectral response 704. Chart 700A
includes wavelength in the abscissae and transmission in the
ordinates. Spectral responses 702 and 704 may be used in R.sub.n,m
for the calculation of the sensor response in synthetic parameter
space (cf. Eq. (2) above).
[0073] FIG. 7B illustrates chart 700B with a data cross-correlation
between the master sensor and the node sensor illustrated in FIG.
7A. Accordingly, the abscissae 751 in chart 700B correspond to the
master sensor responses in tool parameter space. The ordinates 752
in chart 700B correspond to the node sensor responses in tool
parameter space. The multiple data points in chart 700B correspond
to a reduced set of reference fluids, and are clustered as detailed
above in reference to FIGS. 5A-5C. Accordingly, data clusters
701a-c correspond to GOL33; data clusters 703a-c correspond to
GOL13; data clusters 705a-c correspond to H.sub.2O (water); data
clusters 707a-c correspond to toluene; data clusters 709a-c
correspond to 1-5 pentanediol (PEN); data clusters 711a-c
correspond to dodecane (DOD); and data clusters 713a-c correspond
to N.sub.2 (nitrogen).
[0074] Although sensors have the same number of nominal elements in
configuration, the direct data correlation without spectral
correction appears to be non-linear again on the given reference
fluids. For example, reference fluids associated with data clusters
705c and 709a have similar master sensor responses 751, but
completely different node sensor responses 752. Likewise, reference
fluids associated with data clusters 711a and 709c have similar
master sensor responses 751 but different node sensor responses
752. Moreover, reference fluids associated with data clusters 707a,
707b, and 707c have different master sensor responses 751 but
similar node sensor responses 752.
[0075] FIG. 7C illustrates a chart 700C with a data
cross-correlation between the master sensor and the node sensor
illustrated in FIG. 7A. Accordingly, the abscissae 753 in chart
700C correspond to the compensated master sensor responses in tool
parameter space. The compensated master sensor responses are
obtained using a method such as method 400, including step 406.
More specifically, data in the abscissae of FIG. 7C may be obtained
by performing mathematical operations such as included in Eq. (1)
with a suitable weighting factor, C. The ordinates 752 in chart
700C correspond to the node sensor responses in tool parameter
space. The significantly improved cross-sensor ICE data
linearization is also achieved in this case using the invented
method.
[0076] FIGS. 8A-8C illustrate a cross-sensor linearization
procedure for a sensor channel including an ICE for measuring
saturates, or `saturates (SAT) ICE`, according to some embodiments.
FIGS. 8A-8C illustrate a scenario for cross-sensor ICE data
linearization wherein the calibration batch, the sensor
configuration and the optical element design (SAT ICE in this
example) are all different between master and node elements.
[0077] FIG. 8A illustrates chart 800A with a master sensor spectral
response 802 and a node sensor spectral response 804. Chart 800A
includes wavelength in the abscissae and transmission in the
ordinates. Spectral responses 802 and 804 may be used in R.sub.n,m
for the calculation of the sensor response in synthetic parameter
space (cf. Eq. (2) above).
[0078] FIG. 8B illustrates chart 800B with a data cross-correlation
between the master sensor and the node sensor illustrated in FIG.
8A. Accordingly, the abscissae 851 in chart 800B correspond to the
master sensor responses in tool parameter space. The ordinates 852
in chart 800B correspond to the node sensor responses in tool
parameter space. The multiple data points in chart 800B correspond
to a reduced set of reference fluids, and are clustered as detailed
above in reference to FIGS. 5A-5C. Data clusters 801a-c correspond
to GOL33; data clusters 803a-c correspond to GOL13; data clusters
805a-c correspond to H.sub.2O (water); data clusters 807a-c
correspond to toluene; data clusters 809a-c correspond to 1-5
pentanediol (PEN); data clusters 811a-c correspond to dodecane
(DOD); and data clusters 813a-c correspond to N.sub.2
(nitrogen).
[0079] FIG. 8C illustrates a chart 800C with a data
cross-correlation between the master sensor and the node sensor
illustrated in FIG. 8A. Accordingly, the abscissae 853 in chart
800C correspond to the compensated master sensor responses in tool
parameter space. The compensated master sensor responses are
obtained using a method such as method 400, including step 406.
More specifically, data in the abscissae of FIG. 8C may be obtained
by performing mathematical operations such as included in Eq. (1)
with a suitable weighting factor, C. The ordinates 852 in chart
800C correspond to the node sensor responses in tool parameter
space. FIG. 8C indicates that a reasonably good linear correlation
can be generated using linearization methods consistent with the
present disclosure. Accordingly, methods as disclosed herein
provide a robust approach for standardization of sensor data in
tool parameter space for a wide variety of applications.
[0080] The linear correlation between compensated master sensor
responses and node sensor responses in charts 500C, 600C, 700C, and
800C are expressed mathematically by linear coefficients `k` and
`b`, (cf. Eq. 3). As illustrated, the coefficients `k` and `b` and
the associated weighting coefficient `C` in calculating compensated
master sensor response (cf. Eq. 1) depend on the optical element
and the particular sensor pairs being considered. That is, in some
embodiments the coefficients `k`, `b` and `C` may differ between
chart 500C, chart 600C, chart 700C, and chart 800C (e.g., a methane
ICE in FIGS. 5A-5C, an ICE for measuring Gas-to-Oil-Ratio `GOR ICE`
in FIGS. 6A-6C, an aromatics ICE in FIGS. 7A-7C, and a saturates
ICE in FIGS. 8A-8C, respectively). For different master-node sensor
pairs the coefficients `k`, `b` and `C` may also differ even with
same nominal design of a methane ICE, or a GOR ICE, or an aromatics
ICE, or a saturates ICE.
[0081] FIG. 9 illustrates a schematic flowchart including steps in
a method 900 to be implemented for cross-sensor linearization and
its application for improving reverse transformation and fluid
characterization, according to some embodiments. Method 900 may be
performed by a computer device having a memory and a processor
(e.g., computer 140, memory 142, and processor 144, cf. FIG. 1).
The memory may store commands that, when executed by the processor,
cause the computer to perform at least some of the steps in method
900. Methods consistent with method 900 may include at least one
but not all of the steps in method 900, performed in any order.
Furthermore, methods consistent with the scope of method 900 may
include at least some of the steps in method 900 performed
overlapping in time, or even simultaneously.
[0082] Methods consistent with method 900 may include measuring
reference fluids with a measurement system using an optical sensor
having a plurality of optical elements (e.g., measurement system
102 and optical elements 104a-n, cf. FIG. 1). In some embodiments,
the plurality of optical elements in method 400 may correspond to
the same optical sensor, or may belong to different optical
sensors. Accordingly, the plurality of optical elements in method
400 may be selected within the same fabrication batch, among the
different fabrication batches, within the same optical element
design, among different optical element designs, within the same
sensor configuration or among different sensor configurations. In
some embodiments, the measurement system in method 400 may include
a reverse transformation or a forward transformation between sensor
responses in a tool parameter space and sensor responses in a
synthetic parameter space (e.g., tool parameter space 201,
synthetic parameter space 202, reverse transformation 203, and
forward transformation 205).
[0083] Embodiments consistent with method 900 reduce calibration
costs by linearizing cross-sensor correlation. In method 900 and
embodiments consistent with the method 900, only a small number of
master sensors, which could consist of the selected single or
multiple representative sensors from each generation of sensors
with same configuration and optical element design, are calibrated
on a full-set of reference fluids. The remaining number of node
sensors are calibrated on a reduced-set of reference fluids. In
some embodiments, the reduced set of reference fluids is a sub-set
of the full-set of reference fluids. Method 900 is oriented to
obtain a linear mapping, which relates compensated master sensor
response for each optical element to a node sensor response in a
tool parameter space based on the reduced-set of reference fluids
(cf. chart 500C, 600C, 700C, and 800C, above). Further, methods
consistent with method 900 include obtaining node sensor responses
on additional reference fluids from the linear model just found.
Accordingly, methods consistent with method 900 simplify
calibration procedures for node sensor by reducing the measurements
on reference fluids.
[0084] Method 900 describes the procedure to generate node sensor
responses in synthetic and tool parameter space on additional
reference fluids with channel-by-channel linearization as disclosed
herein. Step 902 includes identifying a pair of a master sensor and
a node sensor sharing a reduced set of reference fluids. Step 904
includes determining a difference between the responses of the node
sensor and the master sensor in the synthetic parameter space for
the reduced set of reference fluids to calculate compensated master
sensor response in tool parameter space. Step 906 includes
correlating values for a compensated master sensor response with
values for the measured node sensor response. Step 908 includes
determining a node sensor response for additional reference fluids
in a tool parameter space using channel-by-channel linearization
models with the compensated master sensor response as input.
[0085] Step 908 is applied to new data using the models developed
on the training data of reduced-set of reference fluids. The
additional reference fluids as new data may include live oil sample
and methane, the important representatives of petroleum fluids.
Since the master sensor has measurements of full-set reference
fluids available, and the difference of synthetic node sensor and
master sensor response can be calculated in synthetic parameter
space, the calculation of pseudo node sensor response is
straightforward using the same equations described above (cf. Eqs.
1-3).
[0086] Step 910 includes determining a reverse transformation using
the node sensor response for a complete set of reference fluids.
Step 912 includes adjusting a response from an optical sensor in a
wellbore using the reverse transformation to obtain a
characteristic of a wellbore fluid from the adjusted response. Step
914 includes modifying operation parameters of drilling or well
testing and sampling according to the characteristic of the
wellbore fluid.
[0087] FIG. 10 illustrates a flowchart including steps in a method
1000 as a pre-processing procedure for cross-sensor linearization
according to some embodiments, to generate matched data pairs of
master and node sensors in tool parameter space. Method 1000 may be
performed by a computer device having a memory and a processor
(e.g., computer 140, memory 142, and processor 144, cf. FIG. 1).
The memory may store commands that, when executed by the processor,
cause the computer to perform at least some of the steps in method
1000. Methods consistent with method 1000 may include at least one
but not all of the steps in method 1000, performed in any order.
Furthermore, methods consistent with the scope of method 1000 may
include at least some of the steps in method 1000 performed
overlapping in time, or even simultaneously.
[0088] Methods consistent with method 1000 may include measuring
reference fluids with a measurement system using an optical sensor
having a plurality of optical elements (e.g., measurement system
102 and optical elements 104a-n, cf. FIG. 1). In some embodiments,
the plurality of optical elements in method 1000 may correspond to
the same optical sensor, or may belong to different optical
sensors. Accordingly, the plurality of optical elements in method
1000 may be selected within the same fabrication batch, among the
different fabrication batches, within the same optical element
design, among different optical element designs, within the same
sensor configuration or among different sensor configurations. In
some embodiments, the measurement system in method 1000 may include
a reverse transformation or a forward transformation between sensor
responses in a tool parameter space and sensor responses in a
synthetic parameter space (e.g., tool parameter space 201,
synthetic parameter space 202, reverse transformation 203, and
forward transformation 205).
[0089] Method 1000 includes matching data pairs from the master
sensor and the node sensor to a reduced-set of reference fluids.
The reduced-set of reference fluids may include water, nitrogen,
one or two typical dead oils, toluene, pentanediol and dodecane,
which are safe to take measurements under the specified temperature
and pressure settings. Step 1002 includes collecting master sensor
data for a plurality of reference fluids at a plurality of
temperature and pressure settings and for a plurality of channels.
Step 1004 includes collecting node sensor data for the reduced set
of reference fluids from the plurality of reference fluids at a
plurality of temperature and pressure settings.
[0090] Step 1006 includes sorting measured master and node sensor
responses on each reference fluid in the reduced set at each
stabilized temperature and pressure setting. Step 1008 includes
truncating data pairs to the same number of sample points under
each condition according to which data size is smaller. Step 1010
includes combining multi-fluid data pairs to form a complete data
set.
[0091] FIG. 11 illustrates a flowchart including steps in a method
1100 for cross-sensor linearization with use of a two-dimensional
temperature and pressure interpolation scheme for correcting
synthetic sensor response and generating optimized model
coefficients on the training fluids, according to some embodiments.
Method 1100 may be performed by a computer device having a memory
and a processor (e.g., computer 140, memory 142, and processor 144,
cf. FIG. 1). The memory may store commands that, when executed by
the processor, cause the computer to perform at least some of the
steps in method 1100. Methods consistent with method 1100 may
include at least one but not all of the steps in method 1100,
performed in any order. Furthermore, methods consistent with the
scope of method 1100 may include at least some of the steps in
method 1100 performed overlapping in time, or even
simultaneously.
[0092] Methods consistent with method 1100 may include measuring
reference fluids with a measurement system using an optical sensor
having a plurality of optical elements (e.g., measurement system
102 and optical elements 104a-n, cf. FIG. 1). In some embodiments,
the plurality of optical elements in method 1100 may correspond to
the same optical sensor, or may belong to different optical
sensors. Accordingly, the plurality of optical elements in method
1100 may be selected within the same fabrication batch, among the
different fabrication batches, within the same optical element
design, among different optical element designs, within the same
sensor configuration or among different sensor configurations. In
some embodiments, the measurement system in method 1100 may include
a reverse transformation or a forward transformation between sensor
responses in a tool parameter space and sensor responses in a
synthetic parameter space (e.g., tool parameter space 201,
synthetic parameter space 202, reverse transformation 203, and
forward transformation 205).
[0093] The flowchart in FIG. 11 starts with steps to calculate the
difference between node and master sensor responses in synthetic
parameter space (S.sub.n and S.sub.m in Eq. (1)) on the reduced-set
of reference fluids (steps 1102 to 1106) using Eq. (2) and
two-dimensional interpolation. Synthetic sensor responses obtained
with Eq. (2) are representatives of `ideal` data points with fluid
spectroscopy data averaged over a large number of measurements
around each specified temperature and pressure combination setting.
After the responses of each optical element at 12 combination
setting points (3 temperatures and 4 pressures) are calculated for
each reference fluid, the data relationship between the synthetic
optical responses and the ideal temperatures and pressures can be
organized in the format of 3 by 4 matrices as the given points for
two-dimensional interpolation in simulating the synthetic response
of the same optical element with other within-range temperatures
and pressures as inputs.
[0094] Step 1102 includes determining an estimated value for a node
sensor response on each fluid at ideal setting points in synthetic
parameter space from Eq. (2), and using two-dimensional
interpolation with actually measured temperature and pressure in
tool parameter space as inputs to correct signal variation. Step
1102 corrects the drift of synthetic signals of node sensor in
response to slight variations in temperature and pressure settings
from ideal setting values during measurement. Step 1104 includes
determining an estimated value for a master sensor response under
the same conditions of Step 1102 in synthetic parameter space from
Eq. (2), and using two-dimensional temperature and pressure
interpolation to correct signal drift due to variation in actual
temperature and pressure measurements which may endure slight
change around the ideal setting values. Step 1106 includes
determining a difference between the estimated values for a node
sensor response and the estimated value for a master sensor
response. Step 1108 includes determining a set of cross-sensor
linearized model coefficients (e.g., `C`, `k`, and `b` in Eq. (1)
and Eq. (3)) in an optimization loop. In some embodiments, step
1108 includes selecting the set of coefficients `C`, `k` and `b`
that result in the best linear fit (cf. Eq. (3)). Step 1108
includes determining a set of cross-sensor linearized model
coefficients in an exhaustive searching or optimization loop
through multi-iteration training.
[0095] When the master sensor and the node sensor have different
configuration or element design, a particular element response of
the node sensor may be simulated with a different nominal element
response of the master sensor (i.e., a Methane ICE response of the
node sensor may be better simulated with a GOR ICE response of the
master sensor). Step 1110 includes adjusting master sensor channel
selection to simulate a particular channel response of the node
sensor with different element design, and re-calculating the
compensated master sensor response and re-determining a set of
coefficients of `C`, `k`, and `b`. Accordingly, step 1110 may
include adjusting the master sensor response from a master sensor
channel to simulate a node sensor response from a node sensor
channel wherein the master sensor channel may be the same, or
different, than the node sensor channel. In general, the choice of
master-sensor channel and node sensor channel pairs to be
correlated (e.g. abscissae and ordinates in FIGS. 5B,C, FIGS. 6B,C,
FIGS. 7B,C, and FIGS. 8B,C) is based on which channel correlation
results in the best, or better, linear correlation between the data
from the two sensor channels (i.e., the master sensor channel and
the node sensor channel). Accordingly, method 1100 may further
include adjusting a master sensor channel selection from a master
sensor to simulate a node sensor channel response from a node
sensor wherein the master sensor channel has the same or different
nominal element as the node sensor channel.
[0096] Step 1112 includes storing at least one set of cross-sensor
linearized model coefficients for at least one node sensor channel.
Step 1112 saves the best set of cross-sensor linearized model
coefficients for each node sensor channel based on the reduced set
of reference fluids and applies them to simulate the node sensor
responses of additional reference fluids to be described in FIG.
12.
[0097] FIG. 12 illustrates a flowchart including steps in a method
1200 for cross-sensor linearization by estimating and including a
node sensor response for an additional set of reference fluids for
robust reverse transformation, according to some embodiments.
Method 1200 may be performed by a computer device having a memory
and a processor (e.g., computer 140, memory 142, and processor 144,
cf. FIG. 1). The memory may store commands that, when executed by
the processor, cause the computer to perform at least some of the
steps in method 1200. Methods consistent with method 1200 may
include at least one but not all of the steps in method 1200,
performed in any order. Furthermore, methods consistent with the
scope of method 1200 may include at least some of the steps in
method 1200 performed overlapping in time, or even
simultaneously.
[0098] Methods consistent with method 1200 may include measuring
reference fluids with a measurement system using an optical sensor
having a plurality of optical elements (e.g., measurement system
102 and optical elements 104a-n, cf. FIG. 1). In some embodiments,
the plurality of optical elements in method 1200 may correspond to
the same optical sensor, or may belong to different optical
sensors. Accordingly, the plurality of optical elements in method
1200 may be selected within the same fabrication batch, among
different fabrication batches, within the same optical element
design, among different optical element designs, within the same
sensor configuration or among different sensor configurations. In
some embodiments, the measurement system in method 1200 may include
a reverse transformation or a forward transformation between sensor
responses in a tool parameter space and sensor responses in a
synthetic parameter space (e.g., tool parameter space 201,
synthetic parameter space 202, reverse transformation 203, and
forward transformation 205).
[0099] Step 1202 includes determining an estimated node sensor
response in synthetic parameter space for an extended set of
reference fluids with use of Eq. (2) and two-dimensional
temperature and pressure interpolation. Step 1204 includes
determining a master sensor response in synthetic parameter space
for an extended set of reference fluids. Step 1206 includes
determining a compensated master sensor response in tool parameter
space according to a predetermined weighting factor. At step 1206,
the compensated master sensor response on the additional set of
reference fluids is calculated in Eq. (1) with use of each
pre-determined weighting factor value, C, channel by channel. Step
1208 includes determining an estimated node sensor response through
Eq. (3) with use of each set of pre-determined coefficients `k` and
`b` channel by channel. Step 1210 includes determining a reverse
transformation using the estimated node sensor response in tool
parameter space for a complete set of reference fluids as training
inputs. In some embodiments, step 1210 includes determining node
sensor responses in synthetic parameter for the same set of
reference fluids as training outputs. Step 1212 includes adjusting
a response from an optical sensor in a wellbore using the reverse
transformation to obtain a characteristic of a wellbore fluid from
the adjusted response. Step 1214 includes modifying operation
parameters of drilling or well testing and sampling according to
the characteristic of the wellbore fluid.
[0100] FIG. 13 is a drilling system 1300 configured to use a
calibrated optical sensor for modifying a drilling parameter or
configuration in a measurement-while-drilling (MWD) and a
logging-while-drilling (LWD) operation, according to some
embodiments. Boreholes may be created by drilling into the earth
1302 using the drilling system 1300. The drilling system 1300 may
be configured to drive a bottom hole assembly (BHA) 1304 positioned
or otherwise arranged at the bottom of a drill string 1306 extended
into the earth 1302 from a derrick 1308 arranged at the surface
1310. The derrick 1308 includes a Kelly 1312 and a traveling block
1313 used to lower and raise the Kelly 1312 and the drill string
1306.
[0101] The BHA 1304 may include a drill bit 1314 operatively
coupled to a tool string 1316 which may be moved axially within a
drilled wellbore 1318 as attached to the drill string 1306. During
operation, the drill bit 1414 penetrates the earth 1302 and thereby
creates the wellbore 1318. The BHA 1404 provides directional
control of the drill bit 1314 as it advances into the earth 1302.
The tool string 1316 can be semi-permanently mounted with various
measurement tools (not shown) such as, but not limited to,
measurement-while-drilling (MWD) and logging-while-drilling (LWD)
tools, that may be configured to take downhole measurements of
drilling conditions. In other embodiments, the measurement tools
may be self-contained within the tool string 1316, as shown in FIG.
13.
[0102] Fluid or "mud" from a mud tank 1320 may be pumped downhole
using a mud pump 1322 powered by an adjacent power source, such as
a prime mover or motor 1324. The mud may be pumped from the mud
tank 1320, through a stand pipe 1326, which feeds the mud into the
drill string 1306 and conveys the same to the drill bit 1314. The
mud exits one or more nozzles arranged in the drill bit 1314 and in
the process cools the drill bit 1314. After exiting the drill bit
1314, the mud circulates back to the surface 1310 via the annulus
defined between the wellbore 1318 and the drill string 1306, and in
the process returns drill cuttings and debris to the surface. The
cuttings and mud mixture are passed through a flow line 1328 and
are processed such that a cleaned mud is returned down hole through
the stand pipe 1326 once again.
[0103] The BHA 1304 may further include a downhole tool 1330 that
may be similar to the downhole tools described herein. More
particularly, the downhole tool 1330 may have a calibrated optical
sensor arranged therein, and the downhole tool 1330 may have been
calibrated prior to being introduced into the wellbore 1318 using
the tool validation testing generally described herein. Moreover,
prior to being introduced into the wellbore 1318, the downhole tool
1330 may have been optimized by generally following method 400 of
FIG. 4. In some embodiments, downhole tool 1330 is configured to
perform at least one of the steps described above in any one of
methods 900, 1000, 1100, and 1200.
[0104] FIG. 14, illustrates a wireline system 1400 that may employ
one or more principles of the present disclosure. In some
embodiments, wireline system 1400 may be configured to use a
calibrates optical sensor during formation testing and sampling.
After drilling of wellbore 1318 is complete, it may be desirable to
know more details of types of formation fluids and the associated
characteristics through sampling with use of wireline formation
tester. System 1400 may include a downhole tool 1402 that forms
part of a wireline logging operation that can include one or more
optical sensors 1404, as described herein, as part of a downhole
measurement tool. System 1400 may include the derrick 1308 that
supports the traveling block 1313. Wireline logging tool 1402, such
as a probe or sonde, may be lowered by wireline or logging cable
1406 into the borehole 1318. Tool 1402 may be lowered to the bottom
of the region of interest and subsequently pulled upward at a
substantially constant speed. Tool 1402 may be configured to
measure fluid properties of the wellbore fluids, and any
measurement data generated by downhole tool 1402 and its associated
optical sensors 1404 can be communicated to a surface logging
facility 1408 for storage, processing, and/or analysis. Logging
facility 1408 may be provided with electronic equipment 1410,
including processors for various types of signal processing. In
some embodiments, downhole tool 1402 and logging facility 1408 are
configured to perform at least one of the steps described above in
any one of methods 400, 900, 1000, 1100, and 1200.
[0105] Embodiments disclosed herein include:
[0106] A. A method that includes obtaining a plurality of master
sensor responses with a master sensor in a set of training fluids,
obtaining a plurality of node sensor responses with a plurality of
node sensors in the set of training fluids, finding a linear
correlation between a compensated master data set and a node data
set for the set of training fluids, generating a plurality of node
sensor responses in a tool parameter space from the compensated
master data set on a set of application fluids, obtaining a reverse
transformation based on the plurality of node sensor responses in a
complete set of calibration fluids, the reverse transformation
transforming each node sensor response from a tool parameter space
to a synthetic parameter space, obtaining fluid characteristics
with synthetic fluid predictive models using reverse-transformed
inputs from at least one of the node sensor responses to a fluid
measurement, and modifying operation parameters of a drilling or a
well testing and sampling system according to the fluid
characteristics, wherein the complete set of calibration fluids
comprises the set of training fluids and the set of application
fluids.
[0107] B. A method that includes determining an value for a node
sensor response in synthetic parameter space using a
two-dimensional temperature and pressure interpolation with given
node sensor responses at specified temperatures and pressures,
determining an value for a master sensor response in synthetic
parameter space using the two-dimensional temperature and pressure
interpolation with given master sensor response at specified
temperatures and pressures, determining a difference between the
value for a node sensor response and the value for a master sensor
response, determining a set of cross-sensor linearized model
coefficients in an optimization loop, adjusting a master sensor
channel selection to simulate a channel response of the node
sensor, obtaining a reverse transformation using the simulated
channel responses of the node sensor, and modifying operation
parameters of a drilling or a well testing and sampling system
according to a fluid characteristic obtained with a synthetic fluid
predictive model using node sensor responses as input to the
reverse transformation.
[0108] C. A method that includes introducing a tool into a wellbore
drilled into one or more subterranean formations, the tool having
been previously calibrated for operation by obtaining a plurality
of master sensor responses with a master sensor in a set of
training fluids, obtaining a plurality of node sensor responses
with a plurality of node sensors in the set of training fluids,
each of the plurality of node sensors and the master sensor
including an optical element, finding a linear correlation between
a compensated master data set and a node data set for the set of
training fluids, generating a plurality of node sensor responses in
a tool parameter space from the compensated master data set on a
set of application fluids, and obtaining a reverse transformation
based on the plurality of node sensor responses in a complete set
of calibration fluids, wherein the complete set of calibration
fluids comprises the set of training fluids and the set of
application fluids, determining a fluid characteristic from the
plurality of node sensor responses in the synthetic parameter space
using the reverse transformation and a synthetic fluid predictive
model, and modifying operation parameters of a drilling or a well
testing and sampling according to the fluid characteristic.
[0109] Each of embodiments A, B, and C may have one or more of the
following additional elements in any combination: Element 1:
further comprising selecting a weighting factor to compensate the
master sensor responses with the node sensor responses according to
the linear correlation between the compensated master data set and
the node data set. Element 2: wherein selecting the weighting
factor comprises selecting a weighting factor that results in a
high linear correlation between the compensated master data set and
the node data set, wherein the high linear correlation indicates a
low data transformation error. Element 3: wherein selecting the
weighting factor comprises determining a set of cross-sensor
linearized coefficients comprising the weighting factor for each
channel pair, using an exhaustive searching loop through
multi-iteration training Element 4: further comprising selecting
the master sensor and at least one of the plurality of node sensors
from two sensors having a same design, two sensors having a same
configuration, and two sensors from a fabrication batch. Element 5:
further comprising selecting the master sensor and at least one of
the plurality of node sensors from different fabrication batches,
and from two sensors having a same design and having a same
configuration. Element 6: further comprising selecting the master
sensor and at least one of the plurality of node sensors from
different fabrication batches having a different design, and with a
same number of elements having the same denomination. Element 7:
further comprising selecting the master sensor and at least one of
the plurality of node sensors from different designs, from
different configurations, and originating from different
fabrication batches. Element 8: further comprising truncating a
master data set to a same number of samples as a node data set,
wherein each sample in the master data set comprises a measurement
having a temperature setting and pressure setting similar to a
temperature setting and a pressure setting of at least one
measurement in the node data set. Element 9: wherein finding a
linear correlation between a compensated master data and the node
data set comprises applying a weight factor to a difference between
a synthetic node sensor response and a synthetic master sensor
response for a reference fluid selected from the set of training
fluids, and adding the weighted difference to a master sensor
response measured from the reference fluid. Element 10: further
comprising determining at least one of a node sensor response and a
master sensor response in the synthetic parameter space with a dot
product of a fluid spectral response vector and a convolved
spectral response vector for one of the at least one node sensor or
the master sensor. Element 11: further comprising obtaining fluid
characteristics with a synthetic fluid predictive model using the
reverse transformed inputs from at least one of the node sensor
responses to a fluid measurement. Element 12: wherein obtaining a
plurality of node sensor responses with a plurality of node sensors
on the set of training fluids comprises measured and simulated node
sensor responses from reference fluids at broad ranges of
temperature settings and pressure settings. Element 13: further
comprising collecting optical responses from a plurality of
petroleum fluids with known characteristics using the selected
master sensor, generating a plurality of synthetic node sensor
responses associated with the plurality of petroleum fluids using
the reverse transformation with cross-sensor linearized node sensor
inputs in tool parameter space, and calibrating a fluid
characterization model using the plurality of synthetic node sensor
responses.
[0110] Element 14: wherein determining the value for a node sensor
response and determining the value for a master sensor response
comprises varying temperature and pressure conditions for a
plurality of node sensor responses and master sensor responses.
Element 16: wherein adjusting the master sensor channel selection
to simulate a channel response of the node sensor comprises
identifying a set of cross-sensor linearized coefficients for a
node sensor channel based on a plurality of training fluids, and
obtaining the channel response of the node sensor on a plurality of
application fluids with the set of cross-sensor linearized model
coefficients. Element 17: wherein obtaining a reverse
transformation using the simulated channel responses of the node
sensor comprises combining the node sensor response on a plurality
of training fluids and a plurality of application fluids to develop
the reverse transformation model with a neural network. Element 18:
further comprising adjusting a master sensor channel selection from
a master sensor to simulate a node sensor channel response from a
node sensor. Element 19: wherein the master sensor channel has the
same or different nominal element as the node sensor channel.
[0111] Element 20: wherein obtaining a plurality of node sensor
responses with a plurality of node sensors in the set of training
fluids during the tool calibration comprises determining a
temperature setting and a pressure setting of the reduced set of
training fluids according to a temperature setting and a pressure
setting of the master set of training fluids. Element 21: further
comprising, during the tool calibration, selecting one of the
plurality of master sensors and one of the plurality of node
sensors from two sensors having at least one of different designs,
different configurations, or different fabrication batches.
[0112] By way of non-limiting example, exemplary combinations
applicable to A, B, and C include: Element 1 with Element 2;
Element 1 with Element 2; and Element 18 with Element 19.
[0113] Therefore, the disclosed systems and methods are well
adapted to attain the ends and advantages mentioned as well as
those that are inherent therein. The particular embodiments
disclosed above are illustrative only, as the teachings of the
present disclosure may be modified and practiced in different but
equivalent manners apparent to those skilled in the art having the
benefit of the teachings herein. Furthermore, no limitations are
intended to the details of construction or design herein shown,
other than as described in the claims below. It is therefore
evident that the particular illustrative embodiments disclosed
above may be altered, combined, or modified and all such variations
are considered within the scope of the present disclosure. The
systems and methods illustratively disclosed herein may suitably be
practiced in the absence of any element that is not specifically
disclosed herein and/or any optional element disclosed herein.
While compositions and methods are described in terms of
"comprising," "containing," or "including" various components or
steps, the compositions and methods can also "consist essentially
of" or "consist of" the various components and steps. All numbers
and ranges disclosed above may vary by some amount. Whenever a
numerical range with a lower limit and an upper limit is disclosed,
any number and any included range falling within the range is
specifically disclosed. In particular, every range of values (of
the form, "from about a to about b," or, equivalently, "from
approximately a to b," or, equivalently, "from approximately a-b")
disclosed herein is to be understood to set forth every number and
range encompassed within the broader range of values. Also, the
terms in the claims have their plain, ordinary meaning unless
otherwise explicitly and clearly defined by the patentee. Moreover,
the indefinite articles "a" or "an," as used in the claims, are
defined herein to mean one or more than one of the element that it
introduces. If there is any conflict in the usages of a word or
term in this specification and one or more patent or other
documents that may be incorporated herein by reference, the
definitions that are consistent with this specification should be
adopted.
[0114] As used herein, the phrase "at least one of" preceding a
series of items, with the terms "and" or "or" to separate any of
the items, modifies the list as a whole, rather than each member of
the list (i.e., each item). The phrase "at least one of" allows a
meaning that includes at least one of any one of the items, and/or
at least one of any combination of the items, and/or at least one
of each of the items. By way of example, the phrases "at least one
of A, B, and C" or "at least one of A, B, or C" each refer to only
A, only B, or only C; any combination of A, B, and C; and/or at
least one of each of A, B, and C.
* * * * *