U.S. patent application number 13/480098 was filed with the patent office on 2013-11-28 for spectral analysis techniques for fluid monitoring.
This patent application is currently assigned to Halliburton Energy Services, Inc.. The applicant listed for this patent is Robert P. Freese, Johanna Haggstrom, Kurt Hoeman, Melissa Weston. Invention is credited to Robert P. Freese, Johanna Haggstrom, Kurt Hoeman, Melissa Weston.
Application Number | 20130312956 13/480098 |
Document ID | / |
Family ID | 48577903 |
Filed Date | 2013-11-28 |
United States Patent
Application |
20130312956 |
Kind Code |
A1 |
Weston; Melissa ; et
al. |
November 28, 2013 |
Spectral Analysis Techniques for Fluid Monitoring
Abstract
Spectroscopic analyses of complex mixtures within a fluid phase
can oftentimes be complicated by spectral overlap, making it
difficult to analyze for each constituent therein. Methods for
analyzing a treatment fluid can comprise: providing a treatment
fluid comprising a fluid phase and one or more constituents
therein; exposing the treatment fluid to electromagnetic radiation
in a spectral region where the fluid phase optically interacts with
the electromagnetic radiation, so as to acquire a spectrum of the
fluid phase; analyzing the spectrum of the fluid phase to determine
at least one property of the treatment fluid, the at least one
property of the treatment fluid being selected from the group
consisting of a concentration of at least one constituent in the
treatment fluid, at least one characteristic of the treatment
fluid, and any combination thereof; and introducing the treatment
fluid into a subterranean formation.
Inventors: |
Weston; Melissa; (Duncan,
OK) ; Hoeman; Kurt; (Duncan, OK) ; Freese;
Robert P.; (Pittsboro, NC) ; Haggstrom; Johanna;
(Duncan, OK) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Weston; Melissa
Hoeman; Kurt
Freese; Robert P.
Haggstrom; Johanna |
Duncan
Duncan
Pittsboro
Duncan |
OK
OK
NC
OK |
US
US
US
US |
|
|
Assignee: |
Halliburton Energy Services,
Inc.
Houston
TX
|
Family ID: |
48577903 |
Appl. No.: |
13/480098 |
Filed: |
May 24, 2012 |
Current U.S.
Class: |
166/248 ;
166/250.01 |
Current CPC
Class: |
G01N 2201/129 20130101;
G01N 21/3577 20130101; G01N 21/359 20130101 |
Class at
Publication: |
166/248 ;
166/250.01 |
International
Class: |
E21B 43/00 20060101
E21B043/00; E21B 47/00 20120101 E21B047/00 |
Claims
1. A method comprising: providing a treatment fluid comprising a
fluid phase and one or more constituents therein; exposing the
treatment fluid to electromagnetic radiation in a spectral region
where the fluid phase optically interacts with the electromagnetic
radiation, so as to acquire a spectrum of the fluid phase;
analyzing the spectrum of the fluid phase to determine at least one
property of the treatment fluid, the at least one property of the
treatment fluid being selected from the group consisting of a
concentration of at least one constituent in the treatment fluid,
at least one characteristic of the treatment fluid, and any
combination thereof; and introducing the treatment fluid into a
subterranean formation.
2. The method of claim 1, wherein analyzing the spectrum of the
fluid phase takes place before introducing the treatment fluid into
the subterranean formation.
3. The method of claim 2, further comprising: determining if the
treatment fluid is suitable for being introduced into the
subterranean formation.
4. The method of claim 3, further comprising: altering a
concentration of at least one constituent of the treatment fluid,
altering at least one characteristic of the treatment fluid, or any
combination thereof.
5. The method of claim 4, wherein altering takes place before
introducing the treatment fluid into the subterranean
formation.
6. The method of claim 4, wherein altering takes place on-the-fly
while introducing the treatment fluid into the subterranean
formation.
7. The method of claim 1, wherein the fluid phase comprises water,
an aqueous fluid, an oleaginous fluid, or any combination
thereof.
8. The method of claim 7, wherein the fluid phase comprises a
produced aqueous fluid.
9. The method of claim 1, wherein the one or more constituents
comprise at least one ionic material.
10. The method of claim 9, wherein the at least one ionic material
comprises an ion selected from the group consisting of
sodium-containing ions, potassium-containing ions,
strontium-containing ions, magnesium-containing ions,
calcium-containing ions, barium-containing ions,
aluminum-containing ions, carbon-containing ions, sulfur-containing
ions, halogen-containing ions, boron-containing ions,
manganese-containing ions, lithium-containing ions,
cesium-containing ions, chromium-containing ions,
arsenic-containing ions, lead-containing ions, mercury-containing
ions, nickel-containing ions, copper-containing ions,
zinc-containing ions, titanium-containing ions, and any combination
thereof.
11. The method of claim 1, wherein the at least one characteristic
of the treatment fluid comprises a physical property selected from
the group consisting of pH, ionic strength, specific gravity, total
dissolved solids, total suspended solids, viscosity, opacity, yield
point, and any combination thereof.
12. The method of claim 1, wherein the treatment fluid comprises a
fracturing fluid, an acidizing fluid, a drilling fluid, a scale
inhibitor fluid, a biocidal fluid, or any combination thereof.
13. The method of claim 1, wherein analyzing the spectrum of the
fluid phase takes place within a wavelength range where the fluid
phase has a molar extinction coefficient of at least about 0.005
M.sup.-1mm.sup.-1.
14. A method comprising: providing a fluid phase containing one or
more constituents therein; exposing the fluid phase to
electromagnetic radiation in a spectral region where the fluid
phase optically interacts with the electromagnetic radiation, so as
to acquire a spectrum of the fluid phase; analyzing the spectrum of
the fluid phase to determine at least one property thereof, the at
least one property of the fluid phase being selected from the group
consisting of a concentration of at least one constituent in the
fluid phase, at least one characteristic of the fluid phase, and
any combination thereof; determining if the at least one property
is within a desired range; and introducing the fluid phase into a
vessel.
15. The method of claim 14, wherein the vessel comprises a
pipeline.
16. The method of claim 14, wherein analyzing the spectrum of the
fluid phase and determining if the at least one property is within
a desired range take place before introducing the fluid phase into
the vessel.
17. The method of claim 14, further comprising: altering a
concentration of at least one constituent in the fluid phase,
altering at least one characteristic of the fluid phase, or any
combination thereof.
18. The method of claim 17, wherein altering takes place before
introducing the fluid phase into the vessel.
19. The method of claim 14, wherein the fluid phase is exposed to
the electromagnetic radiation after being introduced to the
vessel.
20. The method of claim 14, wherein analyzing the spectrum of the
fluid phase takes place within a wavelength range where the fluid
phase has a molar extinction coefficient of at least about 0.005
M.sup.-1mm.sup.-1.
21. A method comprising: providing a produced aqueous fluid from a
subterranean formation; exposing the produced aqueous fluid to
electromagnetic radiation in a spectral region where water
comprising the produced aqueous fluid optically interacts with the
electromagnetic radiation, so as to acquire a spectrum of the
water; analyzing the spectrum of the water to determine at least
one property of the produced aqueous fluid, the at least one
property of the produced aqueous fluid being selected from the
group consisting of a concentration of at least one constituent in
the produced aqueous fluid, at least one characteristic of the
produced aqueous fluid, and any combination thereof; and
optionally, altering a concentration of at least one constituent in
the produced aqueous fluid, altering at least one characteristic of
the produced aqueous fluid, or any combination thereof.
22. The method of claim 21, further comprising: re-introducing the
produced aqueous fluid into a subterranean formation, the
subterranean formation comprising the same subterranean formation
that produced the aqueous fluid or a different subterranean
formation.
23. The method of claim 22, wherein the produced aqueous fluid is
re-introduced to the subterranean formation as a treatment fluid
comprising the produced aqueous fluid, the treatment fluid being
selected from the group consisting of a fracturing fluid, an
acidizing fluid, a drilling fluid, a scale inhibitor fluid, a
biocidal fluid, or any combination thereof.
24. The method of claim 21, wherein analyzing the spectrum of the
water takes place within a wavelength range where the water has a
molar extinction coefficient of at least about 0.005
M.sup.-1mm.sup.-1.
Description
BACKGROUND
[0001] The present disclosure relates to spectroscopic
measurements, and, more specifically, to spectral monitoring of
samples having complex mixtures of constituents in a matrix
therein.
[0002] Spectroscopic techniques can be an extremely powerful tool
for conducting sample analyses, since they usually can provide
chemical information more rapidly than is possible with standard
laboratory analyses. Through judicious choice of a spectroscopic
technique, one of ordinary skill in the art may determine multiple
pieces of chemical information about a sample, such as its
qualitative and quantitative chemical composition. By knowing the
chemical composition of a sample, one can determine if the bulk
substance from which the sample is obtained is suitable for its
intended use. However, spectroscopic analyses of samples containing
complex mixtures of constituents can sometimes be extremely
complicated due to overlapping spectral signatures of the
constituents and/or the sample matrix.
[0003] Subterranean operations are one area in which it can be
desirable to analyze complex samples, such as the compositions
and/or characteristics of substances that are introduced to and/or
produced from a subterranean formation. Fluids, which may be
introduced to or produced from a subterranean formation, are
commonly encountered in subterranean operations and can oftentimes
comprise complex mixtures of constituents.
[0004] Fluids can be used in a variety of subterranean operations
to treat a subterranean formation. Fluids can also be used in a
variety of operations to treat the interior of a vessel
transporting or housing the fluid, such as a pipeline, for example.
Accordingly, both such fluids will be referred to herein as
"treatment fluids." As used herein, the term "treatment fluid"
refers to a fluid that is placed in a location in order to perform
a desired function or to achieve a desired purpose. Treatment
fluids can be used in a variety of subterranean operations
including, but not limited to, drilling operations, production
operations, stimulation operations, remediation operations, fluid
diversion operations, secondary or tertiary enhanced oil recovery
(EOR) operations, and the like. As used herein, the terms "treat,"
"treatment," "treating," and other grammatical equivalents thereof
refer to any operation that uses a fluid in conjunction with
performing a desired function and/or achieving a desired purpose.
The terms "treat," "treatment," and "treating," as used herein, do
not imply any particular action by the fluid or any particular
constituent thereof unless otherwise specified. Treatment fluids
can include, for example, drilling fluids, fracturing fluids,
acidizing fluids, conformance treatment fluids, damage control
fluids, remediation fluids, scale removal and inhibition fluids,
biocidal fluids, chemical floods, and the like.
[0005] When conducting treatment operations within a subterranean
formation, it can be beneficial to know the chemical and/or
physical properties of a fluid being introduced into or produced
from the formation. Similarly, it can also sometimes be desirable
to analyze a surface within a subterranean formation to gather
information relating to the formation itself. Because of their
complex nature, analysis of samples encountered in subterranean
operations can be technically challenging, as multiple analytical
techniques may be needed to fully analyze for the constituents and
sample characteristics of interest. Further complicating this
issue, some of these analyses are not particularly well suited for
being conducted in the field and/or require specialized equipment
and operator training. In many cases, analyses are conducted in
off-site laboratories and can take a period of hours to weeks to
complete.
[0006] Fluids, in particular, may present a special set of
challenges with regard to subterranean operations. While analysis
of a fluid takes place, the fluid either has to be stored for
subsequent introduction to the subterranean formation, or it has to
be used blindly in a subterranean operation based on the
presumption that it has acceptable properties. Neither case is
ideal. Waiting on lengthy analyses may result in costly production
delays. Furthermore, the properties and composition of the fluid
may change over time (e.g., due to scaling, precipitation, chemical
reaction of constituents with one another, chemical degradation,
bacterial growth, environmental factors, and the like). On the
other hand, introducing a fluid having unsuitable properties to a
subterranean formation may result in an ineffective treatment
operation and/or formation damage, both of which may result in
delays and additional production cost. Factors that may make a
fluid unsuitable for introduction to a given subterranean formation
may include, for example, an incorrect concentration of a desired
constituent, an incorrect constituent, an incorrect viscosity, an
incorrect pH, an interfering impurity, an incorrect sag potential,
an incorrect kind or concentration of proppant particulates,
bacterial contamination, and/or the like. Similar issues may be
encountered with fluids that are produced from a subterranean
formation, where delayed analyses of the produced fluid are not
representative of the fluid's true nature following production.
[0007] Although off-site analyses of a fluid can be satisfactory in
certain instances, such analyses do not allow real-time or near
real-time monitoring of the fluid to take place during a treatment
operation, as noted above. Thus, off-site analyses do not offer the
possibility for proactive control of a treatment operation to take
place by modifying the properties of a treatment fluid with minimal
production delays. Modifying the properties of a treatment fluid
may make the fluid suitable for introduction to the subterranean
formation. Alternately, by monitoring a fluid being produced from
the subterranean formation, one can determine if a treatment fluid
needs to be used in conjunction with production or if a treatment
fluid is having a desired effect. In addition, produced fluids can
provide valuable insight into the formation chemistry and contents
if properly analyzed.
[0008] In spite of the wealth of chemical information that can be
present in produced fluids, it has sometimes been conventional in
the art to simply dispose of unwanted produced fluids, such as
produced formation water and produced aqueous fluids (e.g., spent
or partially spent treatment fluids). With increasingly stringent
environmental regulations, it has become increasingly more
difficult to dispose of water and other produced aqueous fluids. As
a result, water treatment, conservation, and management are
becoming ever more important in the oil and gas industry. Moreover,
many treatment operations utilize considerable water volumes (e.g.,
millions of gallons to treat a single wellbore), and obtaining
sufficient water of suitable quality to conduct a treatment
operation may be problematic in certain instances and
locations.
[0009] Despite the usual ready availability of produced aqueous
fluids, it has not been conventional in the art to reuse these
fluids for conducting subterranean treatment operations. Chemical
incompatibilities in treatment fluids are commonly observed. As a
result, some produced aqueous fluids may not be suitable for
forming certain types of treatment fluids. This difficulty has
often been exacerbated by the inability to readily analyze produced
aqueous fluids rapidly and accurately in the field (e.g., in
real-time or near real-time). Despite these issues, there remains
considerable interest in the reintroduction of produced aqueous
fluids to a subterranean formation, either for waste disposal
purposes or for carrying out a subsequent treatment operation.
SUMMARY
[0010] The present disclosure relates to spectroscopic
measurements, and, more specifically, to spectral monitoring of
samples having complex mixtures of constituents in a matrix
therein.
[0011] In some embodiments, the present disclosure provides a
method comprising: providing a treatment fluid comprising a fluid
phase and one or more constituents therein; exposing the treatment
fluid to electromagnetic radiation in a spectral region where the
fluid phase optically interacts with the electromagnetic radiation,
so as to acquire a spectrum of the fluid phase; analyzing the
spectrum of the fluid phase to determine at least one property of
the treatment fluid, the at least one property of the treatment
fluid being selected from the group consisting of a concentration
of at least one constituent in the treatment fluid, at least one
characteristic of the treatment fluid, and any combination thereof;
and introducing the treatment fluid into a subterranean
formation.
[0012] In some embodiments, the present disclosure provides a
method comprising: providing a fluid phase containing one or more
constituents therein; exposing the fluid phase to electromagnetic
radiation in a spectral region where the fluid phase optically
interacts with the electromagnetic radiation, so as to acquire a
spectrum of the fluid phase; analyzing the spectrum of the fluid
phase to determine at least one property thereof, the at least one
property of the fluid phase being selected from the group
consisting of a concentration of at least one constituent in the
fluid phase, at least one characteristic of the fluid phase, and
any combination thereof; determining if the at least one property
is within a desired range; and introducing the fluid phase into a
vessel.
[0013] In some embodiments, the present disclosure provides a
method comprising: providing a produced aqueous fluid from a
subterranean formation; exposing the produced aqueous fluid to
electromagnetic radiation in a spectral region where water
comprising the produced aqueous fluid optically interacts with the
electromagnetic radiation, so as to acquire a spectrum of the
water; analyzing the spectrum of the water to determine at least
one property of the produced aqueous fluid, the at least one
property of the produced aqueous fluid being selected from the
group consisting of a concentration of at least one constituent in
the produced aqueous fluid, at least one characteristic of the
produced aqueous fluid, and any combination thereof; and
optionally, altering a concentration of at least one constituent in
the produced aqueous fluid, altering at least one characteristic of
the produced aqueous fluid, or any combination thereof.
[0014] The features and advantages of the present invention will be
readily apparent to one having ordinary skill in the art upon a
reading of the description of the preferred embodiments that
follows.
BRIEF DESCRIPTION OF THE DRAWINGS
[0015] The following figures are included to illustrate certain
aspects of the present invention, 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, as will occur to those skilled in
the art and having the benefit of this disclosure.
[0016] FIG. 1 shows near-infrared absorption spectra for water at
various cell path lengths.
[0017] FIGS. 2A and 2B show near-infrared absorption spectra for
various ionic constituents in water.
[0018] FIGS. 3A-3D show regression vectors determined for chloride,
sulfate, total boron, and total iron, respectively, over the
wavelength range of 2000 nm to 2350 nm.
[0019] FIG. 4 shows a regression vector determined for specific
gravity over the wavelength range of 1375 nm to 1900 nm.
[0020] FIG. 5 shows an aggregate near-infrared absorption spectrum
of 27 field-produced water samples at a path length of 2 mm against
a water reference.
[0021] FIG. 6 shows an aggregate near-infrared spectra absorption
spectrum of 27 field-produced water samples at a path length of 2
mm against a water reference following normalization.
[0022] FIGS. 7A and 7B show expansions of the data of FIG. 6
following conversion into transmission mode.
[0023] FIGS. 8A-8D show illustrative calibration curves for
chloride, sulfate, total boron, and total iron, respectively.
[0024] FIGS. 9A-9I show illustrative plots of predicted
concentration, as determined by dot product analysis, compared to
experimentally determined concentrations.
DETAILED DESCRIPTION
[0025] The present disclosure relates to spectroscopic
measurements, and, more specifically, to spectral monitoring of
samples having complex mixtures of constituents in a matrix
therein.
[0026] As discussed above, analyses of samples having mixtures of
constituents therein may present difficulties that often
necessitate the use of multiple analytical techniques. As used
herein, the term "constituent" refers to a substance that is
disposed within a matrix. As used herein, the term "matrix" refers
to a continuous phase in which a constituent is disposed. Complex
samples may be commonly encountered in the field of subterranean
treatment operations or other types of treatment operations. For
example, it can sometimes be desirable to know the composition and
properties of a fluid being introduced into or produced from a
subterranean formation. Such fluids can often comprise a complex
mixture of constituents within a continuous fluid phase and
heretofore have not been amenable to rapid analyses in or near the
field, certainly not in real-time or near real-time. As used
herein, the terms "real-time" and "near real-time" refer to a
determination of a sample concentration or sample characteristic
that takes place in the same time frame as the interrogation of the
sample with electromagnetic radiation. That is, "real-time" or
"near real-time" determinations do not take place offline after
data sampling using post-acquisition processing techniques.
[0027] A further complication with fluids commonly encountered in
subterranean treatment operations or other types of treatment
operations is that a number of the constituents of interest are not
considered to be spectroscopically active by routine spectroscopic
techniques, such as infrared, visible, and ultraviolet
spectroscopic methodologies. As used herein, the term
"spectroscopically active" refers to a substance that optically
interacts with electromagnetic radiation of a given wavelength or
wavelength range. That is, a substance that is "spectroscopically
active" results in a measurable change in a quantity of
electromagnetic radiation optically interacting therewith. As used
herein, the term "optically interact" and variants thereof refer to
the reflection, transmission, scattering, diffraction, or
absorption of electromagnetic radiation by a sample. In contrast, a
substance that is "spectroscopically inactive" refers to a
substance that does not substantially optically interact with
electromagnetic radiation of a given wavelength or wavelength
range. That is, a substance that is "spectroscopically inactive"
does not measurably change or only negligibly changes
electromagnetic radiation optically interacting therewith.
Accordingly, a substance that is spectroscopically inactive cannot
be classicly interrogated with electromagnetic radiation to produce
a spectrum thereof. Common constituents that are spectroscopically
inactive in the visible and infrared spectral regions of the
electromagnetic spectrum include, for example, alkali metal ions
such as sodium and potassium, as well as other metal ions. When
spectroscopically inactive constituents are present, alternative
chemical analyses (e.g., ion chromatography, volumetric analyses,
or gravimetric analyses) can be employed, or dyes can sometimes be
added to produce a spectroscopically active species. Metal ions can
also be detected by techniques such as atomic absorption
spectroscopy (AAS) or atomic emission spectroscopy (AES), which can
include inductively coupled plasma (ICP) spectroscopy. In most
cases, involved sample preparation techniques can sometimes be
needed to achieve a satisfactory analytical result. In any event,
the analyses are generally not able to be conducted rapidly, or in
the field, certainly not in real-time or near real-time.
[0028] In most conventional spectroscopic analyses, it is typical
to analyze for constituents of a matrix in a spectral region where
the matrix itself is not spectroscopically active. For example,
conventional spectroscopic analyses of a fluid phase are usually
performed by analyzing a spectral region where a fluid phase
constituent of interest is spectroscopically active and the fluid
phase is spectroscopically inactive or substantially
spectroscopically inactive, so as not to obscure spectral features
associated with the constituent. That is, conventional
spectroscopic analyses usually rely upon the constituent of
interest optically interacting with electromagnetic radiation more
strongly than the fluid phase in which it is disposed. In the
alternative, sample preparation techniques may be used to at least
partially separate a constituent from its matrix so as to be able
to separately analyze each.
[0029] In contrast to most conventional spectroscopic analyses, we
have surprisingly discovered that various constituents of a matrix
may be determined spectroscopically, not by direct spectroscopic
determination of the constituent itself (i.e., through analyzing a
spectral feature associated with the constituent), but by analyzing
the spectral features of the matrix. More specifically, we have
discovered that a plurality of constituents within a fluid phase
can influence its spectrum to differing degrees, even at very low
concentration levels. These slight perturbations carry a wealth of
chemical and physical property information, which may be extracted
from the spectrum using regression vectors developed from a
training set of data for standards having known compositions and
properties. The regression vectors may be predictive for
determining the composition and properties of a sample. Further
discussion regarding the creation of regression vectors and using
the regression vectors in spectral analyses is described in greater
detail hereinafter.
[0030] Moreover, we have discovered that concentrations of two or
more constituents within a matrix can be determined from a single
spectrum of the matrix. That is, two or more constituents may be
analyzed simultaneously in the presence of one another using the
regression vector for each. Furthermore, different regions of the
matrix spectrum need not necessarily be analyzed for each
constituent. That is, the wavelength range analyzed for one
constituent may overlap the wavelength range analyzed for another
constituent due to the unique way in which the constituents each
perturb the matrix spectrum in linear or non-linear combinations
with one another. Conventional spectroscopic analyses, in contrast,
ideally seek to utilize well separated spectral features when
analyzing two or more constituents so that the constituents can be
analyzed essentially independently of one another (i.e., so that
their absorption peaks do not overlap). This can present
considerable analytical difficulties when broad spectral features
or a large number of constituents are present.
[0031] Due to the difficulties associated with interfering
constituents, an even more preferred technique in conventional
spectroscopy is to separate and analyze the constituents within a
matrix individually, such that there is a reduced likelihood of
unwanted interference taking place. Although this approach can be
successfully used when analyzing mixtures, it can considerably add
to the time, expense, and complexity of an analysis. Furthermore,
some constituents may not be readily separable from one another or
from the matrix. Although separation methodologies may be used in
conjunction with the techniques described herein, there is no
general necessity to do so. In this regard, the techniques
described herein are especially advantageous in their simplicity,
particularly in their ability to readily analyze mixtures of
constituents within matrix, particularly a fluid phase.
[0032] Even more surprisingly, we have also discovered that at
least some physical and chemical properties of a fluid phase may
have a regression vector associated therewith and may be determined
spectroscopically, even when the physical or chemical property
itself is not conventionally thought to be spectroscopic in nature.
As used herein, the term "characteristic" will be used to refer to
the value of a physical or chemical property. Characteristics such
as, for example, pH, total dissolved solids, ionic strength, and
specific gravity may be determined spectroscopically according to
the techniques described herein. It is anticipated that
characteristics such as, for example, total suspended solids,
viscosity, opacity, and yield point may be determined in a like
manner. These results are completely surprising and unexpected,
since such quantities are non-spectroscopic in nature. Accordingly,
the presently described techniques are further advantageous from
the standpoint of presenting a spectroscopic alternative to
conventional non-spectroscopic analytical techniques.
[0033] Thus, the techniques described herein may be advantageous in
terms of their simplicity and their ability to simultaneously
analyze multiple constituents within a matrix, particularly a fluid
phase. Moreover, we believe that the techniques are extendable to
many different types of fluid phases, as well as non-fluid
matrices. In regard to subterranean treatment operations or other
types of treatment operations, the techniques described herein may
allow the treatment operations to be conducted more rapidly, at
lower cost, and with greater confidence of a treatment fluid's
suitability for a given application than would otherwise be
possible. The techniques may also allow a treatment fluid to be
modified prior to or during its introduction to a subterranean
formation in order to make the treatment fluid more suitable for
use therein. In some cases, a treatment fluid may be monitored and
modified after being introduced to a location. For example, a
treatment fluid being stored in a vessel or introduced to a
pipeline may be modified in some manner after introduction thereto,
if desired.
[0034] In further regard to the analysis of treatment fluids, in
some embodiments, the techniques described herein may allow
produced aqueous fluids (e.g., produced water, spent or partially
spent aqueous treatment fluids, or any combination thereof) to be
analyzed for determining their suitability for use in subsequent
treatment operations. For example, a produced aqueous fluid having
suitable properties may be used as the carrier fluid of a treatment
fluid to be introduced into the subterranean formation that
produced the aqueous fluid or a different subterranean formation.
Although the constituents of any type of treatment fluid may be
analyzed by the techniques described herein, it may be particularly
advantageous to analyze the constituents and properties of
fracturing fluids and acidizing fluids, since these types of
treatment fluids are particularly susceptible to incompatibilities.
Given the analysis of a produced aqueous fluid, one of ordinary
skill in the art will be able to determine the suitability of the
fluid for reuse as the carrier fluid in a particular treatment
fluid. Further, one of ordinary skill in the art will be able to
recognize ways to modify a produced aqueous fluid in order to
improve its suitability for use as the carrier fluid in a
particular type of treatment fluid. Thus, by modifying a produced
aqueous fluid, treatment fluids having custom formulations designed
to meet the particular features of the produced aqueous fluid may
be developed. Accordingly, the techniques described herein may
advantageously allow water management issues that are commonly
encountered in subterranean treatment operations to be better
addressed.
[0035] In regard to fracturing fluids, and without being bound by
any theory or mechanism, it is believed that certain ionic
materials may be detrimental for a number of different reasons. For
example, sodium and potassium ions may affect the hydration state
of polymers. Other ions such as, for example, borate, iron, sodium,
and aluminum may compete for crosslinking sites. In certain cases,
pH control of some aqueous fluids may be problematic. All of these
factors may influence the overall rheological properties and
ultimate performance of a fracturing fluid. Likewise, in an
acidizing fluid, the presence of certain ions may lead to a less
effective acidizing treatment or unwanted precipitation damage.
[0036] Although the foregoing has described the particular
advantages associated with the presently described techniques in
regard to treatment fluids, it is to be recognized that the
techniques may be applicable to other industries, particularly
those in which it is desirable to analyze a substance in real-time
or near real-time. Illustrative but non-limiting industries may
include the food and drug industry, the petrochemical industry, the
water treatment industry, the waste recycling industry, the
cosmetic industry, and the like. Moreover, although the description
herein is primarily directed to analyses of fluid phases having
various constituents therein, it is to be understood that it is
believed that the described techniques may be extended to analyses
of constituents within solid samples as well.
[0037] In some embodiments, methods described herein can comprise:
providing a sample comprising a matrix and one or more constituents
therein; exposing the sample to electromagnetic radiation in a
spectral region where the matrix optically interacts with the
electromagnetic radiation, so as to acquire a spectrum of the
matrix; and analyzing the spectrum of the matrix within a
wavelength range where the matrix has a molar extinction
coefficient of at least about 0.005 M.sup.-1mm.sup.-1 to determine
at least one property of the sample, the at least one property of
the sample being selected from the group consisting of a
concentration of at least one constituent in the sample, at least
one characteristic of the sample, and any combination thereof.
[0038] In some embodiments, methods described herein can comprise:
providing a sample comprising a matrix and one or more constituents
therein; exposing the sample to electromagnetic radiation in a
spectral region where the matrix optically interacts with the
electromagnetic radiation, so as to acquire a spectrum of the
matrix; wherein the constituents are substantially optically
inactive in the spectral region; and analyzing the spectrum of the
matrix to determine at least one property of the sample, the at
least one property of the sample being selected from the group
consisting of a concentration of at least one constituent in the
sample, at least one characteristic of the sample, and any
combination thereof.
[0039] In general, but without being bound by theory or mechanism,
it is believed that the one or more constituents perturb the
spectrum of the matrix in the sample relative to a spectrum of the
matrix alone. Specifically, it is believed that the spectrum of the
matrix is perturbed in a linear or non-linear combinatorial manner
based on a contribution from each constituent. In some embodiments,
the matrix may comprise a fluid phase. As used herein, the term
"fluid phase" refers to any substance that is capable of flowing,
including particulate solids, liquids, gases, slurries, emulsions,
powders, muds, glasses, any combination thereof, and the like. In
some embodiments, the matrix may comprise a non-fluid phase, such
as a solid.
[0040] In some embodiments, methods described herein can comprise:
providing a treatment fluid comprising a fluid phase and one or
more constituents therein; exposing the treatment fluid to
electromagnetic radiation in a spectral region where the fluid
phase optically interacts with the electromagnetic radiation, so as
to acquire a spectrum of the fluid phase; analyzing the spectrum of
the fluid phase to determine at least one property of the treatment
fluid, the at least one property of the treatment fluid being
selected from the group consisting of a concentration of at least
one constituent in the treatment fluid, at least one characteristic
of the treatment fluid, and any combination thereof; and
introducing the treatment fluid into a subterranean formation.
[0041] In some embodiments, methods described herein can comprise:
providing a fluid phase containing one or more constituents
therein; exposing the fluid phase to electromagnetic radiation in a
spectral region where the fluid phase optically interacts with the
electromagnetic radiation, so as to acquire a spectrum of the fluid
phase; analyzing the spectrum of the fluid phase to determine at
least one property thereof, the at least one property of the fluid
phase being selected from the group consisting of a concentration
of at least one constituent in the fluid phase, at least one
characteristic of the fluid phase, and any combination thereof;
determining if the at least one property is in a desired range; and
introducing the fluid phase into a vessel. In some embodiments, the
vessel may comprise a storage tank or a pipeline, for example.
[0042] In some embodiments, the fluid phase may comprise an aqueous
fluid. In some embodiments, the fluid phase may comprise water.
Water sources may include, for example, fresh water, acidified
water, salt water, seawater, brine, aqueous salt solutions, surface
water (i.e., streams, rivers, ponds and lakes), underground water
from an aquifer, municipal water, municipal waste water, or
produced water. In some embodiments, the fluid phase may comprise a
produced aqueous fluid. Produced aqueous fluids may comprise
produced water, formation water, spent aqueous treatment fluids,
partially spent aqueous treatment fluids, and any combination
thereof. As used herein, the term "formation water" refers to water
that is natively present in a subterranean formation and is
expelled from the formation in the course of production. As used
herein, the term "produced water" refers to water that is present
in a subterranean formation, regardless of its source, and is
expelled from the formation in the course of production. As used
herein, the terms "spent aqueous treatment fluid" and "partially
spent aqueous treatment fluid" refer to treatment fluids comprising
an aqueous carrier fluid that are wholly or partially depleted of
their active constituent and are expelled from the formation in the
course of production. In addition, spent or partially spent aqueous
treatment fluids may comprise produced aqueous fluids that are
measurably changed in a characteristic, even though their bulk
composition may be substantially the same. For example, a produced
aqueous fluid may comprise a broken fracturing fluid that is no
longer in a viscosified state, although the viscosified state and
the broken state may not differ significantly in composition from
one another.
[0043] In some or other embodiments, the fluid phase may comprise
an oleaginous fluid such as oil or a like produced hydrocarbon, for
example. In some embodiments, the oleaginous fluid being analyzed
may comprise a drilling mud. In some embodiments, the fluid phase
may comprise a mixture of an aqueous fluid and an oleaginous
fluid.
[0044] In some embodiments, the methods may further comprise
determining a regression vector for each constituent in the sample
or each characteristic of the sample being analyzed. Determination
of the regression vector may allow a concentration of the
constituent in the sample or the value of a sample characteristic
to be spectroscopically calculated. The regression vector for each
constituent or characteristic may be determined using standard
procedures that will be familiar to one having ordinary skill in
the art. A brief summary of these procedures is provided below. In
various embodiments, analyzing the spectrum of the matrix may
comprise determining a dot product of the regression vector for
each constituent in the sample or characteristic of the sample
being analyzed. As one of ordinary skill in the art will recognize,
a dot product of a vector is a scalar quantity (i.e., a real
number). While the dot product value is believed to have no
physical meaning by itself (it may be positive or negative and of
any magnitude), comparison of the dot product value of a sample
with dot product values obtained for known reference standards and
plotted in a calibration curve may allow the sample dot product
value to be correlated with a concentration or value of a
characteristic, thereby allowing unknown samples to be analyzed. To
determine the dot product, one simply multiplies the regression
coefficient of the regression vector at a given wavelength times
the spectral intensity at the same wavelength. This process is
repeated for all wavelengths analyzed, and the products are summed
over the entire wavelength range to yield the dot product. More
importantly, the techniques described herein may allow two or more
properties of a sample to be determined from a single spectrum of
the matrix by applying a regression vector for each constituent or
characteristic.
[0045] Further details regarding the determination of a regression
vector and its use in determining a dot product are now provided.
It is possible to derive information from electromagnetic radiation
interacting with a sample by, for example, separating the
electromagnetic radiation from several samples into wavelength
bands and performing a multiple linear regression of the band
intensity against a constituent concentration or characteristic
determined by another measurement technique for each sample. The
measured concentration or characteristic may be expressed and
modeled by multiple linear regression techniques that will be
familiar to one having ordinary skill in the art. Specifically, if
y is the measured value of the concentration or characteristic, y
may be expressed as in Formula 1,
y=a.sub.0+a.sub.1w.sub.1+a.sub.2w.sub.2+a.sub.3w.sub.3+a.sub.4w.sub.4+
. . . (Formula 1)
where each a is a constant determined by the regression analysis
and each w is the light intensity for each wavelength band.
Depending on the circumstances, the estimate obtained from Formula
1 may be inaccurate, for example, due to the presence of other
constituents within the sample that may affect the intensity of the
wavelength bands.
[0046] A more accurate estimate may be obtained by expressing the
electromagnetic radiation in terms of its principal components. To
obtain the principal components, spectroscopic data is collected
for a variety of similar samples using the same type of
electromagnetic radiation. For example, following exposure to each
sample, the electromagnetic radiation may be collected and the
spectral intensity at each wavelength may be measured for each
sample. This data may then be pooled and subjected to a
linear-algebraic process known as singular value decomposition
(SVD) in order to determine the principal components. Use of SVD in
principal component analysis will be well understood by one having
ordinary skill in the art. Briefly, principal component analysis is
a dimension reduction technique, which takes m spectra with n
independent variables and constructs a new set of eigenvectors that
are linear combinations of the original variables. The eigenvectors
may be considered a new set of plotting axes. The primary axis,
termed the first principal component, is the vector that describes
most of the data variability. Subsequent principal components
describe successively less sample variability, until the higher
order principal components essentially describe only spectral
noise. Use of too few principal components may provide an
inaccurate estimate, whereas use of too many principal components
may unduly model spectral noise. In various embodiments described
herein, we have found that use of about 4 to about 6 principal
components provides sufficient accuracy without unduly modeling
spectral noise.
[0047] As used herein, the term "accuracy" refers to the extent to
which a determined value of a concentration or characteristic
represents the true value. As used herein, the term "precision"
refers to the reproducibility of a result.
[0048] Typically, the principal components are determined as
normalized vectors. Thus, each component of an electromagnetic
radiation sample may be expressed as x.sub.nz.sub.n, where x.sub.n
is a scalar multiplier and z.sub.n is the normalized component
vector for the n.sub.th component. That is, z.sub.n is a vector in
a multi-dimensional space where each wavelength is a dimension. As
will be understood by one having ordinary skill in the art,
normalization determines values for a component at each wavelength
so that the component maintains its shape and the length of the
principal component vector is equal to one. Thus, each normalized
component vector has a shape and a magnitude so that the components
may be used as the basic building blocks of any electromagnetic
radiation sample having those principal components. Accordingly,
each electromagnetic radiation sample may be described by a
combination of the normalized principal components multiplied by
the appropriate scalar multipliers, as set forth in Formula 2.
x.sub.1z.sub.1+x.sub.2z.sub.2+ . . . +x.sub.nz.sub.n (Formula
2)
The scalar multipliers x.sub.n may be considered the "magnitudes"
of the principal components in a given electromagnetic radiation
sample when the principal components are understood to have a
standardized magnitude as provided by normalization.
[0049] Because the principal components are orthogonal, they may be
used in a relatively straightforward mathematical procedure to
decompose an electromagnetic radiation sample into the component
magnitudes, which may accurately describe the data in the original
electromagnetic radiation sample. Since the original
electromagnetic radiation sample may also be considered a vector in
the multi-dimensional wavelength space, the dot product of the
original signal vector with a principal component vector is the
magnitude of the original signal in the direction of the normalized
component vector. That is, it is the magnitude of the normalized
principal component present in the original signal. This is
analogous to breaking a vector in a three dimensional Cartesian
space into its X, Y and Z components. The dot product of the
three-dimensional vector with each axis vector, assuming each axis
vector has a magnitude of 1, gives the magnitude of the three
dimensional vector in each of the three directions. The dot product
of the original signal and some other vector that is not
perpendicular to the other three dimensions provides redundant
data, since this magnitude is already contributed by two or more of
the orthogonal axes.
[0050] Because the principal components are orthogonal (i.e.,
perpendicular) to each other, the dot product of any principal
component with any other principal component is zero. Physically,
this means that the components do not interfere with each other. If
data is altered to change the magnitude of one component in the
original electromagnetic radiation signal, the other components
remain unchanged. In the analogous Cartesian example, reduction of
the X component of the three dimensional vector does not affect the
magnitudes of the Y and Z components.
[0051] Principal component analysis provides the fewest orthogonal
components that can accurately describe the data carried by the
electromagnetic radiation samples. Thus, in a mathematical sense,
the principal components are components of the original
electromagnetic radiation that do not interfere with each other and
that represent the most compact description of the spectral signal.
Physically, each principal component is an electromagnetic
radiation signal that forms a part of the original electromagnetic
radiation signal. Each principal component has a shape over some
wavelength range within the original wavelength range. Summing the
principal components may produce the original signal, provided each
component has the proper magnitude.
[0052] The principal components may comprise a compression of the
information carried by the total light signal. In a physical sense,
the shape and wavelength range of the principal components describe
what information is in the total electromagnetic radiation signal,
and the magnitude of each component describes how much of that
information is present. If several electromagnetic radiation
samples contain the same types of information, but in differing
amounts, then a single set of principal components may be used to
describe (except for noise) each electromagnetic radiation sample
by applying appropriate magnitudes to the components. The principal
components may be used to provide an estimate of a concentration or
characteristic of a sample based upon the information carried by
electromagnetic radiation that has interacted with that sample.
Differences observed in spectra of samples having varying
quantities of a constituent or values of a characteristic may be
described as differences in the magnitudes of the principal
components. Thus, the concentration of a constituent or value of a
characteristic may be expressed by the principal components
according to Formula 3 in the case where 4 principal components are
used,
y=a.sub.0+a.sub.1x.sub.1+a.sub.2x.sub.2+a.sub.3x.sub.3+a.sub.4x.sub.4
(Formula 3)
where y is a concentration of a constituent or value of a
characteristic, each a is a constant determined by the regression
analysis, and x.sub.1, x.sub.2, x.sub.3 and x.sub.4 are the first,
second, third, and fourth principal component magnitudes,
respectively. Formula 3 may be referred to as a regression vector.
The regression vector may be used to provide an estimate for the
concentration of a constituent or a value of a characteristic for
an unknown sample.
[0053] Regression vector calculations may be performed by computer,
based on spectrometer measurements of electromagnetic radiation by
wavelength. The spectrometer system spreads the electromagnetic
radiation into its spectrum and measures the spectral intensity at
each wavelength over the wavelength range. Using Formula 3, the
computer may read the intensity data and decompose the
electromagnetic radiation sample into the principal component
magnitudes x.sub.n by determining the dot product of the total
signal with each component. The component magnitudes are then
applied to the regression equation to determine a concentration or
value of a characteristic.
[0054] To simplify the foregoing procedure, however, the regression
vector may be converted to a form that is a function of wavelength
so that only one dot product is determined. Each normalized
principal component vector z.sub.n has a value over all or part of
the total wavelength range. If each wavelength value of each
component vector is multiplied by the regression constant a.sub.n
corresponding to the component vector, and if the resulting
weighted principal components are summed by wavelength, the
regression vector takes the form of Formula 4,
y=a.sub.0+b.sub.1u.sub.1+b.sub.2u.sub.2+ . . . +b.sub.nu.sub.n
(Formula 4)
where a.sub.0 is the first regression constant from Formula 3,
b.sub.n is the sum of the multiple of each regression constant
a.sub.n from Formula 3 and the value of its respective normalized
regression vector at wavelength n, and u.sub.n is the intensity of
the electromagnetic radiation at wavelength n. Thus, the new
constants define a vector in wavelength space that directly
describes a concentration or characteristic of a sample. The
regression vector in the form of Formula 4 represents the dot
product of an electromagnetic radiation sample with this
vector.
[0055] Normalization of the principal components provides the
components with an arbitrary value for use during the regression
analysis. Accordingly, it is very unlikely that the dot product
value produced by the regression vector will be equal to the actual
concentration or characteristic value of a sample being analyzed.
The dot product result is, however, a function of the concentration
or characteristic value. As discussed above, the function may be
determined by measuring one or more known calibration samples by
conventional means and comparing the result to the dot product
value of the regression vector. Thereafter, the dot product result
can be compared to the value obtained from the calibration
standards in order to determine the concentration or characteristic
of an unknown sample being analyzed. The function relating the dot
product to the concentration or characteristic may be of any type
including, for example, linear functions, quadratic functions,
polynomial functions, logarithmic functions, exponential functions,
and the like.
[0056] In some embodiments, principal component analysis techniques
may include partial least squares analysis, for example. The
principal component analysis may be conducted using standard
statistical analysis software packages including, for example, XL
Stat for MICROSOFT.RTM. EXCEL.RTM., the UNSCRAMBLER.RTM. from CAMO
Software, and MATLAB.RTM. from MATHWORKS.RTM..
[0057] In various embodiments, determination of a regression vector
and calculation of a dot product may take place under computer
control or other types of automated processing means. Further, as
described below, in some embodiments, a fluid may be modified to
change one or more concentrations or characteristics thereof. Such
processes may also take place under computer control, optionally
using an artificial neural network.
[0058] It is to be recognized that in the various embodiments
herein that take place under computer control or other automated
processing means, various blocks, modules, elements, components,
methods, and algorithms can be implemented through using computer
hardware, software and combinations thereof. To illustrate this
interchangeability of hardware and software, various illustrative
blocks, modules, elements, components, methods and algorithms have
been described generally in terms of their functionality. Whether
such functionality is implemented as hardware or software will
depend upon the particular application and any imposed design
constraints. For at least this reason, it is to be recognized that
one of ordinary skill in the art can implement the described
functionality in a variety of ways for a particular application.
Further, various components and blocks can be arranged in a
different order or partitioned differently, for example, without
departing from the spirit and scope of the embodiments expressly
described.
[0059] Computer hardware used to implement the various illustrative
blocks, modules, elements, components, methods and algorithms
described herein can include a processor configured to execute one
or more sequences of instructions, programming, or code stored on a
readable medium. The processor can be, for example, a general
purpose microprocessor, a microcontroller, a digital signal
processor, an application specific integrated circuit, a field
programmable gate array, a programmable logic device, a controller,
a state machine, a gated logic, discrete hardware components, an
artificial neural network or any like suitable entity that can
perform calculations or other manipulations of data. In some
embodiments, computer hardware can further include elements such
as, for example, a memory [e.g., random access memory (RAM), flash
memory, read only memory (ROM), programmable read only memory
(PROM), erasable PROM], registers, hard disks, removable disks,
CD-ROMs, DVDs, or any other like suitable storage device.
[0060] Executable sequences described herein can be implemented
with one or more sequences of code contained in a memory. In some
embodiments, such code can be read into the memory from another
machine-readable medium. Execution of the sequences of instructions
contained in the memory can cause a processor to perform the
process steps described herein. One or more processors in a
multi-processing arrangement can also be employed to execute
instruction sequences in the memory. In addition, hard-wired
circuitry can be used in place of or in combination with software
instructions to implement various embodiments described herein.
Thus, the present embodiments are not limited to any specific
combination of hardware and software.
[0061] As used herein, a machine-readable medium will refer to any
medium that directly or indirectly provides instructions to a
processor for execution. A machine-readable medium can take on many
forms including, for example, non-volatile media, volatile media,
and transmission media. Non-volatile media can include, for
example, optical and magnetic disks. Volatile media can include,
for example, dynamic memory. Transmission media can include, for
example, coaxial cables, wire, fiber optics, and wires that form a
bus. Common forms of machine-readable media can include, for
example, floppy disks, flexible disks, hard disks, magnetic tapes,
other like magnetic media, CD-ROMs, DVDs, other like optical media,
punch cards, paper tapes and like physical media with patterned
holes, RAM, ROM, PROM, EPROM and flash EPROM.
[0062] Various constituents may be present within a matrix,
particularly a fluid phase, and measurable through the techniques
described herein. In various embodiments, constituents within a
matrix that may be analyzed include, for example, organic compounds
(e.g., alcohols, carboxylic acids, amines, surfactants, polymers,
biopolymers, sugars, biomolecules, and the like), inorganic
compounds (e.g., salts, coordination compounds, organometallic
compounds, and the like), bacteria and other microorganisms, and
the like. As described hereinafter, constituents being analyzed in
the matrix are not believed to be particularly limited as long as a
suitable regression vector can be formulated for each constituent.
In some embodiments, the constituent(s) within the matrix may
comprise at least one ionic material. In some embodiments, the
constituent(s) within the matrix may comprise a neutral
substance.
[0063] Illustrative ionic materials that may be analyzed by the
techniques described herein include both cations and anions.
Cations and anions that may be analyzed include, for example, metal
ions, non-metal ions, complex ions, monatomic ions, diatomic ions,
triatomic ions, and polyatomic ions. In some embodiments, organic
cations such as, for example, quaternary ammonium ions or amine
salts may be analyzed by the techniques described herein. In some
embodiments, organic anions such as, for example, carboxylates,
phenoxides, organic phosphates, organic phosphonates, organic
phosphinates, organic sulfates, organic sulfinates, and organic
thiolates may be analyzed by the techniques described herein. In
some embodiments, inorganic ions may be analyzed by the techniques
described herein. In some embodiments, inorganic cations such as,
for example, alkali metal ions, alkaline earth metal ions,
transition metal ions, lanthanide ions, main group metal ions,
complex metal ions, and the like may be analyzed and their
concentration(s) determined. Illustrative metal ions that may be
analyzed include, for example, sodium-containing ions,
potassium-containing ions, strontium-containing ions,
magnesium-containing ions, calcium-containing ions,
barium-containing ions, and aluminum-containing ions. In some
embodiments, inorganic anions may be analyzed and their
concentration(s) determined. Illustrative inorganic anions that may
be analyzed include, for example, carbon-containing ions (e.g.,
carbonate and bicarbonate), sulfur-containing ions (e.g., sulfate,
sulfite, and sulfide), halogen-containing ions (e.g., fluoride,
chloride, chlorate, chlorite, hypochlorite, bromide, bromate,
iodide, iodate, periodate, and triiodide) and boron-containing ions
(e.g., borate). Some of the foregoing cations and anions are of
interest in the analysis of fracturing fluids, since their presence
may impact the suitability of a fracturing fluid for conducting a
fracturing operation. Other cations and anions that may be of
interest for analysis in fracturing operations and other
subterranean treatment operations may include, for example,
manganese-containing ions, lithium-containing ions,
cesium-containing ions, chromium-containing ions,
arsenic-containing ions, lead-containing ions, mercury-containing
ions, nickel-containing ions, copper-containing ions,
zinc-containing ions, and titanium-containing ions. It is to be
recognized that the foregoing lists of cations and anions are meant
to be illustrative in nature and non-limiting. Moreover, one of
ordinary skill in the art will be able to determine suitable
cations and anions to be analyzed to determine the suitability of a
substance for a given application. As discussed above, any cation
or anion may be analyzed by the techniques described herein if a
suitable regression vector can be determined to describe its
concentration in a particular fluid phase.
[0064] In some embodiments, at least some of the constituent(s)
within the matrix may be substantially spectroscopically inactive
within the spectral region being analyzed. In other embodiments, at
least some of the constituent(s) may be at least somewhat
spectroscopically active within the spectral region being analyzed.
For example, at least some of the constituent(s) may absorb
electromagnetic radiation within the spectral region being
analyzed. When a constituent absorbs at least some electromagnetic
radiation, it may absorb at substantially the same wavelengths as
the matrix or at substantially different wavelengths than the
matrix.
[0065] In various embodiments of the methods described herein, the
matrix may be spectroscopically active in the spectral region being
analyzed. That is, there may be a "peak" or like spectral feature
resulting from the optical interaction of electromagnetic radiation
with the matrix. In some embodiments of the present methods, a
spectroscopically active matrix may have a molar extinction
coefficient of at least about 0.001 M.sup.-1mm.sup.-1 associated
therewith. As one of ordinary skill in the art will recognize, the
molar extinction coefficient, .epsilon., of a substance at a given
wavelength is described by Beer's Law according to Formula 5, where
I is the
.epsilon.=I/(cL) (Formula 5)
measured spectral intensity at a given wavelength in units of
optical density, c is the concentration in molarity, and L is the
path length (typically in mm or cm) over which the optical
interaction takes place. In some embodiments, the matrix may have a
molar extinction coefficient of at least about 0.002
M.sup.-1mm.sup.-1, or at least about 0.003 M.sup.-1mm.sup.-1, or at
least about 0.004 M.sup.-1mm.sup.-1, or at least about 0.005
M.sup.-1mm.sup.-1, or at least about 0.006 M.sup.-1mm.sup.-1, or at
least about 0.007 M.sup.-1mm.sup.-1, or at least about 0.008
M.sup.-1mm.sup.-1, or at least about 0.009 M.sup.-1mm.sup.-1, or at
least about 0.01 M.sup.-1mm.sup.-1, or at least about 0.015
M.sup.-1mm.sup.-1, or at least about 0.02 M.sup.-1mm.sup.-1, or at
least about 0.025 M.sup.-1mm.sup.-1, or at least about 0.03
M.sup.-1mm.sup.-1, or at least about 0.035 M.sup.-1mm.sup.-1, or at
least about 0.04 M.sup.-1mm.sup.-1, or at least about 0.045
M.sup.-1mm.sup.-1, or at least about 0.05 M.sup.-1mm.sup.-1, or at
least about 0.055 M.sup.-1mm.sup.-1, or at least about 0.06
M.sup.-1mm.sup.-1, or at least about 0.065 M.sup.-1mm.sup.-1, or at
least about 0.07 M.sup.-1mm.sup.-1, or at least about 0.075
M.sup.-1mm.sup.-1, or at least about 0.08 M.sup.-1mm.sup.-1, or at
least about 0.085 M.sup.-1mm.sup.-1, or at least about 0.09
M.sup.-1mm.sup.-1, or at least about 0.095 M.sup.-1mm.sup.-1, or at
least about 0.1 M.sup.-1mm.sup.-1.
[0066] In addition to being capable of determining the
concentration or form of various constituents within the matrix,
the techniques described herein may be used to quantify at least
some characteristics of the matrix. As described above, certain
characteristics of a matrix, particularly a fluid phase, are not
conventionally believed to be derivable by a spectral analysis. In
some embodiments, characteristics that may be determined by the
techniques described herein include, for example, pH, total
dissolved solids, ionic strength, specific gravity, and any
combination thereof. Other characteristics that are believed to be
analyzable may include, for example, opacity, viscosity, total
suspended solids, and yield point. In some embodiments, bacteria or
like microorganisms are believed to be analyzable by the techniques
described herein.
[0067] The techniques described herein may be applicable to any
region of the electromagnetic spectrum in which the matrix
optically interacts with electromagnetic radiation. Illustrative
electromagnetic spectral regions that may be utilized for analysis
of constituent concentrations or fluid phase characteristics may
include, for example, the infrared (.lamda.=1 mm to 750 nm),
visible (.lamda.=750 nm to 400 nm), and ultraviolet (.lamda.=400 nm
to 10 nm) regions of the electromagnetic spectrum. In more specific
embodiments, the spectral region may comprise the near-infrared
(.lamda.=2500 nm to 750 nm) and/or the mid-infrared (.lamda.=20000
nm to 2500 nm) regions of the electromagnetic spectrum. In still
other embodiments, the spectral region may comprise the
near-ultraviolet (.lamda.=400 nm to 300 nm) and/or
middle-ultraviolet (.lamda.=300 nm to 200 nm) regions of the
electromagnetic spectrum. Choice of the spectral region for
analysis may be determined by the identity of the matrix and the
constituents being analyzed. For example, in some embodiments, the
spectral region may be chosen such that the matrix is
spectroscopically active, while the constituent(s) are
spectroscopically inactive. In other embodiments, the spectral
region may be chosen such that the matrix and at least some of the
constituent(s) are spectroscopically active.
[0068] In some embodiments, the spectral region being analyzed may
lie within a wavelength range of about 1000 nm to about 25000 nm.
In some or other embodiments, the spectral region being analyzed
may lie within a wavelength range of about 2000 nm to about 25000
nm. In still other embodiments, the spectral region being analyzed
may lie within a wavelength range of about 2000 nm and about 2000
nm.
[0069] The near-infrared region of the electromagnetic spectrum may
be particularly useful for analyzing aqueous fluids by the present
techniques. As one of ordinary skill in the art will recognize, the
spectral transitions associated with the infrared region may be
related to rotational and/or vibrational transitions, including
vibrational overtones and combinations thereof, which may be well
suited for assaying the O--H bond in water. As one of ordinary
skill in the art will further recognize, many commonly encountered
fluid constituents, including many inorganic ions, are
conventionally thought to be substantially optically inactive in
this spectral region. Water, in contrast, has strong absorption
bands in this spectral region. FIG. 1 shows near-infrared
absorption spectra for water at various cell path lengths. The
spectra are referenced against air. As can be seen from the
spectra, pristine water shows strong near-infrared absorption bands
between approximately 1360 nm-1600 nm, 1840 nm-2160 nm, and
>2280 nm. Without being bound by any theory or mechanism, it is
believed that these absorptions may be due to various water O--H
bond vibrations, vibrational overtones, or combinations thereof. In
the latter two regions, the peak absorbance is greater than 2
absorbance units, even for a 0.5 mm path length. FIGS. 2A and 2B
show near-infrared absorption spectra for various ionic
constituents in water. The spectra are measured relative to a water
reference at path length of 0.5 mm. As can be seen from FIG. 2A,
the presence of chloride, sulfate, borate, and iron (Fe.sup.3+)
ions results in slight deviations of the near-infrared absorption
spectrum when referenced against a pristine water background, with
chloride producing the most significant and distinct deviations.
FIG. 2B shows the absorption spectra of FIG. 2A with the chloride
spectrum omitted so that the spectral details of the remaining ions
may be more clearly seen. As can be seen from FIGS. 2A and 2B,
subtle differences exist between the various ionic constituents.
Given the location of the spectral absorbances of the ionic
constituents and without being bound by any theory or mechanism, it
is presumed that the constituents perturb the absorption spectrum
of the water rather than directly absorbing electromagnetic
radiation themselves. According to conventional theory in the
spectroscopic arts, the ionic constituents of FIGS. 2A and 2B would
be presumed to be optically inactive in the near-infrared spectral
region. Indeed, the small spectral perturbations induced by the
constituents could easily be lost without careful analysis of the
fluid phase, particularly when analyzing an intense spectral
interaction associated with the fluid phase. However, as
demonstrated herein, these weak spectral perturbations may be used
to extract a wealth of chemical information from a single
spectrum.
[0070] It is to be recognized that the spectral features observable
by the present methods are not thought to be limited to those
associated with the O--H bond in water. Other types of fluids may
also be analyzed by the techniques described herein, and other
spectral regions may be used for the analysis, if necessary. For
example, when analyzing a matrix comprising an oleaginous fluid,
other types of bonds may be analyzed including, for example, C--H,
C--O, C.dbd.O, C--X (X=halogen), C--N, N--H, S.dbd.O, and S--O. In
some embodiments, oleaginous fluids having these types of bonds may
be analyzed using the mid-infrared spectral region. In some
embodiments, a drilling mud comprising an oleaginous fluid or a
mixture of an oleaginous fluid and an aqueous fluid may be analyzed
by the techniques described herein.
[0071] In various embodiments, the techniques described herein may
be applied to samples using conventional, commercially available
spectrophotometers. This represents another advantage of the
present techniques, since custom-built equipment is not necessarily
required in order to practice the techniques. Any suitable type of
optical interaction of electromagnetic radiation with the sample
can be employed including, for example, absorption, transmission,
reflection, dispersion, and the like. Depending on the type of
sample, one of ordinary skill in the art will be able to determine
a suitable type of optical interaction upon which to conduct an
analysis. For example, reflection techniques may be more
appropriate for opaque samples in some cases. In some embodiments,
the techniques described herein may be applied to a static fluid
phase. For example, a sample comprising a fluid phase may be
removed from its source and analyzed in a sample container within a
standard spectrophotometer (e.g., at or near a job site, such as in
a field laboratory). In some or other embodiments, the analysis may
take place without removing the sample from the bulk material. That
is, in some embodiments, the analysis may take place in situ. In
some embodiments, the techniques described herein may be applied to
a fluid phase that is in motion. For example, in some embodiments,
the spectrum of a fluid phase may be obtained while a sample is
flowing through the spectrometer. In such embodiments, an optically
transparent fluid conduit containing the sample may be routed
through the spectrometer such that the analysis may take place.
Analysis of a flowing fluid phase may allow near real-time analysis
of the fluid composition and properties to take place. In still
other embodiments, a solid may be analyzed by the techniques
described herein.
[0072] In some embodiments, analyzing the spectrum of the matrix
may take place in real-time or near real-time. A result that is
returned in "real-time" may be returned essentially
instantaneously. A "near real-time" result may be returned after a
brief delay, which may be associated with processing time, further
data acquisition for determining a concentration or characteristic,
and the like. It will be appreciated by one having ordinary skill
in the art that the rate at which a sample concentration or sample
characteristic is determined in "real-time" or "near real-time" may
be dependent upon the rate at which processing takes place.
[0073] Regression vectors for each constituent or characteristic of
the fluid phase may be determined using the techniques set forth
herein. Data from pristine water and 27 field-produced water
samples having various constituent concentrations and
characteristic values was used to determine the regression vectors
herein (see Experimental Examples for more details on the water
samples). The regression vector for each constituent or
characteristic of a fluid phase may have a specific shape, given
that the various constituents may perturb the spectrum of a matrix
to differing degrees (see FIGS. 2A and 2B). FIGS. 3A-3D show
regression vectors determined for chloride, sulfate, total boron,
and total iron, respectively, over the wavelength range of 2000 nm
to 2350 nm. Comparing the regression vectors to one another, one
can see that differences exist between them. Further, for a given
constituent, subtle differences exist in the regression vectors
depending on the number of principal components used (see FIGS.
3A-3D). Regression vectors determined using both 5 and 6 principal
components are presented in FIGS. 3A-3D.
[0074] Regression vectors for characteristics may also be
determined in a like manner. FIG. 4 shows a regression vector
determined for specific gravity over the wavelength range of 1375
nm to 1900 nm. Regression vectors determined using both 5 and 6
principal components are presented in FIG. 4. Regression vectors
may also be determined for other characteristics, as set forth
above.
[0075] In some embodiments, the techniques described herein may be
used to analyze a fluid phase to determine if at least one property
of the fluid phase lies within a desired range. In some
embodiments, the techniques described herein may be used to analyze
the fluid phase of a treatment fluid, which may include any
constituent therein. In some embodiments, the techniques may be
used to analyze the fluid phase of a treatment fluid before
treatment fluid is introduced into a subterranean formation or
while the treatment fluid is being introduced into a subterranean
formation. For analyses that are conducted before introducing the
treatment fluid into the subterranean formation, the fluid phase
may be static or it may be in motion. For analyses that are
conducted while introducing the treatment fluid into the
subterranean formation, the analyses may typically be conducted
with the fluid phase in motion, although treatment fluid flow may
be stopped momentarily for analysis, if desired. In either case,
analyzing the treatment fluid before or while introducing the
treatment fluid to a subterranean formation may allow a problematic
treatment fluid to be identified and addressed. In various
embodiments, addressing a problematic treatment fluid may comprise
stopping the treatment operation, repeating the treatment
operation, performing a remediation operation, replacing the
treatment fluid, and/or modifying the treatment fluid in order to
address an out-of-range condition. In some or other embodiments,
the techniques described herein may be used to analyze the fluid
phase of a treatment fluid while the treatment fluid is located
within a subterranean formation.
[0076] In some embodiments, the techniques described herein may be
used to analyze a fluid phase (e.g., of a treatment fluid) before
or while introducing the fluid phase into a vessel, such as a
storage tank or a pipeline. For analyses that are performed before
introducing the fluid phase into the vessel, the fluid phase may be
static or in motion during the analysis. In some embodiments, the
techniques may be used to analyze a fluid phase while in the
vessel, such that changes that occur to the fluid therein may be
determined.
[0077] In some embodiments, the methods described herein may
further comprise determining if a treatment fluid is suitable for
being introduced into a subterranean formation (e.g., to determine
if at least one property is within a desired range). Given the
benefit of the analyses described herein and knowing the type of
subterranean formation and the type of treatment operation being
conducted, one of ordinary skill in the art will be able to make a
determination of the suitability of a treatment fluid for a given
situation. For example, a treatment fluid may contain a constituent
that is incompatible with the formation matrix, or the treatment
fluid may have a property that makes it incapable of performing a
desired function in the subterranean formation.
[0078] In some embodiments, the suitability of a treatment fluid
for a given application may be made in real-time or near real-time.
In some embodiments, the suitability of a treatment fluid for a
given application may be made automatically, such as with a
computer or like processing means.
[0079] In some embodiments, if the treatment fluid is determined to
be unsuitable for a given application, the present techniques may
further comprise altering a concentration of at least one
constituent of the treatment fluid, altering at least one
characteristic of the treatment fluid, or any combination thereof.
Such alteration may make the treatment fluid suitable for its
intended purpose. In some embodiments, alteration may comprise
adding more of or removing at least some of a constituent already
present in the treatment fluid. In some embodiments, alteration may
comprise adding another constituent to the treatment fluid that is
not already present. Adding more of or removing at least some of an
existing constituent or adding a new constituent may make the
concentration of the constituent or a characteristic related
thereto suitable for introduction to the subterranean formation.
For example, in some embodiments, a treatment fluid having a high
concentration of a metal constituent may be altered by adding a
chelating agent that is at least partially specific for the metal,
thereby reducing its effective concentration. In other embodiments,
a treatment fluid may be altered by adjusting its acidity. In still
other embodiments, a treatment fluid may be altered to adjust its
viscosity. In yet other embodiments, a treatment fluid may be
altered without addition of a constituent thereto or removal of a
constituent therefrom. For example, in some embodiments, a
treatment fluid may be allowed to stand for a period of time to
allow a concentration or characteristic to change with the passage
of time. In other embodiments, a treatment fluid may be heated,
cooled or exposed to ultraviolet light, for example, to change a
concentration or characteristic. Other types of related alterations
for a treatment fluid or like fluid phase may be envisioned by one
having ordinary skill in the art. In some embodiments, the methods
may further comprise analyzing the fluid phase of the treatment
fluid following its alteration. Thus, the present techniques may be
used to determine if the alteration has had its desired effect. In
some embodiments, altering the treatment fluid may take place
before introducing the treatment fluid into a subterranean
formation or a vessel. In other embodiments, altering the treatment
fluid may take place on-the-fly while introducing the treatment
fluid into a subterranean formation or a vessel. In still other
embodiments, altering a treatment fluid may take place after
introduction to a subterranean formation or a vessel. In some
embodiments, a fluid phase (e.g., of a treatment fluid) may be
exposed to electromagnetic radiation after being added to a
subterranean formation or to a vessel so as to determine its
behavior therein.
[0080] In some embodiments, altering a treatment fluid or like
fluid phase may take place automatically under computer control or
like processing means. For example, if an out-of-range condition is
detected in the treatment fluid, the treatment fluid may be
adjusted automatically in an attempt to correct the out-of-range
condition. In some embodiments, an artificial neural network may be
used to make predictive calculations of how to alter a treatment
fluid in a desired way, particularly if a constituent that is not
already present is being added. Use of an artificial neural network
may be particularly desirable when the treatment fluid composition
is far from that of its optimal composition. In this case, the
artificial neural network may be used to assist in the formulation
of treatment fluids having a custom formulation.
[0081] In some embodiments, providing a treatment fluid for
introduction to a subterranean formation may comprise blending a
fluid phase and at least one constituent. In some embodiments, the
fluid phase may comprise an aqueous fluid. In some embodiments, the
fluid phase may comprise water. In some or other embodiments, the
fluid phase may comprise an oleaginous fluid, such as a drilling
mud, for example. In some embodiments, the fluid phase may comprise
a produced aqueous fluid, such as a produced water, for example. In
some embodiments, the fluid phase may comprise a mixture of an
oleaginous fluid and an aqueous fluid. In some embodiments, the
fluid phase may be analyzed by the present techniques before being
combined with the constituent(s) to form the treatment fluid. Thus,
the present techniques may be used to determine if the fluid phase
is even capable of producing a treatment fluid having desired
properties. For example, a fluid phase having an unwanted
constituent, or too much or too little of a desired constituent,
would be less likely to produce a treatment fluid having desired
properties when combined with another constituent. Thus, the
present techniques may allow a treatment fluid to be analyzed at
various points during its formation and use, thereby potentially
reducing costs associated with poor quality treatment fluids that
ultimately have to be disposed of or reformulated. For example, in
some embodiments, a treatment fluid may be formed, analyzed, and
then stored for a period of time prior to introduction to a
subterranean formation. The techniques described herein may be used
to determine if the treatment fluid remains suitable for use
following its time in storage or transit to a job site.
[0082] Although the foregoing techniques may be used to analyze the
fluid phase of any type of treatment fluid, the techniques may be
particularly advantageous when applied to fracturing fluids,
acidizing fluids, or a combination thereof (e.g., a
fracture-acidizing fluid). As discussed in detail herein,
incompatibilities are particularly common with these types of
treatment fluids, and the present techniques may allow the
suitability of these types of treatment fluids to be better
determined. In other embodiments, the techniques may be used to
analyze drilling fluids, conformance control fluids, sealants,
cements, scale inhibitor fluids, biocidal fluids, and the like.
[0083] The ability to analyze a treatment fluid both before and
after its formation may be particularly advantageous when using
produced water or other produced aqueous fluids as the fluid phase
of a treatment fluid. As described above, reuse of produced aqueous
fluids in subterranean treatment operations may be desirable from a
cost and environmental standpoint. However, the as-obtained
produced aqueous fluids may be unsuitable for some applications, or
they may only become suitable after being further altered in some
manner, as described above. The ability to determine the
suitability of produced aqueous fluids for a given application may
be further complicated by the complexity of these fluids and the
difficulties in quickly analyzing them by conventional analytical
techniques.
[0084] In some embodiments, methods described herein can comprise:
providing a produced aqueous fluid from a subterranean formation;
exposing the produced aqueous fluid to electromagnetic radiation in
a spectral region where water comprising the produced aqueous fluid
optically interacts with electromagnetic radiation, so as to
acquire a spectrum of the water; analyzing the spectrum of the
water to determine at least one property of the produced aqueous
fluid, the at least one property of the produced aqueous fluid
being selected from the group consisting of a concentration of at
least one constituent in the produced aqueous fluid, at least one
characteristic of the produced aqueous fluid, and any combination
thereof; and optionally, altering a concentration of at least one
constituent in the produced aqueous fluid, altering at least one
characteristic of the produced aqueous fluid, or any combination
thereof. Alteration of the at least one concentration or the at
least one characteristic may take place as described above.
[0085] In some embodiments, the methods may further comprise
re-introducing the produced aqueous fluid into a subterranean
formation, the subterranean formation comprising the same
subterranean formation that produced the aqueous fluid or a
different subterranean formation. For example, in some embodiments,
a produced aqueous fluid that is sufficiently free of contaminants
may be reintroduced to a subterranean formation simply as a means
of disposal. In other embodiments, the produced aqueous fluid may
be altered to make it suitable for reintroduction to a subterranean
formation as a means of disposal. In some embodiments, the methods
may further comprise forming a treatment fluid comprising the
produced aqueous fluid, before re-introducing the produced aqueous
fluid into the subterranean formation.
[0086] Various types of treatment fluids may be formulated using a
produced aqueous fluid and analyzed by the foregoing techniques. In
some embodiments, the treatment fluid may comprise a fracturing
fluid, an acidizing fluid, or any combination thereof, for example.
Other types of treatment fluids that may be formed from produced
aqueous fluids may also be envisioned by one having ordinary skill
in the art, such as, for example, drilling fluids, scale inhibitor
fluids, biocidal fluids, or any combination thereof. Depending on
the intended treatment operation, the constituents and
characteristics of the water being analyzed will likely vary from
application to application, as described previously. For example,
when performing a fracturing operation, certain ionic species, if
present, may impact the outcome of a fracturing operation.
Likewise, in an acidizing operation, particularly of a siliceous
subterranean formation, the presence of calcium ions or alkali
metal ions in the fluid phase may cause precipitate formation that
can damage the subterranean formation.
[0087] Illustrative substances that may be present in any of the
treatment fluids described herein include, for example, acids,
acid-generating compounds, bases, base-generating compounds,
surfactants, scale inhibitors, corrosion inhibitors, gelling
agents, crosslinking agents, anti-sludging agents, foaming agents,
defoaming agents, antifoam agents, emulsifying agents,
de-emulsifying agents, iron control agents, proppants or other
particulates, gravel, particulate diverters, salts, fluid loss
control additives, gases, catalysts, clay control agents, chelating
agents, corrosion inhibitors, dispersants, flocculants, scavengers
(e.g., H.sub.2S scavengers, CO.sub.2 scavengers or O.sub.2
scavengers), lubricants, breakers, delayed release breakers,
friction reducers, bridging agents, viscosifiers, weighting agents,
solubilizers, rheology control agents, viscosity modifiers, pH
control agents (e.g., buffers), hydrate inhibitors, relative
permeability modifiers, diverting agents, consolidating agents,
fibrous materials, bactericides, tracers, probes, nanoparticles,
and the like. Combinations of these substances can be used as
well.
[0088] To facilitate a better understanding of the present
invention, the following examples of preferred or representative
embodiments are given. In no way should the following examples be
read to limit, or to define, the scope of the invention.
EXAMPLES
Example 1
Analysis of Field-Produced Water Samples Using Near-Infrared
Spectroscopy
[0089] 27 field-produced water samples from various sources were
obtained, and near-infrared spectra for each were acquired over the
wavelength range of 1000 nm-2500 nm at a cell path length of 2 mm.
FIG. 5 shows an aggregate near-infrared absorption spectrum of the
27 field-produced water samples at a path length of 2 mm against a
water reference. As can be seen from FIG. 5, the spectra were very
complex, particularly in the regions of highest absorption,
although subtle differences do exist between them.
[0090] Following acquisition of the spectra, the spectra were
normalized and converted into transmission spectra. FIG. 6 shows an
aggregate near-infrared absorption spectrum of the 27
field-produced water samples at a path length of 2 mm against a
water reference following normalization. As can be seen from FIG.
6, normalization considerably reduced the complexity of the
individual spectra. FIGS. 7A and 7B show expansions of the data of
FIG. 6 following conversion into transmission mode. Again, it can
be seen that the individual spectra were distinct but very similar
to one another. Further, it can be seen that the percent
transmission is fairly low due to the strong absorbance of water in
the spectral region of interest.
[0091] Experimental values for ionic concentrations of sodium,
total iron, barium, magnesium, calcium, strontium, potassium,
aluminum, total boron, bicarbonate, sulfate, and chloride were
determined by an appropriate analytical technique for each sample.
In addition, the samples were analyzed for their specific gravity,
ionic strength, total dissolved solids, and pH values. The
experimental concentrations and the values of the 27 water samples
are summarized in Table 1. Concentration values are expressed in
ppm units in the Table. Estimated or assumed values are marked with
an asterisk. Estimated or assumed values were used when the
analyzed value was below that of the analytical detection limit. In
this case, the estimated or assumed value was taken to be half the
value of the analytical detection limit.
TABLE-US-00001 TABLE 1 1 2 3 4 5 6 7 specific gravity 1.0000 1.1750
1.1260 1.1440 1.1370 1.1580 1.1160 pH 7.0000 5.550 6.510 6.570
6.500 6.490 7.480 ionic strength 0.0000 4.7190 4.1070 3.9530 3.4520
4.1630 3.2190 bicarbonate 0.0000 122.0 1050.0 397.0 1.40.0 305.0
381.0 chloride 0.0000 166568 116443 138163 125263 152941 108227
sulfate 0.0000 385.6 1103.5 120.5 8834 800.4 810.8 calcium 0.0000
12100 3610 2560 1060 3160 5460 magnesium 0.0000 1400 634 1030 362
936 835 barium 0.0000 3.00 0.73 3.25 0.65 1.15 1.50 strontium
0.0000 655 364 805 207 483 256 total iron 0.0000 2.10 2.37 0.87
0.60 2.52 3.98 aluminum 0.0000 1.19 0.75 0.73 0.40 0.64 0.78 boron
0.0000 209.00 28.00 21.60 29.20 24.60 143.00 potassium 0.0000 3330
1180 1210 1230 1420 2080 sodium 0.0000 84500 64210 75900 73100
84700 57800 total dissolved 0.0000 253000 233000 224000 200000
235000 177000 solids NOTES 1 8 9 10 11 12 13 specific gravity
1.0000 1.1090 1.1920 1.1870 1.1870 1.1000 1.1060 pH 7.0000 7.680
4.660 5.980 6.531 9.431 6.977 ionic strength 0.0000 3.0900 5.6880
5.8940 5.7420 2.8400 2.8410 bicarbonate 0.0000 198.0 31.0 76.0
109.7 0.0 167.7 chloride 0.0000 99226 179708 175068 168525 84940
85641 sulfate 0.0000 602.7 210.9 106.6 211.0 3.8* 3.8* calcium
0.0000 5610 15600 23600 28700 11200 10900 magnesium 0.0000 735 1440
3720 4270 262 543 barium 0.0000 1.88 8.09 4.32 2.53 1160.00 1050.00
strontium 0.0000 304 1130 1690 1130 2130 2020 total iron 0.0000
8.60 7.44 0.95 0.29 0.27 0.10 aluminum 0.0000 0.79 1.12 1.11 1.87
1.30 1.02 boron 0.0000 148.00 308.00 40.20 30.40 77.40 76.40
potassium 0.0000 2180 5380 1560 1390 894 955 sodium 0.0000 51700
83500 70200 67700 43400 42200 total dissolved 0.0000 170000 305000
295000 276000 147000 147000 solids NOTES 1 14 15 16 17 18 19
specific gravity 1.0000 1.0990 1.0720 1.0220 1.1200 1.0100 1.0730
pH 7.0000 6.826 6.625 7.628 7.229 6.847 7.429 ionic strength 0.0000
3.0640 1.8790 0.5190 3.3420 0.2980 2.0600 bicarbonate 0.0000 206.4
219.3 270.9 322.5 567.7 206.4 chloride 0.0000 95189 60421 16158
17009 7900 65970 sulfate 0.0000 186.0 475.0 3.8 96.0 143.0 456.0
Calcium 0.0000 10500 1090 1570 8550 257 4860 magnesium 0.0000 513
195 110 2 25 5 barium 0.0000 15.20 0.80 15.60 6.28 5.88 3.96
strontium 0.0000 813 242 239 647 51 324 total iron 0.0000 0.10 0.18
0.10 0.17 3.96 0.26 aluminum 0.0000 1.08 0.34 0.38 0.86 1.75 0.84
boron 0.0000 202.00 35.70 93.60 366.00 115.00 229.00 potassium
0.0000 3270 402 244 5410 40 3400 sodium 0.0000 48000 40400 8630
57400 5890 36500 total dissolved 0.0000 162000 107000 27600 184000
17200 114000 solids NOTES 1 20 21 22 23 24 25 specific gravity
1.0000 1.0150 1.0270 1.0740 1.0230 1.0760 1.0070 pH 7.0000 7.630
4.400 6.900 7.000 5.540 6.000 ionic strength 0.0000 0.3740 0.7710
2.1510 0.4804 0.1180 2.4336 bicarbonate 0.0000 1020.0 0.0 154.85
258.0 204.0 149.7 chloride 0.0000 5178 12494 69711 20358 3048 72744
sulfate 0.0000 241.0 204.0 2624.6 35.5 25.3 3.8* calcium 0.0000 68
1820 4770 594 445 10300 magnesium 0.0000 37 24 1080 21 17 394
barium 0.0000 0.08 14.10 0.24 7.09 0.79 276.00 strontium 0.0000 41
340 107 1740 6 1600 total iron 0.0000 0.16 19.40 2.03 0.61 2.45
42.70 aluminum 0.0000 0.59 1.88 0.63 0.18 1.61 0.87 boron 0.0000
61.50 40.30 15.20 48.10 50.30 267.00 potassium 0.0000 116 138 570
99 63 1390 sodium 0.0000 7950 12100 36100 7740 1526 30800 total
dissolved 0.0000 21900 41354 113545 26569 6220 115597 solids NOTES
visually oily and opaque visually opaque 1 26 27 28 specific
gravity 1.0000 1.0070 1.0100 1.0100 pH 7.0000 7.090 7.910 7.710
ionic strength 0.0000 0.1702 0.2100 0.2482 bicarbonate 0.0000 529.0
812.8 890.2 chloride 0.0000 3473 4633 6068 sulfate 0.0000 96.0
348.5 294.3 calcium 0.0000 228 190 162 magnesium 0.0000 97 87 74
barium 0.0000 1.26 1.57 2.23 strontium 0.0000 13 20 23 total iron
0.0000 9.49 9.30 10.80 aluminum 0.0000 0.84 0.64 0.57 boron 0.0000
99.00 98.90 101.00 potassium 0.0000 68 83 98 sodium 0.0000 2030
2900 3680 total dissolved 0.0000 6850 9479 11892 solids NOTES oil-
oil- oil- contaminated contaminated contaminated
[0092] Using the experimentally determined concentrations or
values, a regression vector was determined for each ionic
concentration or characteristic. The regression vectors for
chloride, sulfate, total boron, and total iron are shown in FIGS.
3A-3D, respectively, and the regression vector for specific gravity
is shown in FIG. 4. Regression vectors for the other constituents
and characteristics were determined but are not shown herein.
Regression vector determination was conducted using partial least
squares (PLS) analysis and 5 or 6 principal components. PLS
analysis was conducted using two different software protocols:
MATLAB (Mathworks) and THE UNSCRAMBLER (Camo). We found that use of
5 to 6 principal components provided a sufficiently accurate
estimation without overly modeling spectral noise. Additional
details concerning determination of the regression vectors can be
found in the Detailed Description hereinabove. It is to be noted
that no data was excluded from the analyses, even for samples that
were visually opaque or oily or that utilized estimated values. For
purposes of the analyses presented herein, the regression vector
for each ionic constituent or fluid phase characteristic can be
considered to be a chart of the regression coefficient as a
function of wavelength.
[0093] Using each experimental spectrum, the dot product of each
regression vector was determined over the spectral region of
interest. A scalar quantity was obtained from the dot product
analysis (i.e., a real number) for each ionic constituent or fluid
phase characteristic. The dot product of each regression vector was
determined by multiplying the spectral intensity at a given
wavelength by the regression coefficient at the same wavelength and
summing the product over the entire wavelength region.
[0094] The result of the dot product analysis was then correlated
with a concentration or characteristic of reference standards
having a known concentration or characteristic value. In this
instance, the known concentrations of the samples were used as the
set of standard reference samples, rather than formulating a set of
independent reference standards. Comparison of the dot product
values of the 27 water samples to the known values from the
calibration curves then allowed the water sample concentrations or
characteristic values to be determined.
[0095] FIGS. 8A-8D show illustrative calibration curves for
chloride, sulfate, total boron, and total iron, respectively. The
dot product values in the calibration curves were obtained using
the regression vectors (6 principal components) of FIGS. 3A-3E,
respectively. In each case, the data was modeled with a linear
least square fit, and an R.sup.2 value of >0.94 was obtained in
each instance.
[0096] Comparison of the dot product values from the 27 water
samples to the corresponding calibration curves for each
constituent or characteristic produced good correlations with the
experimentally determined values. FIGS. 9A-9I show illustrative
plots of predicted concentration, as determined by dot product
analysis, compared to the experimentally determined concentration.
Predicted values using both 5- and 6-principal component regression
vectors are presented (calibration curve data for 5-principal
component regression vectors not shown herein). In each case, the
data was modeled with a linear least squares fit, and an R.sup.2
value of >0.89 was obtained in each instance. FIGS. 9A and 9B
show plots of predicted versus experimental sulfate concentration
for 5- and 6-principal components, respectively. FIGS. 9C and 9D
show plots of predicted versus experimental total boron
concentration for 5- and 6-principal components, respectively.
FIGS. 9E and 9F show plots of predicted versus experimental total
iron concentration for 5- and 6-principal components, respectively.
FIG. 9G shows a plot of predicted versus experimental total iron
concentration for 6-principal components with additional high iron
concentration values included. FIGS. 9H and 9I show plots of
predicted versus experimental chloride concentration for 5- and
6-principal components, respectively. It should be noted that a
good fit was obtained even for samples that were visually opaque or
oil-contaminated. In FIGS. 9A-9F, 9H, and 9I, samples that were
"oily" or "opaque" have been indicated in the chart legend as
such.
[0097] Table 2 summarizes the accuracies associated with the
analyses described above for the 27 field-produced water samples.
The data presented in Table 2 is that obtained for the 5-principal
component regression vector. The wavelength range in Table 2
indicates the wavelength range over which the presented values were
determined. It is to be recognized that conducting the analysis
over a different wavelength range would produce a slightly
different result.
TABLE-US-00002 TABLE 2 Standard Concentration Standard Deviation/
or Property Deviation Full Range Analyzed Analyzed Range of
Prediction (%) Wavelength Range specific gravity 1-1.192 0.003 1.3
1850 nm-2350 nm ionic strength 0-5.894 0.119 2.0 1875 nm-2350 nm
total dissolved solids 0-305,000 6827 2.2 2025 nm-2325 nm pH
4.4-9.43 0.35 7.0 1900 nm-2300 nm sodium 0 ppm-84700 ppm 1472 ppm
1.7 1750 nm-2200 nm calcium 0 ppm-28700 ppm 881 ppm 3.1 2125
nm-2350 nm magnesium 0 ppm-4270 ppm 196 ppm 4.6 1875 nm-2325 nm
chloride 0 ppm-179708 ppm 8539 ppm 4.8 2175 nm-2350 nm total iron 0
ppm-42.7 ppm 2.2 ppm 5.3 2000 nm-2300 nm barium 0 ppm-1160 ppm 81
ppm 7.0 2025 nm-2300 nm strontium 0 ppm-2130 ppm 152 ppm 7.1 2150
nm-2325 nm potassium 0 ppm-5410 ppm 424 ppm 7.8 2100 nm-2350 nm
sulfate 0 ppm-2625 ppm 221 ppm 8.4 2075 nm-2325 nm aluminum 0
ppm-1.88 ppm 0.16 ppm 8.7 2075 nm-2350 nm bicarbonate 0 ppm-1340
ppm 120 ppm 9.0 2050 nm-2350 nm borate 0 ppm-366 ppm 36 ppm 9.8
2150 nm-2350 nm
[0098] Therefore, the present invention is 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 present invention 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
and spirit of the present invention. The invention illustratively
disclosed herein suitably may 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," "having," 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.
* * * * *