U.S. patent application number 15/062164 was filed with the patent office on 2016-09-08 for risk assessment for drilling and well completion operations.
The applicant listed for this patent is The Hartford Steam Boiler Inspection and Insurance Company. Invention is credited to Richard B. Jones.
Application Number | 20160260036 15/062164 |
Document ID | / |
Family ID | 55661555 |
Filed Date | 2016-09-08 |
United States Patent
Application |
20160260036 |
Kind Code |
A1 |
Jones; Richard B. |
September 8, 2016 |
RISK ASSESSMENT FOR DRILLING AND WELL COMPLETION OPERATIONS
Abstract
A method, apparatus and system is provided for assessing risk
for well completion, comprising: obtaining, using an input
interface, a Below Rotary Table hours and a plurality of well-field
parameters for one or more planned runs, determining, using at
least one processor, one or more non-productive time values that
correspond to the one or more planned runs based upon the
well-field parameters, developing, using at least one processor, a
non-productive time distribution and a Below Rotary Table
distribution via one or more Monte Carlo trials; and outputting,
using a graphic display, a risk transfer model results based on a
total BRT hours from the Below Rotary Table and the non-productive
time distribution produced from the one or more Monte Carlo
trials.
Inventors: |
Jones; Richard B.;
(Georgetown, TX) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
The Hartford Steam Boiler Inspection and Insurance Company |
Hartford |
CT |
US |
|
|
Family ID: |
55661555 |
Appl. No.: |
15/062164 |
Filed: |
March 6, 2016 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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62129615 |
Mar 6, 2015 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06Q 50/02 20130101;
G06Q 10/0635 20130101; G16C 10/00 20190201 |
International
Class: |
G06Q 10/06 20060101
G06Q010/06 |
Claims
1. A method for assessing risk for well completion, comprising:
obtaining, using an input interface, a Below Rotary Table hours and
a plurality of well-field parameters for one or more planned runs;
determining, using at least one processor, one or more
non-productive time values that correspond to the one or more
planned runs based upon the well-field parameters; developing,
using at least one processor, a non-productive time distribution
and a Below Rotary Table distribution via one or more Monte Carlo
trials; and outputting, using a graphic display, a risk transfer
model results based on a total BRT hours from the Below Rotary
Table and the non-productive time distribution produced from the
one or more Monte Carlo trials.
2. The method of claim 1, wherein the number of Monte Carlo trials
is supplied by a user as an input parameter.
3. The method of claim 1, wherein the non-productive time
distribution is based on a non-productive time event frequency
parameter and a non-productive time severity parameter associated
with the one or more planned runs.
4. The method of claim 3, wherein the non-productive time event
frequency parameter is a function of a bottom hole assembly
configuration parameter.
5. The method of claim 3, wherein the non-productive time severity
is a function of a hole size parameter, a depth parameter, a
drilled length parameter, and a maximum dog leg parameter
associated with the one or more planned runs.
6. The method of claim 3, wherein the non-productive time severity
for each planned run is multiplied by a binary frequency function
to compute the non-productive time distribution.
7. The method of claim 1, wherein the risk transfer model results
account for a specific country or field based on a location
modifier factor.
8. The method of claim 1, wherein the risk transfer model results
generate a future risk score that estimates future non-productive
time performance.
9. The method of claim 1, wherein the risk transfer model comprises
land and offshore non-productive time.
10. The method of claim 1, wherein the risk transfer model converts
the risk transfer model results from a non-productive time risk to
a financial risk.
11. An apparatus for estimating actual downtimes from drilling
operations using a risk transfer model, comprising: a
non-transitory memory; a processor coupled to the non-transitory
memory, wherein the processor obtains computer executable
instructions stored on a non-transitory memory that when executed
by the processor causes the apparatus to perform the following:
receive a plurality of field parameters that correspond to a
plurality of planned runs via an input interface; determine a
non-productive time risk for each of the planned runs based on the
field parameters; generate a total non-productive time risk using
one or more Monte Carlo trials; and output the total non-productive
time risk via a user interface, wherein the number of Monte Carol
trials is received via the input interface.
12. The apparatus of claim 11, wherein the non-productive time risk
is based on a non-productive time event frequency parameter and a
non-productive time severity parameter associated with the one or
more planned runs.
13. The apparatus of claim 12, wherein the non-productive time
event frequency parameter is a function of a bottom hole assembly
configuration parameter.
14. The apparatus of claim 12, wherein the non-productive time
severity is a function of a hole size parameter, a depth parameter,
a drilled length parameter, and a maximum dog leg parameter
associated with the one or more planned runs.
15. The apparatus of claim 12, wherein the non-productive time
severity for each planned run is multiplied by a binary frequency
function to compute the non-productive time distribution.
16. The apparatus of claim 11, wherein the non-productive time risk
results account for a specific country or field based on a location
modifier factor.
17. The apparatus of claim 11, wherein the non-productive time risk
results comprises land and offshore non-productive time.
18. A system comprising: an input interface; an user interface; a
processor coupled the input interface and the user interface,
wherein the processor receives computer executable instructions
stored on a memory that when executed by the processor causes the
following: receive a plurality of field parameters that correspond
to a plurality of planned runs via an input interface; determine a
non-productive time risk for each of the planned runs based on the
field parameters; generate a total non-productive time risk using
one or more Monte Carlo trials; and output the total non-productive
time risk via a user interface, wherein the number of Monte Carol
trials is received via the input interface.
19. The system of claim 18, wherein the non-productive time risk is
based on a non-productive time event frequency parameter and a
non-productive time severity parameter associated with the one or
more planned runs.
20. A method, system, and apparatus magnetically detecting
equipment external to a downhole pipe prior to performing certain
well operations as shown and described.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims the benefit, and priority benefit,
of U.S. Provisional Patent Application Ser. No. 62/129,615, filed
Mar. 6, 2015, titled "RISK ASSESSMENT FOR DRILLING AND WELL
COMPLETION OPERATIONS," the disclosure of which is incorporated
herein in its entirety.
STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT
[0002] Not applicable.
REFERENCE TO A MICROFICHE APPENDIX
[0003] Not applicable.
FIELD OF THE INVENTION
[0004] The present invention generally relates to determining and
predicting risk based on results from failures that originate from
an operator's product and service delivery using a risk transfer
model (RTM). Specifically, but not by way of limitation,
embodiments of the present invention include quantitatively
assessing risk and reliability for drilling and well completion
based upon a variety of parameters, such as non-productive time
(NPT).
BACKGROUND OF THE INVENTION
[0005] In the oil and gas industry, drilling and completing
hydrocarbon wells involve a complex process of drilling and other
operations. When completing a hydrocarbon well, drilling operators
typically run a variety of downhole monitoring equipment within the
wellbore to improve well completion effectiveness and profitably.
Often times when completing wells, drilling operators are bound to
strict timelines and may gauge their performance using variety of
performance metrics. In many instances, to remain profitable, the
drilling operators may need to exceed the performance metrics
and/or complete the wells before the designed timeline. For
instance, drilling operators may be evaluated on the performance
metric nonproductive time or NPT. In certain instances, if the
total NPT time for completing a well exceeds a specified threshold,
the drilling operator may be required to pay a penalty equivalent
to the damages caused by the excessive NPT. Alternatively, for the
same well completion job, the drilling operator may receive bonus
compensation if the total NPT time falls under the specified
threshold. As such, numerous innovations and improvements are
needed to accurately gauge NPT and/or other performance parameters
and assess well completion risk.
BRIEF SUMMARY
[0006] The following presents a simplified summary of the disclosed
subject matter in order to provide a basic understanding of some
aspects of the subject matter disclosed herein. This summary is not
an exhaustive overview of the technology disclosed herein. It is
not intended to identify key or critical elements of the invention
or to delineate the scope of the invention. Its sole purpose is to
present some concepts in a simplified form as a prelude to the more
detailed description that is discussed later.
[0007] In one embodiment, a method for assessing risk for well
completion, comprising: obtaining, using an input interface, a
Below Rotary Table hours and a plurality of well-field parameters
for one or more planned runs, determining, using at least one
processor, one or more non-productive time values that correspond
to the one or more planned runs based upon the well-field
parameters, developing, using at least one processor, a
non-productive time distribution and a Below Rotary Table
distribution via one or more Monte Carlo trials; and outputting,
using a graphic display, a risk transfer model results based on a
total BRT hours from the Below Rotary Table and the non-productive
time distribution produced from the one or more Monte Carlo
trials.
[0008] In another embodiment, an apparatus for estimating actual
downtime from drilling operations using a risk transfer model,
comprising: a non-transitory memory, a processor coupled to the
non-transitory memory, wherein the processor obtains computer
executable instructions stored on a non-transitory memory that when
executed by the processor causes the apparatus to perform the
following: receive a plurality of field parameters that correspond
to a plurality of planned runs via an input interface; determine a
non-productive time risk for each of the planned runs based on the
field parameters; generate a total non-productive time risk using
one or more Monte Carlo trials; and output the total non-productive
time risk via a user interface, wherein the number of Monte Carol
trials is received via the input interface.
[0009] In another embodiment, a system comprising: an input
interface, an user interface, a processor coupled the input
interface and the user interface, wherein the processor receives
computer executable instructions stored on a memory that when
executed by the processor causes the following: receive a plurality
of field parameters that correspond to a plurality of planned runs
via an input interface, determine a non-productive time risk for
each of the planned runs based on the field parameters, generate a
total non-productive time risk using one or more Monte Carlo
trials, and output the total non-productive time risk via a user
interface, wherein the number of Monte Carol trials is received via
the input interface.
BRIEF DESCRIPTION OF THE DRAWING
[0010] For a more complete understanding of this disclosure,
reference is now made to the following brief description, taken in
connection with the accompanying drawings and detailed description,
wherein like reference numerals represent like parts.
[0011] FIG. 1 is a schematic diagram of an embodiment of a RTM
processing system;
[0012] FIG. 2 illustrates an embodiment of the displayed RTM on the
RTM processing system;
[0013] FIG. 3 illustrates a "Country RM" tab used by the RTM;
[0014] FIG. 4 illustrates an embodiment of the NPT event frequency,
severity, and risk, by year, location, land and offshore;
[0015] FIG. 5 illustrates an embodiment of running an RTM for 22
planned runs with 10,000 Monte Carlo trials;
[0016] FIG. 6 illustrates an embodiment of the well completion time
risk with a prescribed time deductible;
[0017] FIG. 7 illustrates an embodiment of an amended distribution
based on FIG. 5 that shows the 10 hours NPT deductible from FIG.
6;
[0018] FIG. 8 illustrates an embodiment of the well completion time
risk with a prescribed time deductible and a time ceiling;
[0019] FIG. 9 illustrates an embodiment of an updated distribution
based on FIG. 5 that show the risk exposure the drilling operator
is retaining; and
[0020] FIG. 10 illustrates an embodiment of when the well
completion time risk greater than the time ceiling.
[0021] While certain embodiments will be described in connection
with the preferred illustrative embodiments shown herein, it will
be understood that it is not intended to limit the invention to
those embodiments. On the contrary, it is intended to cover all
alternatives, modifications, and equivalents, as may be included
within the spirit and scope of the invention as defined by claims
to be filed in a subsequent non-provisional patent application. In
the drawing figures, which are not to scale, the same reference
numerals are used throughout the description and in the drawing
figures for components and elements having the same structure, and
primed reference numerals are used for components and elements
having a similar function and construction to those components and
elements having the same unprimed reference numerals.
DETAILED DESCRIPTION
[0022] It should be understood that, although an illustrative
implementation of one or more embodiments are provided below, the
various specific embodiments may be implemented using any number of
techniques known by persons of ordinary skill in the art. The
disclosure should in no way be limited to the illustrative
embodiments, drawings, and/or techniques illustrated below,
including the exemplary designs and implementations illustrated and
described herein. Furthermore, the disclosure may be modified
within the scope of the appended claims along with their full scope
of equivalents.
[0023] Disclosed herein are various example embodiments that
produces and implements a quantitative RTM that accounts for a
variety of well-field characteristics that include, but are not
limited to drilling time, location, downhole tools used, distance
drilled, and well conditions. The RTM may standardize key
performance indicators that may be used to differentiate drilling
operators' products and services in the marketplace. The RTM may
comprise a NPT parameter that indicates well failures from the
product or service delivery of the drilling operator(s). Other well
failures, such as failure operating outside specification,
non-product or service delivery not impacted by the drilling
operator(s), product relevant notification and in-house product
functional failure may be omitted in assessing a drilling
operator(s) NPT performance.
[0024] In one embodiment, the RTM may be used to set and price
insurance structures and coverage limitations. For example, the RTM
may provide a more competitive edge in drilling operation tenders,
promote continuous performance improvement in drilling operations,
and generate operational key performance indicators that can be
financially interpreted and measured by oil field personnel.
[0025] FIG. 1 is a schematic diagram of an embodiment of a RTM
processing system 100 that may correspond to or may be part of a
computer and/or any other computing device, such as a workstation,
server, mainframe, super computer, and/or database. The RTM
processing system 100 includes a processor 102, which may be also
be referenced as a central processor unit (CPU). The processor 102
may communicate (e.g., via a system bus) and/or provide
instructions to other components within the RTM processing system
100, such as the input interface 104, output interface 106, and/or
memory 108. In one embodiment, the processor 102 may include one or
more multi-core processors and/or memory (e.g., cache memory) that
function as buffers and/or storage for data. In other words,
processor 102 may be part of one or more other processing
components, such as application specific integrated circuits
(ASICs), field-programmable gate arrays (FPGAs), and/or digital
signal processors (DSPs). Although FIG. 1 illustrates that
processor 102 may be a single processor, processor 102 is not so
limited and instead may represent a plurality of processors. The
processor 102 may be configured to implement any of the methods
described herein.
[0026] FIG. 1 illustrates that the processor 102 may be operatively
coupled to one or more input interfaces 104 configured to obtain
drilling data for one or more wells sites and one or more output
interfaces 106 configured to output and/or display the simulated
RTM results, inputted drilling data, and/or other field drilling
information. The input interface 104 may be configured to obtain
the drilling data via electrical, optical, and/or wireless
connections using one or more communication protocols. In one
embodiment, the input interface 104 may be a network interface that
comprises a plurality of ports configured to receive and/or
transmit data via a network. In particular, the network may
transmit the drilling data via wired links, wireless link, and/or
logical links. Other examples of the input interface 104 may
include keyboards, mice, universal serial bus (USB) interfaces,
CD-ROMs, DVD-ROMs and/or onscreen input devices (e.g., onscreen
keyboard). The output interface 206 may include, but is not limited
to a graphic display (e.g., monitors and display screens), a user
interface, and/or an interface used to connect to a printing device
configured to produce hard-copies of the generated results.
[0027] In addition, FIG. 1 also illustrates that memory 108 may be
operatively coupled to processor 102. Memory 108 may be a
non-transitory medium configured to store various types of data.
For example, memory 108 may include one or more memory devices that
comprise secondary storage, read-only memory (ROM), and/or
random-access memory (RAM). The secondary storage is typically
comprised of one or more disk drives, optical drives, solid-state
drives (SSDs), and/or tape drives and is used for non-volatile
storage of data. In certain instances, the secondary storage may be
used to store overflow data if the allocated RAM is not large
enough to hold all working data. The secondary storage may also be
used to store programs that are loaded into the RAM when such
programs are selected for execution. The ROM is used to store
instructions and perhaps data that are read during program
execution. The ROM is a non-volatile memory device that typically
has a small memory capacity relative to the larger memory capacity
of the secondary storage. The RAM is used to store volatile data
and perhaps to store instructions.
[0028] As shown in FIG. 1, the memory 108 may be used to house the
instructions for carrying out various embodiments described herein.
In an embodiment, the memory 108 may comprise a RTM module 110 that
may be accessed and implemented by processor 102. Alternatively,
RTM module 110 may be stored and accessed within memory embedded in
processor 202 (e.g., cache memory). Specifically, the RTM module
110 may receive a variety of inputted information relating to field
parameters for one or more wells and quantify risks associated with
future well completions. The RTM analysis may involve transforming
acquired raw drilling data into a new data base where the actual
run data is summed to produce a new data set where each record is a
planned run.
[0029] In assessing and quantifying risks, the unit of exposure of
risk for well construction may be identified as a "planned run."
The term "planned run" is defined throughout this disclosure as how
drilling operator(s) plan to drill a well. A "planned run" may
constitute a specific hole size, drilled length, dog leg (for
directional drilling), and bottom hole assembly. For example, a
drilling operation for completing a well may on average have about
5 planned runs because of the different hole or piping sizes. An
actual run corresponds to number of instances the drilling
operators has to actually insert and remove a tool string when
drilling a well. Typically, a drilling operation may average more
than 20 actual runs for jobs estimated with about 5 planned run. In
practice more actual runs are compiled than the "planned run"
because of equipment failures and various other situations that may
require the drilling operator to stop drilling and pull the tool
string out of the wellbore.
[0030] Non-productive time extends a drilling period but does not
include or determine all of the actual drilling time. Well planners
may utilize previous field drilling experience and simulations to
estimate how long each planned run will take in real hours assuming
the drilling period has about zero NPT. The estimated time for a
planned run is referenced throughout this disclosure as Below
Rotary Table (BRT) hours. In other words, BRT hours is an estimate
of how long a drilling operator may take to finish drilling that
includes not just pure drilling time, but also tripping and logging
time. NPT extends the drilling period by adding the NPT risk
distribution to the computed BRT hours to determine the total time
for drilling, which includes the time for tool failures. BRT hours
may be the RTM's model output variable even though the RTM analysis
is based on NPT. Uncertainty in BRT determination are addressed by
the RTM analysis and will be discussed later in the disclosure.
Non-productive time for product or service delivery with influenced
(PSDI) may be used as one of the main risk variables in well
drilling operations.
[0031] The BRT distribution may be important regarding tenders from
customers of drilling operators. The tenders may be based on the
time to drill a specific number of wells and not by individual run
parameters. In one embodiment, the BRT distribution may be the
dominant (and most complex) part of the time to well completion
equation. In other embodiments, other elements of a well can be
analyzed and considered using the RTM, such as completion (packer
and liner setting) and artificial lift (pumping).
[0032] In one embodiment, the RTM may comprises four field-based
actuarial variables: hole size (inches), drilling depth (feet
(ft.)), drilled length (ft.), and maximum dog leg. Hole size may be
a direct measurement based on contract specifications and/or the
implementation plan regarding the size of the hole to be drilled
for a well. Drilling depth may be computed as the current maximum
depth of a well, the drilled distance may correspond to the drilled
length, and dog leg, which may be expressed in degrees/100 ft., may
reference the maximum direction change for the planned run. The
field-based actuarial variables may be chosen due to their physical
nature that makes them relatively easy to measure and also as
variables that may be shown to influence NPT risk. Each variable
may be categorized in a manner that reflects both the engineering
exposure and the availability of data to suitably represent the
risk characteristics. For example, Table 1 illustrates an example
embodiment of category definitions that may be used in the RTM
analysis:
TABLE-US-00001 TABLE 1 Hole Size Hole Size Depth Depth Category
Drilled Length Drilled Length Dog Leg Category Cat Range Category
Range Category Category Range Category Label Degrees/100 ft 1
<=6.25 1 <1,000 1 <1,000 1 Short >70, <=180 2
>6.25 <= 8.5 2 1,000-5,000 2 1,000-5,000 2 Inter >40,
<=70 3 >8.5 <= 12.25 3 5,000-10,000 3 5,000-10,000 3 Med
>6, <=40 4 >12.25 4 10,000+ 4 10,000+ 4 Long <=6
Other variables may be added or the category criteria may be
modified depending on the field characteristics of the well site.
The variables and category criteria included in the RTM may be used
to generate NPT event frequency, severity, and risk with suitable
data populations in order to provide the required statistical
significances.
[0033] The RTM's basic drilling information may be computed by
"planned run." In one embodiment, a user or analyst may enter raw
drilling data for the planned BRT hours, the hole size, maximum
depth, drilled length, dog leg category, and the bottom hole
assembly configuration for each planned run. In another embodiment,
the raw drilling data may be imported and/or downloaded from one or
more pre-existing data files. The data entered and/or obtained
could be for a single well with multiple planned runs or runs
planned to be executed for several wells. For example, a project
plan for drilling four off shore wells in the United Kingdom may
appear as shown in Table 2.
TABLE-US-00002 TABLE 2 Hole Max Drilled Max BRT Size Depth Length
Dogleg BHA Products 10 36 100 100 4 Prod # 62 Prod # 29 52.6 26 707
607 4 Prod # 37 Prod # 12 26.2 17.5 1010 403 4 Prod # 71 Prod # 75
Prod # 57 Prod # 60 68.7 12.25 1890 1487 4 Prod # 47 Prod # 8 Prod
# 18 Prod # 32 32.5 9.5 2790 1303 4 Prod # 17 Prod # 76 Prod # 10
Prod # 18 12 36 100 100 4 Prod # 84 Prod # 2 33 26 687 587 4 Prod #
24 Prod # 79 124 17.5 2145 1558 4 Prod # 28 Prod # 63 88 12.25 3154
1596 4 Prod # 64 Prod # 31 158 9.5 4789 3193 4 Prod # 47 Prod # 17
8.6 36 95 95 4 Prod # 53 Prod # 75 44.4 26 654 559 4 Prod # 36 Prod
# 43 71.3 17.5 1765 1206 4 Prod # 80 Prod # 67 22.3 12.25 1876 670
4 Prod # 89 Prod # 79 66.5 9.5 2456 1786 4 Prod # 66 Prod # 20 67.3
9.5 2678 892 4 Prod # 11 Prod # 17 5.9 36 90 90 4 Prod # 89 Prod #
38 76 26 902 812 4 Prod # 37 Prod # 53 144.3 17.5 2534 1722 4 Prod
# 34 Prod # 90 108.4 12.25 3754 2032 4 Prod # 24 Prod # 28 Prod #
71 Prod # 48 57.9 9.5 4665 2633 4 Prod # 90 Prod # 71 Prod # 86
Prod # 32 Prod # 68 167.7 9.5 5432 2799 4 Prod # 24 Prod # 22 Prod
# 81 Prod # 63 Prod # 85
[0034] Subsequently, the RTM and/or user may convert the acquired
raw drilling data into data used in the RTM data base. FIG. 2
illustrates an example on how the raw data is converted and/or
entered into the RTM. As shown in FIG. 2, the RTM or user enters
the category numbers and other data only in the gray sections based
on the acquired raw drilling data. For the planned run entries, the
corresponding labels are automatically completed in the columns
with the blue headers to provide a visual check that the correct
data is being entered. The other columns are used by the RTM to
access the corresponding NPT event frequency and severity
distributions for the entered category sequences.
[0035] As shown in FIG. 2, the RTM parameters or variables include
hole size, depth, drilled length, and bottom hole assembly data
supplied for each planned run. The NPT event frequency may be a
direct function of the bottom hole assembly configuration. Failure
of this assembly may be the originating cause of NPT. For well
drilling and construction, the probability of a failure of the
bottom hole assembly configuration may drive NPT event frequency.
NPT severity may be computed to include modification factors for
hole size, depth, drilled length and maximum dog leg. These factors
are viewed as environmental factors which influence NPT risk. NPT
severity may be a function of the time required to remove a failed
bottom hole assembly and re-insert a new one. From the data, four
statistically significant distributions may be determined, which
will be discussed in more detail below. They are individually
assigned to planned run sequence distributions as a function of
their hole size, depth, and drilled length categories. The maximum
dog leg value may represent a risk modifier as a function of hole
size and dog leg parameters.
[0036] NPT and BRT distributions may be statistically developed via
a Monte Carlo method with the number of trials being supplied by
the user as an input parameter. Persons of ordinary skill in the
art are aware that a Monte Carlo method typically follows that
pattern of determining a domain of possible inputs, generates
inputs randomly from a probability distribution over the domain,
perform a deterministic computation on the inputs, and aggregate
the results. Specifically for NPT and BRT distributions, each
planned run is first tested to check for the occurrence of an NPT
failure event of the run's bottom hole assembly. In one embodiment,
the test outcome may be represented as binary with 0 for no failure
and 1 for a failure. The NPT severity distribution for each planed
run may then be multiplied by the binary frequency function to
compute the planned run NPT risk. The NPT risk value may then be
modified by factors to account for the specific hole size, depth,
drilled length, and maximum dog leg values. The product may be the
risk adjusted NPT risk value for every planned run. After each
planned run has been computed, the NPT risk values may be summed
over the total set of runs under analysis to compute total NPT risk
for one Monte Carlo trial. This method may continue to repeat
itself as many times as specified by the user (e.g., user provides
information on an input screen) and the statistical analysis of the
Monte Carlo process forms the basis of output results. The total
BRT hours may be added to the NPT risk distribution to produce
output statistics that have direct application to well completion
times.
[0037] In one embodiment, to translate the determined NPT risk to
another quantifiable risk, such as financial risk, a matrix may be
defined in the model reference section that introduces the dollar
cost (or loss) per hour of NPT as a function of hole size, depth,
and drilled length. This information is entered by the user based
on the value of the wells being completed. For example, the NPT
time and financial risk may be computed for each planned run and
aggregated over all planned runs. The aggregate NPT value may then
be added to the total BRT hours to produce the total time to well
(or wells) completion distribution.
[0038] In one embodiment, the produced output statistics from the
FTM may not represent or account for specific local operating
conditions. To be applicable to insuring minimum levels of
performance (API) for a specific contract tender, the RTM may be
adjusted to reflect three conditions: (1) The specific country or
field; (2) The distribution of well risks that are land &
off-shore; (3) and the future.
[0039] The RTM, as shown in FIG. 2, allows for about three risk
modifiers to customize the results to these conditions. The
"Location" modifier may be a factor that is computed based on the
analysis of the data shown in the RTM's "Country RM" tab, which is
shown in FIG. 3. Referring to FIG. 3, the user may initially select
the country of interest and subsequently using an input interface
(e.g., click on a run button) to update the plot within the RTM.
The numbers next to the country name represent the percentage of
total records that are from each country. This information may
inform the user about the general amount of data or drilling
activity that has been performed in each country which implicitly
relates to the statistical reliability of the risk frequency,
severity, and risk results.
[0040] The graph's horizontal axis represents the NPT event
frequency measured in terms of number of NPT events per planned
run. The vertical axis is the NPT event severity: average NPT
duration per event in hours. The curved lines on the plot are
iso-risk contours. Along each line, the product of frequency and
severity has the same value. The small boxes illustrate the value
of each iso-risk contour. In FIG. 3, the heavy dotted line may
represent a Non-USA average (well sites located outside for a
drilling operator) over the entire data range for a specified time
range corresponding to the acquired raw data (e.g., from 2006 to
2014-Quarter 3). The points show the NPT frequency, severity, and
risk for each year for the selected country (e.g., United Kingdom
in FIG. 3) for a drilling operator.
[0041] In FIG. 3, each point represents the annual average NPT
performance for the selected country. The general computational
results can be customized to a specific country by taking the ratio
of a particular year's risk value to the overall average: 3.16. For
example, consider the country risk modification factor to replicate
the 2006 results for the United Kingdom. The iso-risk contour for
the 2006 point can be computed by multiplying the 2006 coordinates
together or via approximately by visually interpolating between the
displayed iso-risk contours. In one embodiment, the RTM may display
the precise coordinates by having the user placing the cursor over
the interested point. In FIG. 3, the coordinates are 26.6% and
32.29 which produce the iso-risk contour of 8.56. The 2006 United
Kingdom country risk modifier may be computed as 8.56/3.16 =2.7.
The computed risk modifier may be the value entered on the RTM
model input screen as the Country Risk Modifier shown in FIG. 2.
For 2010 the risk modifier may be about 1.56/3.16 or about 0.5, and
for 2014, the Country Risk Multiplier may be approximately 1.0.
[0042] Trending the information output by the RTM into the future
may include data generated beyond the arithmetic operations
previously discussed above. The plot can provide information on
historical trends in frequency, points consistently moving left or
right, severity--points moving consistently up or down, and risk
points consistently moving diagonally. To generate a future risk
score, the trend (or pattern) of the frequency, severity, and risk
data is extrapolated and applying factors associated with
operational data and having a performance improvement plan in
place. For example, if there are robust training programs in place
or comprehensive root cause activities, then the user can apply
this knowledge to input, for example, that the future NPT will be
25% less than the current year's results. The use of operational
data along with the risk-based time performance results presented
in the specialized graphic format provides a "real time"
data-driven approach to estimate future NPT performance.
[0043] Field specific information inside a country may be extracted
and formatted from one or more database and local operation reports
so that it can be presented in the frequency-severity format used
for the country risk multiplier in the RTM. A process similar to
the country risk multiplier development can be applied to compute a
field risk modifier. The field risk modifier factor may then be
multiplied by the country risk multiplier to adjust the generalized
model results to a specific country and field. In one embodiment,
the general model results may combine land and offshore NPT
performance risk. In some instances the acquired drilling data may
not specify operational type, and thus, may not separate land and
off-shore operations from the location or country in the dataset.
The acquired drilling data may indicate that the majority of
countries are dominated by one type of drilling so in most cases,
and thus, computing the country (and field) multipliers may be
sufficient to adjust the results to actual performance. In another
embodiments, the acquire drilling data may specify land and
off-shore operations and could be computed and applied similar to
the country risk and field risk modifiers as discussed above.
[0044] In one embodiment, the NPT event frequency, severity, and
risk, by year, location, land and offshore as shown in FIG. 4 may
include a trend (or pattern) information to assist the user in
forecasting future NPT performance by operational location. The RTM
allows for the user to adjust the general results based on insights
the users gain by combining the risk-based information with the
user's operational knowledge. The multiplier is set to unity by
default. The user can change this factor by entering their choice
in the land and off-shore cell on the input data screen as shown in
FIG. 2.
[0045] The Time "Trend" cell as shown in FIG. 2 of the input data
screen is a free form element constructed for the user to add
another adjustment factor to the generalized results based on
future drilling operations data for the planned runs listed in the
input data screen. The Time "Trend" cell could also be used for the
field multiplier previously discussed. Entering this value here
helps document the run values for future reference in order to
increase the user's understanding of how to apply the risk model
for forecasting future NPT risk results.
[0046] The generalized results are computed from the stochastic
analysis of each record in the input data screen. The NPT time risk
equation for each record `k` is as shown in equation (1):
NPT Time Risk(h, d, l, t)k=Prob Fail(BHAk)*max{Sev(h, d,
l)*Dogleg(h, r), Severity Limit}*CR*SH*TR (1)
where "h" represents hole size category, "l" represents the drilled
length category, "d" represents the depth category, "t" represents
the maximum dog leg category. Additionally, Prob Fail(BHAk)
represents the probability of failure of the BHA for planned run
`k`; Sev(h, d, l) represents the NPT time severity distribution
(e.g., one of four) assigned to the hole size, depth, and drilled
length categories; and Dogleg(h, r) represents the Dog leg modifier
as a function of hole size and turn rate; Severity Limit represents
the maximum value allowed for a single NPT event for all planned
run severity calculations; CR represents Country risk modifier; SH
represents Land/Off-shore risk modifier; and TR represents Trend
risk modifier.
[0047] The Severity Limit may be entered once at the beginning of
an analysis and applies to all subsequent severity calculations.
The actual severity distributions may be by construction unbounded
on the upper side. Consequently, they can produce NPT downtime
values that may be relatively large for the actual situations being
modeled as judged by the user. For example, suppose that for a
specific planned run, the drilling operator's actual operational
data (and therefore distribution fitted to this data) indicates
that NPT event times could exceed 30 days. However, based on the
logistic data and condition data associated with the specific set
of wells under analysis, a user selection can be made to use a
maximum value for any single run of 5 days. Setting this upper
limit recognizing local logistic and operational environmental data
is another way the user can make data input selections to customize
the RTM to a specific set of conditions. By setting the Severity
Limit to a relatively large number, such as about 9,999, the user
can utilize the full variation of NPT values produced by the
distributions constructed by the underlying NPT severity data.
[0048] In the embodiment, the NPT financial risk may include an
additional term, HourRate(h, d, l), to the NPT Time risk that
converts NPT time to financial costs as shown in equation 2:
NPT Cost Risk(h, d, l, t)k=Prob Fail(BHAk)*max{Sev(h, d,
l)*Dogleg(h, r), Severity Limit}*CR*SH*TR*HourRate(h, d, l) (2)
[0049] In the RTM used for the NPT financial risk analysis, the
cost structure is entered for each hole size, depth, and drilled
length combination. As such, the RTM captures non-productive time
value through drilling characteristics. With additional data, the
RTM is capable of providing different valuation approaches for time
to well completions. The fundamental stochastic variables of the
RTM are the above time and cost equations summed over all planned
runs. The total NPT time and cost risk over "N" planned runs for
Monte Carlo trial "j" can be expressed as:
NPT Time Risk j = k = 1 N NPT Time Risk ( h , d , 1 , t ) k , j ( 3
) NPT Cost Risk j = k = 1 N NPT Cost Risk ( h , d , 1 , t ) k , j (
4 ) ##EQU00001##
[0050] The statistical results of these two variables constitute
the risk management and insurance model variables. Since the data
necessary for the financial analysis formulation is incomplete at
this time, the RTM's insurance analysis will be described only for
the time variable. Since NPT extends the well completion time, we
add the total BRT hours as computed in the model's input screen to
equation (3) to produce the practical variable of interest: the
distribution of time required to complete the wells as described by
the planned runs. The BRT slack percentage is a user selected input
for the uncertainty in this number. The BRT hours plus the
percentage slack amount is plotted on the output risk plots for
comparison of the BRT uncertainty with the NPT uncertainty produced
from the model calculations.
[0051] The BRT uncertainty may not be directly applied to the
insurance analysis. Changes in BRT hours are not necessarily under
the control of drilling operators since geological, climate,
customer requirements, and several other factors can influence this
time. However, drilling operators are typically accountable for
schedule delays due to the failure of their products and
services.
[0052] Insurance Analysis
[0053] In one embodiment, the insurance analysis contains four
parts: (1) well completion time risk without insurance parameters;
(2) well completion time risk with a prescribed time deductible;
(3) well completion time risk with a prescribed time deductible and
a time ceiling; and (4) well completion time risk greater than the
time ceiling. A common output format may be used for all parts to
facilitate a common understanding of the risks associated with each
element. The format as shown in FIG. 5 may be modified depending on
the insurance environment. The insurance model features as shown in
FIG. 5 may be based on the planned runs example depicted in FIG. 2.
The set of 22 runs shown in FIG. 2 are for plans to drill about
four wells. Actual tender analysis could consider significantly
more or less wells and FIG. 2 is used to only to depict the RTM
functionality.
[0054] For the well completion time risk without insurance
parameters section, this section computes the total BRT risk
without any constraints imposed on the BRT distribution. These
results present the total risk exposure that the planned runs
contain. It forms the basis for quantifying the insurance effect of
various deductibles and ceilings (insured floors.) The risk
variable for this analysis is equation (3). FIG. 5 illustrates
running the RTM for the 22 planned runs with 10,000 Monte Carlo
trials.
[0055] In FIG. 5, the average and presented percentile statistics
are common elements of risk management decision-making. The black
line in the plot depicts the entire NPT risk distribution as a
cumulative distribution function. For example, the probability
value associated with a BRT time of 1,540 hours has about a 90%
chance that the total BRT hours will be less than or equal to 1,540
hours or a 10% chance BRT will exceed 1,540 hours. The BRT slack is
added to show the relative uncertainty upper limit of BRT hours
computed to the BRT increase from NPT. The plot may be used to form
the basis for developing insurance parameters that produce the
desired level of risk retained by a drilling operator and the
amount of risk transferred to an insurer. The other legend
components will be addressed below.
[0056] FIG. 6 illustrates the well completion time risk with a
prescribed time deductible. Using data in FIG. 5 as an example,
suppose the customer is willing to accept the risk below the median
value of 1,520 hours. There may be no penalty if the BRT is below
this threshold. If the BRT exceeds about 1,520 hours, then the
service provider may be financially accountable for the time in
excess of 1,520 hours. As illustrated in FIG. 6, applying a 1,520
hour deductible produces the results of the risk the service
provider is assuming under this scenario. FIG. 6 illustrates the
BRT, NPT-based risk distribution that the service provider is
assuming by ensuring the total BRT for drilling the 4 wells with
the 22 planned runs will be less than or equal to about 1,520
hours. FIG. 7 illustrates an amended distribution based on FIG. 5
that shows the 1,520 hours BRT deductible from FIG. 6.
[0057] FIG. 8 illustrates the well completion time risk with a
prescribed time deductible and a time ceiling. The plot in FIG. 8
shows that most of the risk is actually for BRT hours greater than
1,520 hours and this where insurance applies. To keep the service
provider's risk exposure to a manageable level, an insured floor
may be placed at 1,540 hours. From the plot, there may be about a
10% chance that the total non-productive time for drilling the 4
wells would exceed this value. Adding the insured floor or the
service provider's risk assumed ceiling produces the plot shown in
FIG. 8 and statistical results for the risk exposure they have
retained between 1,520 and 1,540 hours. FIG. 9 illustrates an
updated distribution based on FIG. 5 that show the risk exposure
the drilling operator is retaining. In FIG. 9, this is represented
between the blue and red solid vertical lines. The risk exposure is
transferred to an insurer, which is to the right of the red
vertical line.
[0058] FIG. 10 illustrates when the well completion time risk is
greater than the time ceiling. The last portion of the analysis
displays the risk exposure transferred to an insurer in terms of
insurance. The results show that there may be a low probability of
occurrence associated with experiencing NPT results in excess of
total BRT hours of about 1,540. However, the potential for
relatively large NPT (and therefore relatively large BRT hours)
results does exist. Mitigating these low frequency-high severity
events may be the purpose of the insurance.
[0059] Programming and/or loading executable instructions onto
memory 108 and processor 102 in order to transform the RTM
processing system 100 into a particular machine or apparatus that
utilizes the RTM is well known in the art. For example, the RTM
processing system 100 may be implemented using macros within
Microsoft Excel.RTM.. Implementing instructions, real-time
monitoring, and other functions by loading executable software into
a computer can be converted to a hardware implementation by
well-known design rules. For example, decisions between
implementing a concept in software versus hardware may depend on a
number of design choices that include stability of the design and
numbers of units to be produced and issues involved in translating
from the software domain to the hardware domain. Often a design may
be developed and tested in a software form and subsequently
transformed, by well-known design rules, to an equivalent hardware
implementation in an ASIC or application specific hardware that
hardwires the instructions of the software. In the same manner as a
machine controlled by a new ASIC is a particular machine or
apparatus, likewise a computer that has been programmed and/or
loaded with executable instructions may be viewed as a particular
machine or apparatus.
[0060] At least one embodiment is disclosed and variations,
combinations, and/or modifications of the embodiment(s) and/or
features of the embodiment(s) made by a person having ordinary
skill in the art are within the scope of the disclosure.
Alternative embodiments that result from combining, integrating,
and/or omitting features of the embodiment(s) are also within the
scope of the disclosure. Where numerical ranges or limitations are
expressly stated, such express ranges or limitations may be
understood to include iterative ranges or limitations of like
magnitude falling within the expressly stated ranges or limitations
(e.g., from about 1 to about 10 includes, 2, 3, 4, etc.; greater
than 0.10 includes 0.11, 0.12, 0.13, etc.). The use of the term
"about" means .+-.10% of the subsequent number, unless otherwise
stated.
[0061] Use of the term "optionally" with respect to any element of
a claim means that the element is required, or alternatively, the
element is not required, both alternatives being within the scope
of the claim. Use of broader terms such as comprises, includes, and
having may be understood to provide support for narrower terms such
as consisting of, consisting essentially of, and comprised
substantially of Accordingly, the scope of protection is not
limited by the description set out above but is defined by the
claims that follow, that scope including all equivalents of the
subject matter of the claims. Each and every claim is incorporated
as further disclosure into the specification and the claims are
embodiment(s) of the present disclosure.
[0062] While several embodiments have been provided in the present
disclosure, it may be understood that the disclosed embodiments
might be embodied in many other specific forms without departing
from the spirit or scope of the present disclosure. The present
examples are to be considered as illustrative and not restrictive,
and the intention is not to be limited to the details given herein.
For example, the various elements or components may be combined or
integrated in another system or certain features may be omitted, or
not implemented.
[0063] In addition, the various embodiments described and
illustrated in the various embodiments as discrete or separate may
be combined or integrated with other systems, modules, techniques,
or methods without departing from the scope of the present
disclosure. Other items shown or discussed as coupled or directly
coupled or communicating with each other may be indirectly coupled
or communicating through some interface, device, or intermediate
component whether electrically, mechanically, or otherwise. Other
examples of changes, substitutions, and alterations are
ascertainable by one skilled in the art and may be made without
departing from the spirit and scope disclosed herein.
* * * * *