spe 98010_new method for production data analysis to identify new opportunities in mature fields
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Copyright 2005, Society of Petroleum Engineers
This paper was prepared for presentation at the 2005 SPE Eastern Regional Meeting held inMorgantown, W.V., 1416 September 2005.
This paper was selected for presentation by an SPE Program Committee following review ofinformation contained in a proposal submitted by the author(s). Contents of the paper, aspresented, have not been reviewed by the Society of Petroleum Engineers and are subject tocorrection by the author(s). The material, as presented, does not necessarily reflect anyposition of the Society of Petroleum Engineers, its officers, or members. Papers presented atSPE meetings are subject to publication review by Editorial Committees of the Society ofPetroleum Engineers. Electronic reproduction, distribution, or storage of any part of this paperfor commercial purposes without the written consent of the Society of Petroleum Engineers isprohibited. Permission to reproduce in print is restricted to a proposal of not more than 300words; illustrations may not be copied. The proposal must contain conspicuousacknowledgment of where and by whom the paper was presented. Write Librarian, SPE, P.O.Box 833836, Richardson, TX 75083-3836, U.S.A., fax 01-972-952-9435.
ABSTRACT
Most of the mature fields in the United States have been
producing for many years. Production in these fields started ata time when reservoir characterization was not a priority;
therefore they lack data that can help in reservoir
characterization. On the other hand to re-vitalize these fields
in a time that price of hydrocarbon is high, requires certaindegree of reservoir characterization in order to identify
locations with potentials of economical production. The most
common type of data that may be found in many of the maturefields is production data. This is due to the fact that usually
production data is recorded as a regulatory obligation orsimply because it was needed to perform economic analysis.
Using production data as a source for making decisions havebeen on the petroleum engineers agenda for many years and
several methods have been developed for accomplishing this
task. There are three major shortcomings related to the efforts
that focus on production data analysis. The first one has to dowith the fact that due to the nature of production data its
analysis is quite subjective. Even when certain techniques
show promise in deducing valuable information from
production data, the issue of subjectivity remains intact.Furthermore, as the second shortcoming, existing production
data analysis techniques usually address individual wells and
therefore do not undertake the entire field or the reservoir as acoherent system.
The third short-coming is the lack of a user friendly software
product that can perform production data analysis withminimum subjectivity and reasonable repeatability while
addressing the entire field (reservoir) instead of autonomous,
disjointed wells. It is a well known fact that techniques such as
decline curve analysis and type curve matching address
individual wells (or sometime groups of wells without
geographic resolution) and are highly subjective.
In this paper a new methodology is introduced that attempts toaddress the first and the second, i.e. unify a comprehensive
production data analysis with reduced subjectivity while
addressing the entire reservoir with reasonable geographic
resolution. The geographic mapping of the depletion or
remaining reserves can assists engineers in making informeddecision on where to drill or which well to remediate. The
third shortcoming will be addressed in a separate paper wherea software product is introduced that would perform the
analysis with minimum user interaction.
The techniques introduced here are statistical in nature andfocuses on intelligent systems to analyze production data. This
methodology integrates conventional production data analysis
techniques such as decline curve analysis, type curve matching
and single well radial simulation model, with new techniques
developed based on intelligent systems (one or more oftechniques such as neural networks, genetic algorithms and
fuzzy logic) in order to map fluid flow in the reservoir as a
function of time. A set of two dimensional maps are generatedto identify the relative reservoir quality and three dimensiona
maps that track the sweet spots in the field with time in order
to identify the most appropriate locations that may still have
reserves to be produced. This methodology can play animportant role in identifying new opportunities in mature
fields.
In this paper the methodology is introduced and its applicationto a field in the mid-continent is demonstrated.
INTRODUCTIONTechniques of production data analysis (PDA) have improved
significantly over the past several years. These techniques areused to provide information on reservoir permeability, fracture
length, fracture conductivity, well drainage area, original gasin place (OGIP), estimated ultimate recovery (EUR) and skin
Although there are many available methods identified, there is
no one clear method that always yields the most reliable
answer1. Furthermore, tools that make these techniques
available to the engineers are not readily available. The goal ofthis study is to develop a comprehensive tool for production
data analysis and make it available for use by industry.
Production data analysis techniques started systematically
SPE 98010
New Method for Production Data Analysis to Identify New Opportunities in MatureFields: Methodology and ApplicationMohaghegh, S. D., Gaskari, R. and Jalali, J., West Virginia University
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2 New Method for Production Data Analysis to Identify New Opportunities in Mature Fields SPE 98010
with a method presented by Arps in the 1950s1. Arps decline
analysis is still being used because of its simplicity, and since
its an empirical method, it doesnt require any reservoir orwell parameters. Fetkovich proposed a set of equations
described by an exponent, b, having a range between 0 and 12.
Arps equation is based on empirical relationships of rate vs.
time for oil wells and is shown below3:
( )( )bi
i
tbD
qtq
1
1+
= (1)
In this relationship, b = 0 and b = 1 represent exponential and
harmonic decline, respectively. Any value of bbetween 0 and
1 represents a hyperbolic decline. Although Arps equation is
only for pseudo-steady state conditions, it has been often
misused for oil and gas wells whose flow regimes are in atransient state.
The Fetkovich methodology analyzes oil wells producing at a
constant pressure. He combined early time, analyticaltransient solutions with Arps equations for the later time,
pseudo-steady state solutions. The Fetkovich method likeArps equation, calculates expected ultimate recovery.
Carters gas system type curves were published in 19853.
Carter used a variable identifying the magnitude of the
pressure drawdown in gas wells. A curve with a value of 1corresponds to b=0 in Fetkovich liquid decline curves and
represents a liquid system curve with an exponential decline.
Curves with =0.5 and 0.75 are for gas wells with an
increasing magnitude of pressure drawdown.
Agarwal-Gardner also introduced a method for productiondata analysis in 1998. This technique combines decline curveand type curve concepts for estimating reserves and other
reservoir parameters for oil and gas wells using production
data. 3
Other methods were introduced by Palacio & Blasingame, andAgarwal-Gardner, which provide information on gas in place,
permeability, and skin.
There are also modern analytical methods that do not use typecurves. One of these methods is flowing material balance.
This technique provides the hydrocarbons in place usingproduction rate and flowing pressure data from a reservoir
under volumetric depletion. 3
METHODOLOGY
In this section a step by step process for Intelligent ProductionData Analysis (IPDA) is introduced. Several items must be
mentioned in order to put the degree of reliability of these
techniques in perspective. Please keep in mind that IPDA isdeveloped for situations that only production data is available.
Therefore, in situations that one has access to other data such
as logs, core analysis, pressure tests, geologic models, etc. onemight choose to use other well established techniques.
Nevertheless, it will be demonstrated that as more data such a
those mentioned above are available, one may use them to
increase the accuracy and the reliability of the methodologybeing introduced here.
Furthermore, as it will be explained in this paper, IPDA, at
this time, may come across as a lengthy procedure. This is due
to the fact that IPDA is an iterative and essentially anoptimization technique. The next step in our efforts is the
development of an automatic or semi-automatic procedure thawill make this process much faster and easier to implement. In
the new development the goal is to reduce the user interaction
to a minimum. Figure 1 is a flow chart that describes the
methodology used in this process. As shown in the chart, thereare four distinct and inter-related parts to this process, namely
decline curve analysis, type curve matching, single-wel
reservoir simulation (history matching) and finally relative
reservoir quality indexing and mapping that includes three
dimensional mapping of remaining reserves.
In a nutshell, IPDA is a two step process. In the first step theidea is to simultaneously, interactively and iteratively perform
decline curve analysis, type curve matching and history
matching on the production data of a particular well in the
field until convergence is achieved to a unified set of reservoir
characterizations. Given the fact that each of these techniquesare quite subjective by nature, by letting each one technique to
guide and keep an eye on the other two during the analysis, the
degree of confidence and reliability on the results as well as
repeatability of the analysis will increase.
The second step is to use the well-based results from the firs
step and super impose them on the field as a whole anddevelop two and three dimensional maps of reservoir quality
and remaining reserves. In the following sections each part othis process will be explained.
Decline Curve Analysis
Decline curve analysis is the first step in the process. The
production data is plotted on semi-log scale and decline curve
is fitted. If one uses hyperbolic decline, then the outcome ofthe decline curve analysis would be qi (initial flow rate)
Di (initial decline rate) and b (hyperbolic exponent)
Figure 2 is an example of decline curve analysis performed ona well located in the Golden Trend Fields of Oklahoma.
It is a good idea to plot both production rate and cumulative
production versus time simultaneously and try to match theproduction rate while keeping an eye on the cumulativeproduction. This usually helps in getting a reasonably good
match. Once the match is completed the result would be a set
of decline curve characteristics as mentioned above. Based on
the decline curve characteristics that you have identified inthis step you can easily calculate Estimated Ultimate Recovery
(EUR) for a certain number of years, say 30 years. The 30
year EUR for the well in Figure 2 is calculated to be 1,795MMSCF.
Type Curve Matching
Two things are important in selecting the type curves for this
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SPE 98011 Mohaghegh, Gaskari and Jalali 3
step of the analysis. First, the type curves must be for rate and
not pressure, and second, it would be great if it could provide
some commonality with the decline curve results, such that itwould use some of the results of the decline curve. Something
that would weave the two (the decline curve analysis and the
type curve matching) together. This is essential since the
procedure would call for an iterative process.
In this case a set of type curves for low permeability reservoirs
with hydraulically fractured wells4were used. Figure 3 showsthe type curve match achieved for the same well shown in
Figure 2. The link between this set of type curves and the
decline curve analysis are the hyperbolic exponent b. In
Figure 3 it is shown that the type curves for a particular bvalue are generated and used for matching the production rates
from this well. The b value used for this well (b=0.9) was
the same that was calculated during the decline curve analysis.
This is important since a new set of decline curves can be
developed for any values of b. Figure 4 shows theproduction data from this well plotted against type curves that
have been generated for b values 0.1 (on the left) and 1.5(on the right).
Once the match is accomplished, the results from type curve
matching is a set of parameters such as permeability, fracture
half length and drainage area. Results of these parametersappear on the top left corner of the type curve screen as show
in Figures 3 and 4. On the top right corner of the screen the
EUR (again for 30 years) is calculated and shown. The EUR
from the decline curve analysis was calculated to be 1,795MMSCF while the EUR from the type curve matching is
calculate to be 1,800 MMSCF as shown in Figure 3. The EUR
calculated from the decline curve analysis is used as acontrolling factor for the match of the type curve. This allows
us to develop more confidence on the type curve matching andhave a bit more faith on the values calculated for permeability,
fracture half-length and the drainage area. The EUR values
from the two procedures (type curve and decline curve) areused in order to finalize the match in both cases. As we
mentioned before this is an iterative procedure and sometimes
it may take several iterations before the decline curve analysisand the type curve match would agree with one another.
One important issue that needs to be mentioned at this point isthat the type curve matching process requires knowledge
about a set of parameters. These parameters are used during
the calculation of permeability, fracture half length, drainage
area and EUR. These parameters are listed below:
Initial reservoir pressure; Average reservoir temperature; Gas specific gravity; Isotropicity (kx/kyratio); Drainage shape factor (L/W ratio); Average porosity; Average pay thickness; Average gas saturation;
Average flowing bottom-hole pressure.
Most of the parameters above can be (and usually are) guessed
within a particular range that is acceptable for a particular
field. Usually the initial reservoir pressure for a field or
formation is known with reasonable range or it can beassumed based on formation depth. Formation depth can also
be a good indication of average reservoir temperature. Gas
specific gravity can be easily calculated based on the assumed
average initial pressure and reservoir temperature. In most of
our calculations we assume that the reservoir is isotropicmeaning that the kx/kyratio is equal to 1. The drainage shape
factor is also assumed to be 1 meaning that we are assuming asquare drainage area. Average porosity, thickness and gas
saturation can be calculated for each well from logs, if they
are available. If they are not, then an average value for the
entire field can be assumed. This method allows for bettermatches and results in higher confidence level if wells in the
field have logs. By having access to logs; porosity, thickness
and saturation can be calculated and used individually for each
well during the analysis. If and when such logs are no
available or prove to be too expensive to analyze (whichseems to be the case in many fields in the United States) then
the procedure allows the user to input an average value (as thebest guess) for all wells. In the bottom right corner of Figure 3
you can see two buttons. The button on the top lets the user
input values for the above parameters that will be used as
default for the entire field and the second button will let the
user to input and over-write the default values for any specificwell.
Single-Well Reservoir Simulation
This step of the analysis calls for history matching theproduction data using a single-well, radial reservoir simulator
Reservoir characterization data that is the result of type curve
matching is used as the starting point for the history matchingprocess and the objective is to match the production data of a
particular well. History matching is not a simple and straighforward procedure. Care must be taken in order to preserve the
integrity of the match, in other words, the match that results
from this procedure must make physical sense. One of themain reasons for performing the history matching in
conjunction with the type curve matching and the decline
curve analysis as an iterative process is to preserve theintegrity of the entire process. Many times the results of the
history matching will force us to go back and re-evaluate our
decline curve results and the type curve match. This rigorousiterative process is the key to the successful completion of this
process.
This is performed for all the wells in the field. In most cases amatch should be possible within reasonable ranges of thereservoir characteristics that were used as the starting poin
(result of iterative type curve matching and decline curve
analysis). This may prove to be a long process. The goal is to
automate this process with minimal user interaction.
Once a match is accomplished within a reasonable range of al
the reservoir characteristics, the ground work has beenestablished for another important step in the analysis, namely
Monte Carlo Simulation. Since now we have (most probably)
diverged from the reservoir characteristics that we started
with, it is reasonable to expect that we have converged on a
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range of values for each of the parameters rather than a single
value. In order to get a more realistic look at the capabilities of
a well and its potential future production a Monte Carlosimulation is performed at this point. During the Monte Carlo
simulation each of the parameters that play a role during the
simulation process is represented by a probability distribution
function (instead of a crisp, certain number) with
characteristics that have been identified during the entireintegrated analysis, i.e. decline curve analysis, type curve
matching and history matching using reservoir simulator.
The result of the Monte Carlo simulation is a probability
distribution of the 30 year EUR. If we have done everything
properly, it is expected that the EUR that was calculatedduring the decline curve analysis and the type curve matching
process for a given well would fall within the probability
distribution that has been calculated as the result of the Monte
Carlo simulation. As the values of these EURs get closer to
higher probability values, our confidence on the accuracy ofthe ranges (of the reservoir characteristics) that were used in
the Monte Carlo simulation will increase.
Mapping of the Reservoir Quality
Once the integrated production data matching is completed
and a set of reservoir characteristics are identified, it is time to
produce some maps that would ultimately help operators inidentifying the sweet spots in the field. The two and three
dimensional maps include relative reservoir quality indices as
a function of time as well as some reservoir characteristics and
map of the remaining reserve as a function of time.
Let us look at an example to help demonstrate the process.
First step is to identify the parameter that would be used as thekey for the reservoir partitioning. Figure 5 shows that Gas
has been selected as the fluid of choice and from among theProduction Indicators (PIs) that have been calculated First 3
Months of Production is selected as the output as well as an
attribute. The PI identified as the output (you may only haveone output but multiple attributes) is used as the key to
partitioning the field while attributes are those PIs that their
average values are calculated for each partition and are used asa guide to fine tune the partitioning process. Once these
selections are made, the next step is to partition the field into
different reservoir qualities based on the average values of theoutput for each partition. This is shown in Figure 6.
As displayed in this figure, 85 wells in this field have been
analyzed for this study. Fuzzy pattern recognition5
isperformed on the latitude and longitude as a function of theselected output and then super imposed on one another to
create the partitions as shown in Figure 6. The numbers on the
map indicate the relative reservoir quality with 1 being the
highest quality. In this representation, the relative quality ofthe reservoir rock in terms of hydrocarbon productivity
decreases with higher numbers of RRQI.
The values corresponding to the partitions in Figure 6 are
shown in Table 1. In this table the 85 wells in the field are
divided into five different categories identified as Relative
Reservoir Quality Indices of 1 through 5. The average value of
the PI (First 3 Months of Production) decreases as the index o
the relative reservoir quality increases. This confirms our
partitioning practices. Figure 7 displays a three dimensionaview of the reservoir quality with smooth transitions from one
reservoir quality to the next.
If the output of the problem is changed from a PI representing
early life of the field to one representing a later time in the lifeof the field, then the difference in these figures may represent
depletion in the reservoir or fluid movement. Furthermoreeach of the reservoir characteristics that are generated as the
result of the analysis that was mentioned in the previous
sections can be mapped throughout the field. This will provide
a good visual of the status of the field at different times in itslife and can provide insight on the locations that have high
production potentials for future development. More details on
these mapping procedures and how remaining reserve are
calculated on a field-wide basis is provided in the next section
RESULTS & DISCUSSIONS
The methodology described in this paper was applied toproduction data from 85 wells producing in the Golden Trend
fields of Oklahoma. The only data used to perform the
analysis shown here are the production data that are publicly
available; therefore, all these analyses can be performed on
any field throughout the United States and Canada. This mayprove to be a valuable tool for independent asset valuation
prior to any acquisition.
The first step in the process is performing three different kindsof analysis techniques on each well simultaneously in an
iterative manner in order to converge to one unified set of
matches and EURs. The three analysis techniques are declinecurve analysis, type curve matching and history matching
using a single-well radial reservoir simulation. Figures 8through 12 show results of two analyses, reached
simultaneously (iteratively) on five different wells in the filedThe graph on the left in each figure is the decline curve
analysis and the graph on the right is the type curve match.
The EURs are quite comparable in each of the figures. Table 3shows the parameters that were calculated in this analysis for
the five wells shown in Figures 8 through 12. As mentioned
before, the hyperbolic exponent found in the decline curveanalysis is used to identify the set of type curves that should
be used during the matching process. Then the EUR from the
type curve matching is compared with the EUR calculated
from the decline curve analysis. If the difference is greaterthan a certain threshold, then the characteristics of the declinecurve is modified and the new bvalue is used in the type curve
matching resulting in a new EUR. This process is continued
until the hyperbolic exponent provides the closest EUR
between type curve matching and the decline curve analysis.
Results shown in Table 3 are the final results that have been
achieved upon convergence when all three methods (declinecurve analysis, type curve matching and history matching
using a single-well radial simulation model) are performed
simultaneously and convergence is achieved. Figures 13 and
14 show the history match results achieved for two of the
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SPE 98011 Mohaghegh, Gaskari and Jalali 5
wells in the database. Figures 15 an 16 are results of Monte
Carlo simulation performed on the two wells shown in Figures
13 and 14. During the Monte Carlo simulation each of theparameters that were used and calculated during the type curve
matching and history matching process is assigned a
probability distribution function and the history matched
model is run for hundreds of times each time calculating the
30 year EUR. The 30 year EUR calculated during the declinecurve analysis and the type curve matching are also shown in
Figures 15 and 16. As expected (since this was an iterativeprocedure to converge to a common set of characteristics that
would provide a common set of results) the 30 year EUR
values from the other two techniques fall in the high frequency
areas of the probability distribution function of the 30 yearEUR obtained from Monte Carlo simulation.
Once the iterative process of matching the production data of
each well individually using three simultaneous process of
decline curve analysis, type curve matching and historymatching is completed, several production indicators (that are
simply statistical measures of a wells production) arecalculated such as:
Best 3 Months of production Best 6 Months of production Best 9 Months of production Best 12 Months of production First 3 Months of production First 6 Months of production First 9 Months of production First year cumulative production Three year cumulative production Five year cumulative production Ten year cumulative production
Other indicators are calculated as a result of decline curve
analysis and type curve matching such as:
Decline curve related indicators:
o Initial flow rateo Initial decline rateo Hyperbolic exponento Estimated Ultimate Recover
Type curve matching indicators
o Permeabilityo Fracture half length
o Drainage areao Permeability * Thickness (kh)o Estimated Ultimate Recovery
All the above characteristics are now available for all the wells
in the field, in our case for 85 wells. Figures 17 through 20show the change of the relative reservoir quality index with
time. In Figure 17 the Relative Reservoir Quality Index
(RRQI) is shown as a result of First 3 months of production.As mentioned before RRQI 1 indicates the best location in the
field followed by numbers from 2 to 5. Please keep in mind
that all these numbers and quality indicators are relativeas the
name points out.
The RRQIs are identified based on partitioning the fuzzy
pattern recognition performed on latitude and longitude as
shown in the figure. The red and blue lines in the fuzzy patternrecognition part of the figure are the partitioning agents that
can be moved up and down. Each time the partition agents are
moved the partitioning of the field is renewed and the average
Production Indicator (in the case of Figure 17, the First 3
Months cumulative production) for each of the partitions arerecalculated. This process can be used as a guide for
identification of the correct partitions in the field.
Comparing Figures 17 and 18 one can see the migration of
several wells from RRQI 1 to RRQI 2 and from RRQI 2
to RRQI 3. Based on the reservoir characteristics (andsometimes well related issues such as damage) several wells
do not produce as well as others and exhibit differen
behaviors. If it is a well related problem, these would be
isolated wells and in a long term analysis can be identified by
other means such as a process called Intelligent CandidateSelection Analysis6-8 (ICSA). Integration of ICSA with IPDA
is the topic of a future paper. This integration allows theidentification of wells that are in need of remedial processes
and those that cannot be rescued. This integration would unify
the field development strategies as a combination of wel
remedial operations and new drilling opportunities.
Table 3 shows the statistical change of the RRQIs as the
analysis move from first 3 month of production to first 9
months of production. Following items are notable in this
table. The value of average cumulative production in bothcases (3 and 9 months of production) decreases with an
increase in RRQI (please remember that increase in RRQI
indicates a relative decrease in the reservoir quality)Furthermore, the number of wells that are located in RRQI 1
2, and 3 are different in two cases, identifying the migration owells from one RRQI to another which can be an indication o
depletion.
In Figure 19, First 3 years of production is selected as the
output for partitioning. Changes in this figure as compared
with Figure 18 show the movement of RRQIs in the field thatis a clear indication of depletion and fluid movement in the
reservoir. Furthermore, Figure 20 shows the 30 year EUR
forecast for this field if no new wells are drilled. This can helpoperators to identify the locations in the field that would
present good candidate for infill drilling due to the remaining
gas in place.
Similar maps can be generated for other parameters that havebeen calculated as a result of decline curve and type curve
matching analyses. Figure 21 shows the two dimensional map
based on reservoir permeability and Figure 22 is the three
dimensional version of permeability distribution in the filed.
The next step is to track the fluid movement in the reservoir
In case of primary recovery this can simply mean depletion ordetection of remaining hydrocarbon in place. Wells are drilled
at different times and therefore may be in production for
different length of time. This is shown in Figure 23 where
example for several wells is presented. What we are interested
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6 New Method for Production Data Analysis to Identify New Opportunities in Mature Fields SPE 98010
in is the map of the remaining fluid in the reservoir. This can
help operators (in conjunction with other maps such as those
presented in Figure 17 through 24) to identify the best locationfor drilling new wells or to make decision on the wells that
need to be stimulated or re-stimulated.
As shown in Figure 23 we are going to identify the remaining
gas in place by calculating the 30 Year EUR for each well andthen subtracting the amount of gas that they have produced up
to a certain point in time. This way we can identify theremaining gas in place as of any date of interest. Figure 23
shows the dates at which we would perform the above
calculation. Identification of remaining gas in place based on
the current cumulative production and decline curve forecastis not new. New items that are introduced here are the
identification of fuzzy patterns that evolves in the field and
three dimensional mapping of the remaining hydrocarbon in
place.
Such mapping is shown in Figures 24 and 25. In Figure 24 the
remaining gas in place as of year 2005 is shown and Figure 25is the same map after 15 more years of production. The
difference between these two figures shows the depletion in
the reservoir and identifies the parts of the field that still have
potential for more recovery. Figure 26 shows the reservoir
depletion in 5 year intervals by mapping remaining gas inplace as of January of 2005, 2010, 2015 and 2020.
An important note is that using this technique, new wells can
be virtually drilled in a particular location and their effects(production) on the remaining reserves can be observed. In
other words many What If scenarios can be played out in
order to make the best possible decision on the location of thenext wells.
Application of these techniques to water flooding (and other
enhanced recovery) operation may prove to be very useful in
identification of flood fronts as a function of injection andproduction data when reservoir characteristics of the formation
is complex and not very well understood.
CONCLUSIONS
A new technique for field-wide production data analysis has
been introduced in this paper. This technique takes advantageof an iterative procedure to unify the matching of production
data for each well using three independent techniques, namely
decline curve analysis, type curve matching and history
matching using a single-well radial reservoir simulationmodel. Working with the above three techniquessimultaneously and iteratively a set of reservoir characteristics
emerge that approximately satisfies all three production
matches. In absence of any reservoir characterization studies,
this technique resolves the subjectivity associated with each ofthese techniques by using each one of them as a controlling
factor for the other two in an iterative fashion.
The techniques presented in this paper also provide means for
mapping reservoir quality throughout the field using the
results of the production data matching mentioned above. The
result is a set of two and three dimensional maps that identifies
the potential places for drilling new wells. This technique uses
state of the art in intelligent systems in order to discoverpatterns in the production data using all the wells in the field
simultaneously. This is an important recognizing factor for
this technique as compared to others in the literature tha
allows mapping of the reservoir characterization and
remaining reserves in the field in two and three dimensionagraphs.
Implementation of this technique in its present form for a field
with tens or hundreds of wells is a relatively lengthy process
The research and development team is currently working to
automate this process that would require minimum userinteraction during the iterative process. A new paper
introducing the automated procedure will be presented as soon
as the development process is completed.
REFERENCES1. A Systematic and Comprehensive Methodology for
Advanced Analysis of Production Data, L. Matter,D. M. Anderson, Fekete Associates Inc., SPE 84472,
2003.
2. Useful Concepts for Decline-Curve Forecasting,Reserve Estimation, and Analysis, M.J. Fetkovich,
E.J. Fetkovich, and M.D. Fetkovich, PhillipsPetroleum Co., SPE Reservoir Engineering, February
1996.
3. Analyzing Well Production Data Using CombinedType Curve and Decline Curve Analysis Concepts,Ram G. Agarwal, David C. Gardner, Stanley W.
Kleinsteiber, and Del D. Fussell, Amoco Exploration
and Production Co., SPE 49222, 19984. Advanced Type Curve Analysis for Low
Permeability Gas Reservoirs, Cox, Kuuskraa andHansen. SPE 35595, 1998.
5. Fuzzy Clustering Models and Applications, Studiesin Fuzziness and Soft Computing, Volume 9. M
Sato, Y. Sato, L.C. Jain. Physica-Verlag, Heidelberg
New York.
6. "Benchmarking of Restimulation Candidate SelectionTechniques in Layered, Tight Gas Sand Formations
Using Reservoir Simulation", Reeves, Bastian
Spivey, Flumerfelt, Mohaghegh, and Koperna, SPE63096, 2000.
7. "Development of an Intelligent Systems Approach toRestimulation Candidate Selection", Mohaghegh
Reeves, and Hill, SPE 59767, 2000.8. "Restimulation Technology for Tight Gas Sand
Wells", Reeves, Hill, Hopkins, Conway, Tiner, and
Mohaghegh, SPE 56482, 1999.
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Table 1.Average value of First 3 Months PI for each of the partitions.
RRQI No. Wells % of WellsFirst 3 Months of
Production (MSCF)
1 15 17.65 89,684.5
2 22 25.88 74,832.9
3 9 10.59 51,179.0
4 22 25.88 30,801.4
5 17 20.00 22,141.6
TOTAL 85 100
Figure 1.Flow chart of Intelligent Production Data Analysis IPDA.
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Figure 2.Decline curve analysis of well C-ANY #1-4
Figure 3.Type curve matching of well C-ANY #1-4
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Figure 4.Type curve matching of well C-ANY #1-4 with b values 0.1 and 1.5.
Figure 5.Selecting output and attributes for partitioning the field into zones with different quality.
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Figure 6.Partitioning the field into zones with different quality based on First 3 Months PI.
Table 2.Results of field partitioning for 3 and 9 months production.
Number of Wells Average Cumulative Production
RRQI 3 Months 9 Months 3 Months 9 Months
1 15 9 89,684 270,355
2 22 21 74,832 198,686
3 9 16 51,179 152,250
4 22 22 30,801 100,389
5 17 17 22,141 67,964
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Figure 7.Three dimensional view of the relative reservoir quality partitioning of the field based on First 3 Months PI.
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Figure 8.Simultaneous decline curve analysis and type curve matching of well C-LL#1-28.
Figure 9.Simultaneous decline curve analysis and type curve matching of well C-WBY #1-1.
Figure 10.Simultaneous decline curve analysis and type curve matching of well C-AN #2-27.
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Figure 11.Simultaneous decline curve analysis and type curve matching of well N-CERO 3-3.
Figure 12.Simultaneous decline curve analysis and type curve matching of well T-dle A#1.
Table 3.Results of simultaneous decline curve analysis and type curve matching
shown for five wells in the field.Decline Curve Analysis Type Curve Matching
Well NameQi
(MSCF)Di b
EUR(MMSCF)
K(md)
Xf(ft)
A(ac)
EUR(MMSCF)
C-LL#1-28 40,500 0.051 0.51 1,449.3 3.45 35.8 73.4 1,455.5
C-WBY #1-1 28,130 0.057 1.15 1,709.6 2.09 28.3 48.1 1,671.6
C-AN #2-27 47,830 0.248 1.30 1,314.8 3.78 29.3 17.7 1,328.3
N-CERO 3-3 6,333 0.026 1.80 800.8 0.18 27.2 6.8 809.6
T-DLE A#1 10,894 0.268 1.42 318.9 0.75 8.6 4.2 318.5
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Figure 13.History match results for Well C-ER #1-16.
Figure 14.History match results for Well C-VA #1-34.
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Figure 15.Results of Monte Carlo Simulation study for Well C-ER #1-16.
Figure 16.Results of Monte Carlo Simulation study for Well C-VA #1-34.
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Figure 17.Relative Reservoir Quality Index based on first three months of cumulative production.
Figure 18.Relative Reservoir Quality Index based on first nine months of cumulative production.
Fuzzy Pattern Recognition
FuzzyPatternRecognition
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Figure 19.Relative Reservoir Quality Index based on first three years of cumulative production.
Figure 20.Relative Reservoir Quality Index based on 30 year EUR forecast.
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Figure 21.Distribution of reservoir permeability in the field based on IPDAs integrated technique.
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Figure 22.Three dimensional distribution of reservoir permeability in the field based on IPDAs integrated technique.
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Figure 23.Production schedule for different wells, corresponding production forecast and dates for calculating remaining gas in place
Figure 24.Remaining gas in place as of January 2005.
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Figure 25.Remaining gas in place as of January 2020.
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Figure 26.Remaining gas in place as a function of time.
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