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New Method for Production Data Analysis to Identify New Opportunities in Mature Fields:Methodology & Application

Shahab D. MohagheghRazi Gaskari, Jalal JalaliWest Virginia University

SPE 98010

SPE Eastern Regional Conference, Morgantown, West Virginia, September 2005

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SPE 98010

Shahab D. Mohaghegh

IntroductionMost mature fields were developed at a time when reservoir characterization was not a priority.The most common data that an engineer can count on for analysis is the “PRODUCTION DATA”.

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SPE 98010

Shahab D. Mohaghegh

IntroductionState-of-the-art in production data analysis:

ArpsFetkovichCarterWattenbargerBlasingameAgrawalHornHagoort

Decline curve analysis, Type curve matching, computer aided regression, …

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SPE 98010

Shahab D. Mohaghegh

IntroductionShortcomings of the current state-of-the-art.

Inherent subjectivity.Addresses individual wells rather than the entire field.Lack of user friendly software product that uses the latest developments.

Minimizing subjectivityReasonable repeatability

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Shahab D. Mohaghegh

OBJECTIVEDevelopment of a unified & comprehensive production data analysis technique:

Minimize subjectivity.Address the entire field (reservoir).Reasonable geologic resolution.

Identify opportunities in mature fields:Sweet spots.Optimum remedial operations.

THIS IS A WORK IN PROGRESS

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Shahab D. Mohaghegh

METHODOLOGYAn iterative approach that integrates:

Decline curve analysisType curve matchingSingle-well reservoir simulationFuzzy pattern recognition & analysisGenetic optimization

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Shahab D. Mohaghegh

METHODOLOGYThe methodology is demonstrated through application to a mature field in mid-continent U.S.

Golden Trend Fields of Oklahoma85 wells used in the analysisOnly publicly available production data was used for analysis

NOTE: as more data (geology, logs, cores, well tests, …) becomes available they can be incorporated in the analysis to increases accuracy and dependability of the results.

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THE WORK FLOW

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Shahab D. Mohaghegh

A TWO STEP PROCESSSTEP ONE:An integrated, interactive and iterative approach that would converge to a set of reservoir characteristics by:

Decline Curve Analysisqi, Di, b, EUR

Type Curve Matching Analysisb, EUR, kh, s, φ, A

History Matchingb, EUR, k, h, s, φ, A, …

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SPE 98010

Shahab D. Mohaghegh

A TWO STEP PROCESSSTEP TWO:Super-impose reservoir characteristics from all wells in the field.Using a fuzzy pattern recognition technique:

Detect trends in the field.Identify sweet spots.Track fluid movement (remaining reserves – water flooding).Identify remedial opportunities (underperformers)

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Shahab D. Mohaghegh

DECLINE CURVE ANALYSIS

qi, Di, b, EUR

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SPE 98010

Shahab D. Mohaghegh

DECLINE CURVE ANALYSIS

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SPE 98010

Shahab D. Mohaghegh

TYPE CURVE MATCHINGType Curve selection is important.

Must be rate and not pressure type curve.

Look for commonality between type curve and decline curve.

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SPE 98010

Shahab D. Mohaghegh

TYPE CURVE MATCHING

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SPE 98010

Shahab D. Mohaghegh

TYPE CURVE MATCHING

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SPE 98010

Shahab D. Mohaghegh

TYPE CURVE MATCHING

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Shahab D. Mohaghegh

COMBINED DCA & TCM

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SPE 98010

Shahab D. Mohaghegh

COMBINED DCA & TCM

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SPE 98010

Shahab D. Mohaghegh

TYPE CURVE MATCHINGA set of parameters are required for the type curve matching process. Default values can be entered for all wells. Also if more data is available (logs, cores, …) they can be entered for each well separately.

To be used in History matching

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Shahab D. Mohaghegh

COMBINED DCA & TCM

“b” from DCA is used to identify the most likely type curve to use for matching and matching EUR from DCA & TCM is used to find the most likely set of reservoir characteristics.

To be used in History matching along with the default parameters

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SPE 98010

Shahab D. Mohaghegh

RESERVOIR SIMULATIONSHistory matching of production data using a single-well radial model.Use the results of type curve matching procedure as a guideline to achieve reasonable history match.In order to achieve a reasonable match we have to go back to TCM and DCA and iteratively modify some parameters.

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RESERVOIR SIMULATIONS

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Shahab D. Mohaghegh

RESERVOIR SIMULATIONS

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SPE 98010

Shahab D. Mohaghegh

RESERVOIR SIMULATIONSOnce a reasonable match has been achieved, the reservoir characteristics from the history match may be somewhat different from those calculated during Type Curve Matching.To resolve this final step we use Monte Carlo Simulation.

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Shahab D. Mohaghegh

MONTE CARLO SIMULATIONUsing the reservoir characteristic values from TCM & HM we generate probability distribution functions for each of them.Perform Monte Carlo Simulation (using the reservoir simulator as the objective function) and calculate the 30 Year EUR.Plot the resulting 30 Year EUR PDF as well as the EUR calculated from TCM & DCA.

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MONTE CARLO SIMULATION

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MONTE CARLO SIMULATION

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Reservoir Quality IndexUpon completion of the first step, we have a set of reservoir characteristics that are reasonable close to reality, if not in actual numbers, but in quality and range.In the second step we use these “fuzzy” reservoir characteristics and try to detect trends and make field-wide judgments.After all this is what we normally do during human thought process.

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Reservoir Quality IndexTwo and three dimensional maps using production indicators such as:

First and Best 3 Months of ProductionFirst and Best 6 Months of ProductionFirst and Best 9 Months of ProductionFirst Year of Cumulative ProductionThree Year Cumulative ProductionFive Year Cumulative ProductionTen Year Cumulative Production

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Reservoir Quality Index

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Reservoir Quality Index

Fuzzy Pattern Recognition

Fuzz

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Low Relative Reservoir Quality Index – RRQI represents higher quality reservoir characteristics.

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Reservoir Quality Index

Fuzzy Pattern Recognition

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Note the movement of a well from one RRQI to another with time.

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Reservoir Quality Index

Fuzzy Pattern Recognition

Fuzz

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Shahab D. Mohaghegh

Reservoir Quality Index

Fuzzy Pattern Recognition

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Shahab D. Mohaghegh

Reservoir Permeability

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Reservoir Permeability

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Remaining Reserves

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Remaining Reserves

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SPE 98010

Shahab D. Mohaghegh

Remaining Reserves

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Shahab D. Mohaghegh

Sweet Spots

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Remaining ReservesUsing this result and performing some What-Ifscenarios, one may identify the best location for in-fill drilling.

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CONCLUSIONSA new technique has been introduced for production data analysis.The new technique integrates DCA, TCM and HM in an iterative fashion to converge to a reasonable set of reservoir characteristics.The new technique uses fuzzy pattern recognition with the results o

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Shahab D. Mohaghegh

CONCLUSIONSThe new technique uses fuzzy pattern recognition with the results of above integration and produces 2D and 3D maps of the filed for:

Reservoir QualityReservoir CharacteristicsRemaining Reserves

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LIMITATIONS At this point in time performing this analysis is somewhat time consuming.This is a more qualitative analysis compare to other techniques. We do not claim to calculate permeability with 3 significant figures after decimal.Nevertheless, lack of precision by no means translates to lack of accuracy.

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FUTURE WORKAutomation of this process with absolutely minimum human interaction is the ultimate goal. Developing means for performing What-If scenarios.We are currently working on a genetic optimization routine that would accomplish the intelligent automation task.