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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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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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IntroductionState-of-the-art in production data analysis:
ArpsFetkovichCarterWattenbargerBlasingameAgrawalHornHagoort
Decline curve analysis, Type curve matching, computer aided regression, …
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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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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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METHODOLOGYAn iterative approach that integrates:
Decline curve analysisType curve matchingSingle-well reservoir simulationFuzzy pattern recognition & analysisGenetic optimization
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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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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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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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DECLINE CURVE ANALYSIS
qi, Di, b, EUR
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DECLINE CURVE ANALYSIS
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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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TYPE CURVE MATCHING
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TYPE CURVE MATCHING
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TYPE CURVE MATCHING
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COMBINED DCA & TCM
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COMBINED DCA & TCM
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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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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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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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RESERVOIR SIMULATIONS
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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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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
y P
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Rec
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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
Fuzz
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Rec
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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
y P
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Rec
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Reservoir Quality Index
Fuzzy Pattern Recognition
Fuzz
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Rec
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Reservoir Permeability
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Reservoir Permeability
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Remaining Reserves
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Remaining Reserves
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Remaining Reserves
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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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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.