Developing a Surrogate Reservoir Model (SRM) Using CMG...
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Reservoir Simulations and Statistical Analyses History Matching and Generating Forecasts Acknowledgements References Developing a Surrogate Reservoir Model (SRM) Using CMG and IBM Analytics to Optimize Waterflooding Trixie Anne Roque 1 , Muhammad Zulqarnain 2 , Esmail Ansari 2 , Mayank Tyagi 2,3 1 Department of Computer Science, Loyola Marymount University, 90045 2 Craft & Hawkins Department of Petroleum Engineering, 3 Center for Computation and Technology, Louisiana State University, 70803 • 1 http://www.chinaoilfieldtech.com/oilrecovery.html • 2 http://petrowiki.org/Material_balance_in_oil_reservoirs • Islam, Akand W., and Kamy Sepehrnoori. "A Review on SPE’s Comparative Solution Projects (CSPs)." Journal of Petroleum Science Research 2.4 (2013): 167-79. Science and Engineering Publishing Company. Web. 1 June 2016. • Killough, J. E. (1995). Ninth SPE Comparative Solution Project: A Reexamination of Black-Oil Simulation. Society of Petroleum Engineers. doi:10.2118/29110-MS Building the Surrogate Reservoir Model This material is based upon work supported by the National Science Foundation under award OCI-1560410 with additional support from the Center for Computation and Technology at Louisiana State University. Background Organic materials under extreme temperature and pressure form petroleum deposits. Data collected by the oil and gas industry through the exploration and production of hydrocarbons (E&P) help in developing production models to interpret the subsurface information and optimize E&P operations. A surrogate reservoir model (SRM) functions as an analog to a physical reservoir. In contrast to traditional numerical reservoir models that require high- computational costs in exchange for accurate predictions, the SRM combines high-speed calculations and accuracy. This project deals with optimizing the waterflooding phase of oil extraction using a SRM. Waterflooding is the second stage of oil recovery. It involves injecting pressurized fluids into the reservoir in order to sweep the residual oil not recovered by the primary recovery phase. Through this process, up to 40% of the original oil in place (OOIP) can be extracted from a reservoir. 1 • The created model demonstrated a high correlation to the generated data. • The model was used to forecast 25 months into the future (Figure 7). Equation 2. • IBM SPSS Modeler is a software that provides an environment for data mining and modeling: o Allows automatic model identification in the Time Series Modeling feature (Figure 5) o Provides best-fit measurements like the R-squared value (Equation 2) in tabular (Figure 6) and graphical form to help determine the suitable model Equation 1 2 . o G fgi , N foi , and W are the initial free gas, oil, and water in place, respectively o G p , N p , and W p are the cumulative produced gas, oil, and water, respectively o G I and W I are the cumulative injected gas and water respectively o E g , E o , E w , and E f are the gas, oil, water, and rock (formation) expansivities • Reservoir simulations follow a material-balance equation to account for the rock expansion and the fluids that enter (injected fluids) and leave (withdrawn fluids). CMG – IMEX Simulator Figure 1. 3-D view of SPE-9 showing porosity levels for each layer. SPE-9 contains 9,000 (24x25x15) grid blocks. Figure 2. Top-down view shows varying permeability. 25 producer wells are placed sporadically across the center. Figure 3. Reservoir oil saturation before simulation begins. Original injector is located at X=24, Y=25, 11 <= Z <= 15 (bottom-right corner) with injection rate at 5000 bbl/day. Figure 4. One instance of a simulation. Here, the injector is placed at X=23, Y=2, 7 <= Z <= 15. Multiple runs revealed this location to produce the most cumulative oil. IBM SPSS Modeler Figure 5. Directed graph reflects the flow of data from Excel source (round node) to time plots (triangle nodes). The pentagon (Time Series Modeling node) creates the model (golden nugget). The time plots visually indicate how closely the model fits the data. Figure 6. The modeler also presents various goodness-of-fit values to determine how close the model matches the given data set. • Collected data consisted of field production rates (oil, gas, and water), injection rate, and cumulative oil. • Each simulation produced approximately 250 data points. • To perform a history match, the data points were split in half into targets and inputs. • Column 3 contains the visual representation of the effectiveness of the model. Process Flow Diagram Reservoir data are generated Model is created Change parameters (injection rate and location) Observe reservoir behavior based on changes Run simulations using CMG – IMEX and SPE-9 reservoir (Figures 1-4) Match the data using IBM SPSS Modeler (Figures 5-6) Forecast future reservoir behavior using model (Figure 7) Figure 7. Side-by-side comparison of injection rate = 2500 bbl/day (left) and 7000 bbl/day (right). The forecast for each is represented by the trailing red line.
Transcript of Developing a Surrogate Reservoir Model (SRM) Using CMG...
ReservoirSimulationsandStatisticalAnalyses
HistoryMatchingandGeneratingForecasts
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Acknowledgements
References
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DevelopingaSurrogateReservoirModel(SRM)UsingCMGandIBMAnalyticstoOptimizeWaterflooding
TrixieAnneRoque1,MuhammadZulqarnain2,Esmail Ansari2,Mayank Tyagi2,31DepartmentofComputerScience,LoyolaMarymountUniversity,90045
2Craft&HawkinsDepartmentofPetroleumEngineering,3CenterforComputationandTechnology,LouisianaStateUniversity,70803
• 1http://www.chinaoilfieldtech.com/oilrecovery.html• 2http://petrowiki.org/Material_balance_in_oil_reservoirs• Islam, Akand W., and Kamy Sepehrnoori. "A Review on SPE’s Comparative Solution
Projects (CSPs)." Journal of Petroleum Science Research 2.4 (2013): 167-79. Science and Engineering Publishing Company. Web. 1 June 2016.
• Killough, J. E. (1995). Ninth SPE Comparative Solution Project: A Reexamination of Black-Oil Simulation. Society of Petroleum Engineers. doi:10.2118/29110-MS
BuildingtheSurrogateReservoirModel
This material is based upon work supported by the National Science Foundation under award OCI-1560410 with additional support from the Center for Computation and Technology at Louisiana State University.
Background
Organic materials under extreme temperature and pressure form petroleum deposits. Data collected by the oil and gas industry through the exploration and production of hydrocarbons (E&P) help in developing production models to interpret the subsurface information and optimize E&P operations.
A surrogate reservoir model (SRM) functions as an analog to a physical reservoir. In contrast to traditional numerical reservoir models that require high-computational costs in exchange for accurate predictions, the SRM combines high-speed calculations and accuracy. This project deals with optimizing the waterflooding phase of oil extraction using a SRM.
Waterflooding is the second stage of oil recovery. It involves injecting pressurized fluids into the reservoir in order to sweep the residual oil not recovered by the primary recovery phase. Through this process, up to 40% of the original oil in place (OOIP) can be extracted from a reservoir.1
• The created model demonstrated a high correlation to the generated data.• The model was used to forecast 25 months into the future (Figure 7).
Equation2.
• IBM SPSS Modeler is a software that provides an environment for data mining and modeling:
o Allows automatic model identification in the Time Series Modeling feature (Figure 5)
o Provides best-fit measurements like the R-squared value (Equation 2) in tabular (Figure 6) and graphical form to help determine the suitable model
Equation12.
o Gfgi, Nfoi, and W are the initial free gas, oil, and water in place, respectivelyo Gp, Np, and Wp are the cumulative produced gas, oil, and water, respectivelyo GI and WI are the cumulative injected gas and water respectivelyo Eg, Eo, Ew, and Ef are the gas, oil, water, and rock (formation) expansivities
• Reservoir simulations follow a material-balance equation to account for the rock expansion and the fluids that enter (injected fluids) and leave (withdrawn fluids).
CMG– IMEXSimulator
Figure 1. 3-D view of SPE-9 showing porosity levels for each layer. SPE-9 contains 9,000 (24x25x15) grid blocks.
Figure 2. Top-down view shows varying permeability. 25 producer wells are placed sporadically across the center.
Figure 3. Reservoir oil saturation before simulation begins. Original injector is located at X=24, Y=25, 11 <= Z <= 15 (bottom-right corner) with injection rate at 5000 bbl/day.
Figure 4. One instance of a simulation. Here, the injector is placed at X=23, Y=2, 7 <= Z <= 15. Multiple runs revealed this location to produce the most cumulative oil.
IBMSPSSModeler
Figure 5. Directed graph reflects the flow of data from Excel source (round node) to time plots (triangle nodes). The pentagon (Time Series Modeling node) creates the model (golden nugget). The time plots visually indicate how closely the model fits the data.
Figure 6. The modeler also presents various goodness-of-fit values to determine how close the model matches the given data set.
• Collected data consisted of field production rates (oil, gas, and water), injection rate, and cumulative oil.
• Each simulation produced approximately 250 data points.• To perform a history match, the data points were split in half into targets and
inputs.• Column 3 contains the visual representation of the effectiveness of the model.
ProcessFlowDiagram
Reservoir data are generated
Model is createdChange parameters
(injection rate and location)Observereservoir
behaviorbasedonchanges
RunsimulationsusingCMG– IMEXandSPE-9reservoir
(Figures1-4)
MatchthedatausingIBMSPSSModeler
(Figures5-6)
Forecastfuturereservoirbehavior
usingmodel(Figure7)
Figure 7. Side-by-side comparison of injection rate = 2500 bbl/day (left) and 7000 bbl/day (right). The forecast for each is represented by the trailing red line.
