Rapid Calibration of High Resolution Geologic Models to Dynamic Data Using Inverse Modeling: Field Application and Validation
The goal of the project is to develop a systematic procedure and workflow for dynamic data integration for improved reservoir characterization. The overall goal is additional oil recovery by locating critical reservoir features such as flow channels, barriers, and reservoir compartmentalization that result in bypassed oil.
Texas Engineering Experiment Station (TEES), Texas A&M University, College Station, TX
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA
The expected results will be the development of a procedure and workflow for integration of dynamic data with geologic model. This approach will quickly identify the discrepancy between geologic model and production data and allow for rapid updating of reservoir description using inverse modeling. The objective is also to assess the uncertainty in reservoir parameters and rock-fluid properties at various scales.
The new reservoir characterization methodology will increase oil recovery by locating critical reservoir features such as flow channels, barriers, and reservoir compartmentalization that result in bypassed oil.
This approach to history matching uses three-phase flow using a novel compressible streamline formulation and streamline-derived analytic sensitivities. The methodology is designed to identify the discrepancy between geologic model and production data and allow for rapid updating of reservoir description using inverse modeling.
The objective is also to assess the uncertainty in reservoir parameters and rock-fluid properties at various scales. The project plans a systematic procedure and work flow for dynamic data integration for improved reservoir characterization.
Project tasks include the:
- Development and testing of a compressible streamline formulation.
- Generalization of streamline-based sensitivities to three-phase flow.
- Validation of streamline-derived sensitivities.
- Use of streamline-derived sensitivities for inverse modeling and uncertainty assessment.
The practical feasibility of the approach will be demonstrated using a field application of a CO2 injection pilot project in West Texas that will be followed by a detailed model validation.
Current Status (April 2008)
This project was completed and awaiting the final report.
This project was selected in response to DOE’s Oil and Gas Program solicitation, DE-PS26-05NT15600.
Project Start: October 1, 2005
Project End: March 31, 2008
Anticipated DOE Contribution: $ 429,585 (plus $ 100,000 to LBNL)
Performer Contribution: $ 179,542
Other Government Organizations Involved: Lawrence Berkeley National Laboratory (LBNL)
NETL – Traci Rodosta (Traci.Rodosta@netl.doe.gov or 304-285-1345)
TEES – Akhil Data-Gupta (email@example.com or 979-847-9030)
Cheng, H., Oyerinde, A., Datta-Gupta, A. and Milliken, W., “Compressible Streamlines and Three-Phase History Matching,” SPE/DOE Symposium on Improved Oil Recovery, Tulsa, OK, April 22–26, 2006.
Cheng, H., Oyerinde, A., Datta-Gupta, A., and Milliken, W., “Compressible Streamlines and Three- Phase History Matching,” SPE 99465, SPE/DOE Symposium on Improved Oil Recovery, Tulsa, OK, April 22–26, 2006.
Arroyo-Negrete, Elkin, Deepak Devegowda, Datta-Gupta, Akhil, “Continuous Reservoir Model Updating Using Ensemble Kalman Filter with Streamline-based Covariance Localization,” SPE 104255, 2006 SPE International Oil & Gas Conference and Exhibition in China in Beijing, China, December 5–7, 2006.
Osako, Ichiro, and Datta-Gupta, A., “A Novel Compositional Streamline Formulation with Compressiblity Effects,” SPE 106148, SPE Reservoir Simulation Symposium in Houston, TX, February 26–28, 2007.
Devegowda Deepak, Arroyo, Elkin and Datta-Gupta, A., “Efficient and Robust Reservoir Model Updating Using Ensemble Kalman Filter With Sensitivity Based Covariance Localization,” SPE 106144 to be presented at the SPE Reservoir Simulation Symposium, Houston, TX, February 26–28, 2007.
Hohl, D., Jimenez, E. and Datta-Gupta, A., “Field Experiences With History Matching an Offshore Turbiditic Reservoir Using Inverse Modeling,” SPE 101983, 2006 SPE Annual Technical Conference and Exhibition, San Antonio, TX, U.S.A., September 24–27, 2006.
Ma, X., Al-Harbi, M., Datta-Gupta, A., and Efendiev, Y. “A Multistage Sampling Approach to Quantifying Uncertainty During History Matching Geological Models,” SPE 102476, SPE Annual Technical Conference and Exhibition in San Antonio, TX, September 24–27, 2006.
An illustration of impact of dynamic data integration on permeability distribution: (Top) permeability model based on static data only; (Bottom) permeability model updated after integration of water-cut response (from SPE 89857).