Science-informed Machine Learning to Increase Recovery Efficiency in Unconventional Reservoirs
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Production of hydrocarbons from fractured, unconventional reservoirs is inherently inefficient. But machine learning offers a pathway both to increasing recovery efficiency at a site and to improving forecasts of production, thereby improving the economics of operations in unconventional reservoirs. Los Alamos—in partnership with DOE, NETL, and WVU—has been developing a science-informed workflow and platform for optimizing pressure-drawdown at a site, which will allow an operator to make reservoir-management decisions that optimize recovery in consideration of future production. This work relies on a hybridization of physics-based prediction and machine learning, whereby accurate synthetic data (in combination with available site data) can enable the application of machine learning methods for rapid forecasting and optimization. The physics-based prediction is built upon experimental and theoretical work to determine transport characteristics in shale at various scales, with an emphasis on materials from MSEEL-I; this fundamental shale R&D was conducted in partnership with DOE, NETL, and several other national labs. This work has resulted from a coordinated leveraging of developments across several projects within DOE FE30, along with internal investments from Los Alamos via LDRD. The development has utilized data from the MSEEL–I site for calibration and demonstration; however, the workflow and platform are readily extendable to operations at other sites, plays, and basins. This machine-learning method can aid operators to improve both recovery efficiency and competitiveness; to this end, future work would quantify processes for other plays/basins and integrate production details with economics.
- Research Organization:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Organization:
- USDOE Office of Fossil Energy (FE), Oil and Natural Gas
- DOE Contract Number:
- 89233218CNA000001
- OSTI ID:
- 1614818
- Report Number(s):
- LA-UR--20-22789
- Country of Publication:
- United States
- Language:
- English
Similar Records
LANL Activities on Mechanistic Approach to Analyzing and Improving Unconventional Hydrocarbon Production
A machine-learning inverse model framework for rapid forecasting and history matching in unconventional reservoirs
Supplementary Data for "Evaluation of the Economic Implications of Varied Pressure Drawdown Strategies Generated Using a Real-time, Rapid Predictive, Multi-fidelity Model for Unconventional Oil and Gas Wells" by Bello, K., Vikara, D., Sheriff, A., Viswanathan, H., Carr, T., Sweeney, M., O'Malley, D., Marquis, M., Vactor, R.T., and Cunha, L.
Technical Report
·
Thu Jul 07 00:00:00 EDT 2022
·
OSTI ID:1880447
A machine-learning inverse model framework for rapid forecasting and history matching in unconventional reservoirs
Journal Article
·
Fri Nov 05 00:00:00 EDT 2021
· Scientific Reports
·
OSTI ID:1830370
Supplementary Data for "Evaluation of the Economic Implications of Varied Pressure Drawdown Strategies Generated Using a Real-time, Rapid Predictive, Multi-fidelity Model for Unconventional Oil and Gas Wells" by Bello, K., Vikara, D., Sheriff, A., Viswanathan, H., Carr, T., Sweeney, M., O'Malley, D., Marquis, M., Vactor, R.T., and Cunha, L.
Dataset
·
Tue Nov 29 23:00:00 EST 2022
·
OSTI ID:1894134