Redistribution of Steam Injection in Heavy Oil Reservoir Management to Improve EOR Economics, Powered by a Unique Integration of Reservoir Physics and Machine Learning

Pallav Sarma

The application of a novel modeling and optimization approach is presented, demonstrating the impact of quantitatively optimized steam redistribution in mature heavy oil fields. Results are presented for a steamflood in the San Joaquin Basin in California, demonstrating significant savings of steam and operational costs and significant production increase, ultimately increasing net present value (NPV) by at least 10%.

The new approach, termed Data Physics, is based on a novel combination of state-of-the-art machine learning methods with the partial differential equations of reservoir fluid flow, as present in reservoir simulators. Combined with an advanced data assimilation algorithm that merges a modified ensemble Kalman filter with quadratic programming, this approach allows rapid and simultaneous integration of production, injection, temperature, completion, maintenance and log data for large fields with thousands of wells. Next, the fitted models are combined with advanced multi-objective optimization algorithms and cloud computing to consider thousands of scenarios to optimize steam redistribution.

Results demonstrate that steam redistribution can be quantitatively optimized rapidly to maximize short and long-term EOR economics.

Back to Workshop III: Data Assimilation, Uncertainty Reduction, and Optimization for Subsurface Flow