Applications of Machine Learning in Subsurface Reservoir Simulation—A Review—Part I

Abstract

In recent years, machine learning (ML) has become a buzzword in the petroleum industry with numerous applications that guide engineers toward better decision making. The most powerful tool that most production development decisions rely on is reservoir simulation with applications in numerous modeling procedures, such as individual simulation runs, history matching and production forecast and optimization. However, all these applications lead to considerable computational time- and resource-associated costs, and rendering reservoir simulators is not fast or robust, thus introducing the need for more time-efficient and smart tools like ML models which can adapt and provide fast and competent results that mimic simulators’ performance within an acceptable error margin. The first part of the present study (Part I) offers a detailed review of ML techniques in the petroleum industry, specifically in subsurface reservoir simulation, for cases of individual simulation runs and history matching, whereas ML-based production forecast and optimization applications are presented in Part II. This review can assist engineers as a complete source for applied ML techniques since, with the generation of large-scale data in everyday activities, ML is becoming a necessity for future and more efficient applications.

Optimization of Well Placement in Carbon Capture and Storage (CCS): Bayesian Optimization Framework under Permutation Invariance

“…Petrophysical and fluid properties, such as porosity, permeability, fluid saturation, compressibility, and relative permeability, are crucial inputs for reservoir simulation [22,23]. Accurate characterization of these properties is essential for realistic simulation results.…”

Prospects of an Acid Gas Re-Injection Process into a Mature Reservoir

“…This output is then integrated into simulation models that represent physical components within the hydrocarbon production chain, which encompass systems responsible for producing fluids at the surface (wellbore) and their subsequent processing (surface facilities). This comprehensive modeling approach extends all the way to the final sales point, ensuring a thorough representation of the entire production system and allowing its detailed study and optimization [32,33].…”

“…Contrariwise, a dynamic reservoir model operates as a transient, time-dependent simulation of fluid flow within the reservoir and builds upon the static model by integrating production history, fluid properties, and reservoir management strategies. Dynamic models are essential in predicting reservoir behavior, optimizing production, and evaluating diverse development scenarios, such as EOR strategies and the effects of new wells on production [32,33].…”