Review of AI Implementations and Hybrid Data-Physics Modeling for EOR Applications

Abstract

Abstract Enhanced oil recovery (EOR) projects are generally considered to be of high cost and risk. Modeling and prediction of performance remains a challenging task. Artificial Intelligence (AI) techniques have the potential to aid in performance optimization of EOR projects that results in increase in hydrocarbon recovery. The classical approaches to EOR optimization have either relied on the human expertise that utilizes low scale analysis, experience or physics-based reservoir simulation that requires long lead time to build. These are difficult to use to evaluate huge number of possible optimization scenarios. A combined approach of physics-based and data-driven AI models can provide a more efficient way for EOR performance optimization. This paper investigates the active research of deployment of AI in this particular application. Modeling of EOR performance optimization includes data assimilation, model fitting, validation, and testing. EOR performance optimization is investigated using a single- and multi- objective problem approach. The goal of creating the models is to provide a quantitative outcome, and for the model to be continuously updated to reflect actual field data and conditions. A case study of a steam flooding EOR project in San Joaquin Basin is presented. The project optimization included building combined physics-based and data-driven AI models to maximize production, maximize NPV, and minimize steam injection. Enormous number of possible injection plans were generated to select the best scenario with consideration of field and operator constrains. The modeling was used as a basis in a significant change in the field development strategy. Assessment after plan implementation showed a remarkable incremental gain in production in comparison to expected decline. The research reviews challenges of this type of modelling when it comes to data quality needs, long term implementations, and potential of rapid add up of computing power needs. Artificial intelligence offers opportunities to improve EOR performance optimization processes. It provides the capability of modeling to perform analysis and provide a fast and actionable plan as well as to predict production forecast. Also, it gives a powerful tool for day-to-day decision making, which is needed in this type of projects. The ever-evolving nature of artificial intelligence techniques hold more promise for improvement, as it can address some of the short comes experienced today.