Abstract
Large CO2-enhanced oil recovery (EOR) projects usually contain an abundance of geological and good performance data. While this volume of data leads to robust models, it often results in difficult to manage, slow-running numerical flow models. To dramatically reduce the numerical run-times associated with the traditional simulation techniques, this work investigated the feasibility of using artificial intelligence and machine learning technologies to develop a smart proxy model of the Scurry Area Canyon Reef Operators Committee (SACROC) oilfield, located in the Permian Basin, TX, USA. Smart proxy models can be used to facilitate injection-production optimization for CO2-EOR projects. The use of a coupled grid-based, and well-based surrogate reservoir model (SRM) (also known as smart proxy modeling) was investigated as the base of the optimization. A fit-for-purpose coupled SRM, which executes in seconds, was built based on high-resolution numerical reservoir simulation models of the northern platform of the SACROC oilfield. This study is unique as it is the first application of coupled SRM at a large oilfield. The developed SRM was able to identify the dynamic reservoir properties (pressure, saturations, and component mole-fraction) at every grid-block, along with the production characteristics (pressure and rate) at each well. Recent attempts to use machine learning and pattern recognition to build proxy models have been simplistic, with limited predictive capabilities. The geological model used in this study is comprised of more than nine million grid blocks. The high correlation between the actual component and SRM, which can be visualized by mapping the properties, along with the fast footprint of the developed model demonstrate the successful application of this methodology.
Highlights
Despite the great advances in reservoir modeling tools and the advent of high-performance computing, high-fidelity physics based numerical simulation still remains a challenging step in understanding the physics of the reservoir and the relationship between the model parameter and control inputs for improved recovery efficiency
The primary production mechanism was indicated as merely solution gas drive, based on the early performance history of the field, which would probably result in an ultimate recovery of less than 20% of the original oil in place (OOIP)
The flood is as important as the production. Rate front monitoring this end, we propose to assimilate the SRMG and SRMW by feeding the data from one model to
Summary
Despite the great advances in reservoir modeling tools and the advent of high-performance computing, high-fidelity physics based numerical simulation still remains a challenging step in understanding the physics of the reservoir and the relationship between the model parameter and control inputs for improved recovery efficiency. The computational time of such large-scale models becomes a bottleneck of fast turnarounds in the decision-making process. Modeling and optimization of the water-alternating-gas (WAG) cycles and well controls [1], the building of the reduced order models for this phenomenon has not gained as much attention. The objective in this work is to describe the surrogate reservoir model (SRM) procedure that can be used in the context of computational production optimization. A strategic requirement for any such technique is the ability to provide very fast, yet sufficiently accurate, simulation results
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