Application of Machine Learning and Optimization of Oil Recovery and CO2 Sequestration in the Tight Oil Reservoir

Abstract

Summary In recent years, shale and tight reservoirs have become an essential source of hydrocarbon production since advanced multistage and horizontal drilling techniques were developed. Tight oil reservoirs contain huge oil reserves but suffer from low recovery factors. For tight oil reservoirs, CO2-water alternating gas (CO2-WAG) is one of the preferred tertiary methods to enhance the overall cumulative oil production while also sequestering significant amounts of injected CO2. However, the evaluation of CO2-WAG is strongly dependent on the injection parameters, which renders numerical simulations computationally expensive. In this study, a novel approach has been developed that utilized machine learning (ML)-assisted computational workflow in optimizing a CO2-WAG project for a low-permeability oil reservoir considering both hydrocarbon recovery and CO2 storage efficacies. To make the predictive model more robust, two distinct proxy models—multilayered neural network (MLNN) models coupled with particle swarm optimization (PSO) and genetic algorithms (GAs)—were trained and optimized to forecast the cumulative oil production and CO2 storage. Later, the optimized results from the two algorithms were compared. The optimized workflow was used to maximize the predefined objective function. For this purpose, a field-scaled numerical simulation model of the Changqing Huang 3 tight oil reservoir was constructed. By December 2060, the base case predicts a cumulative oil production of 0.368 million barrels (MMbbl) of oil, while the MLNN-PSO and MLNN-GA forecast 0.389 MMbbl and 0.385 MMbbl, respectively. As compared with the base case (USD 150.5 million), MLNN-PSO and MLNN-GA predicted a further increase in the oil recovery factor by USD 159.2 million and USD 157.6 million, respectively. In addition, the base case predicts a CO2 storage amount of 1.09×105 tons, whereas the estimates from MLNN-PSO and MLNN-GA are 1.26×105 tons and 1.21×105 tons, respectively. Compared with the base case, CO2 storage for the MLNN-PSO and MLNN-GA increased by 15.5% and 11%, respectively. In terms of the performance analysis of the two algorithms, both showed remarkable performance. PSO-developed proxies were 16 times faster and GA proxies were 10 times faster as compared with the reservoir simulation in finding the optimal solution. The developed optimization workflow is extremely efficient and computationally robust. The experiences and lessons will provide valuable insights into the decision-making process and in optimizing the Changqing Huang 3 low-permeability oil reservoir.