Applying hybrid support vector regression and genetic algorithm to water alternating CO₂gas EOR

Abstract

Water alternating CO2 gas injection (WAG CO2) is one of the most promising enhanced oil recovery techniques. The optimization of this process requires performing many time‐consuming simulations. In this paper, an intelligent hybridization based on support vector regression (SVR) and genetic algorithm (GA) is introduced for the WAG process optimization in the presence of time‐dependent constraints. Multiple SVRs are used as dynamic proxy to mimic numerical simulator behavior in real time. Latin hypercube design (LHD) is applied to generate the proper runs to train the proxy and ten supplementary runs are randomly chosen to validate it. The goal of GA in this study is twofold. First, it is employed during the training of multiple SVRs to find their appropriate hyper‐parameters. Second, once the training and validation of the dynamic proxy are done, the GA is coupled with it to find the optimum WAG parameters which maximize field oil production total (FOPT) subject to time‐dependent water‐cut constraint and some domain constraints. The task is formulated as a non‐linear constrained optimization problem. A semi‐synthetic WAG CO2 case is used to examine the reliability of the approach. The results show that the established dynamic proxy is fast and accurate in reproducing the simulator outputs. The hybridization proxy‐GA is demonstrated to be reliable for the real‐time optimization of the formulated WAG process. © 2020 The Authors. Greenhouse Gases: Science and Technology published by Society of Chemical Industry and John Wiley & Sons, Ltd.

Harnessing the power of machine learning for carbon capture, utilisation, and storage (CCUS) – a state-of-the-art review

“…The parameters were optimised using the SQP method. Amar et al 238 introduced a method to optimise the parameters of the CO 2 -WAG process to maximise oil production. SVR was used to build the proxy model and then the proxy was used with the GA to find the combinations of parameters that led to the optimal oil production.…”

Miscible Water Alternating Gas CO2 Injection Performance Analysis as Efforts to Improve Oil Recovery Factors in Field CW

“…The advantage of the WAG CO2 injection method is that it has a high sweep efficiency because it combines microscopic sweeping from CO2 gas with macroscopic sweeping of water, has a small mobility ratio so that the pressing occurs is very The drawback is that a more complicated injection system is needed because it requires a facility with a double injection system and a CO2 gas recycling processing facility [14].…”

Prediction of Oil Recovery Factor in Stratified Reservoirs after Immiscible Water-Alternating Gas Injection Based on PSO-, GSA-, GWO-, and GA-LSSVM

“…In recent studies, [42,43] simulated CO 2 WAG injection in a reservoir model and developed machine learning proxy models with different algorithms to predict current rates of oil, gas and water based on current time, gas and water injection rates, half cycle time and operational constraints. The recovery factor and cumulative production were calculated from the produced output.…”