Qiao Lu, Wang Huijun, Lu Shuangfang, Liu Yang, He Taohua
Key Laboratory of Deep Oil and Gas, China University of Petroleum, Qingdao 266580, China.
School of Geosciences, China University of Petroleum (East China), Qingdao, Shandong 266580, China.
ACS Omega. 2022 Feb 28;7(10):8294-8305. doi: 10.1021/acsomega.1c05158. eCollection 2022 Mar 15.
Recently, production optimization has gained increasing interest in the petroleum industry. The most computationally intensive and critical part of the production optimization process is the evaluation of the production function performed by the numerical reservoir simulator. Employing proxy models as a substitute for the reservoir simulator is proposed for alleviating this high computational cost. In this study, a new approach to construct adaptive proxy models for production optimization problems is proposed. An adaptive difference evolution algorithm (SaDE) optimized least-squares support vector machine (LSSVM) is used as an approximation function, while training is performed using a self-adaptive response surface experimental design (SaRSE). SaDE selects the optimal hyperparameters of LSSVM during the training process to improve the prediction accuracy of the proxy model. Cross-validation methods are used in the recursive training and network evaluation phases. The developed method is used to optimize the production of block gas reservoir models. Computational results confirm that the developed adaptive proxy model outperforms traditional regression methods. It is further verified that when the experimental data are updated, the alternative model still has high prediction accuracy when performing the objective function evaluation. The results show that the proposed proxy modeling approach enhances the entire optimization process by providing a fast approximation of the actual reservoir simulation model with better accuracy.
近年来，生产优化在石油工业中越来越受到关注。生产优化过程中计算量最大且最关键的部分是由数值油藏模拟器执行的生产函数评估。为了减轻这种高计算成本，有人提出采用代理模型来替代油藏模拟器。在本研究中，提出了一种为生产优化问题构建自适应代理模型的新方法。一种自适应差分进化算法（SaDE）优化的最小二乘支持向量机（LSSVM）被用作近似函数，同时使用自适应响应面实验设计（SaRSE）进行训练。SaDE在训练过程中选择LSSVM的最优超参数，以提高代理模型的预测精度。交叉验证方法用于递归训练和网络评估阶段。所开发的方法用于优化块状气藏模型的产量。计算结果证实，所开发的自适应代理模型优于传统回归方法。进一步验证了，当实验数据更新时，替代模型在进行目标函数评估时仍具有较高的预测精度。结果表明，所提出的代理建模方法通过提供对实际油藏模拟模型的快速近似且具有更高的精度，增强了整个优化过程。
