作者:SHAHKARAMI Alireza,MOHAGHEGH Shahab
摘要:Using artificial intelligence(AI) and machine learning(ML) techniques, we developed and validated the smart proxy models for history matching of reservoir simulation, sensitivity analysis, and uncertainty assessment by artificial neural network(ANN). The smart proxy models were applied on two cases, the first case study investigated the application of a proxy model for calibrating a reservoir simulation model based on historical data and predicting well production while the second case study investigated the application of an ANN-based proxy model for fast-track modeling of CO2 enhanced oil recovery, aiming at the prediction of the reservoir pressure and phase saturation distribution at injection stage and post-injection stage. The prediction effects for both cases are promising. While a single run of basic numerical simulation model takes hours to days, the smart proxy model runs in a matter of seconds, saving 98.9% of calculating time. The results of these case studies demonstrate the advantage of the proposed workflow for addressing the high run-time, computational time and computational cost of numerical simulation models. In addition, these proxy models predict the outputs of reservoir simulation models with high accuracy.
發文機構:Saint Francis University West Virginia University
關鍵詞:smartproxymodelingreservoirsimulationmachinelearningartificialneuralnetworkhistorymatchingsensitivityanalysisoptimizationtechnologyCO2EOR
分類號: TP18[自動化與計算機技術—控制理論與控制工程]TE311[石油與天然氣工程—油氣田開發工程]