智能代理在油藏建模中的应用

Applications of smart proxies for subsurface modeling

利用人工智能和机器学习技术,采用人工神经网络开发并验证了用于油藏模拟历史拟合、敏感性分析和不确定性评估的智能代理模型,将其应用于油藏模拟的两个案例中。第1个案例研究了代理模型在油藏模型历史拟合中的应用,输出结果预测了井的产量;第2个案例研究了基于人工神经网络的代理模型在CO2提高采收率油藏快速建模中的应用,目标为预测油藏压力和相饱和度在注入期间以及注入后的分布,预测效果均良好。相比基础数值模拟模型,智能代理模型运行单次模拟只需几秒钟,总节省98.9%的运算时间。智能代理模型在运算速度、消耗时间以及成本等方面都有巨大的优势。此外,智能代理模型与基础油藏模型模拟结果非常接近。

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 great. 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.