增强恢复

机器学习可实现数据驱动的 CO™ EOR 数值研究预测

作者提出了一个开源框架，用于开发和评估机器学习辅助数据驱动的 CO2 强化采油过程模型，以预测石油产量和 CO2 保留。

图 1 — 用于评估 CO2-WAG 过程数据驱动预测的油藏模型后处理示意图。单位为英尺。
图 1 — 用于评估 CO2-WAG 过程数据驱动预测的油藏模型后处理示意图。单位为英尺。
来源：SPE 218441。

本文提出了一个开源框架，用于开发和评估机器学习 (ML) 辅助数据驱动的 CO 2提高采收率 (EOR) 过程模型，以预测石油产量和 CO 2保留量。作者的主要目标是使用结合 Python 编程、油藏模拟和 ML 技术的完整开源方法提高预测石油采收率和 CO 2保留量的速度、稳健性和准确性。

开源框架概述

使用两个 CO 2水-气交替 (WAG) 模拟案例对预测模型进行了评估，这两个案例是使用 SPE 比较解决方案项目 (CSP) 5 模拟模型作为参考提出的。首先，生成一个油藏模拟甲板模板和一个配置文件（包括变量输入），以便为每个案例创建任意数量的模拟作业。模拟的输入值范围是在 Python 脚本中设置的。

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原文链接/JPT
Enhanced recovery

Machine Learning Enables Data-Driven Predictions of CO₂ EOR Numerical Studies

The authors present an open-source framework for the development and evaluation of machine-learning-assisted data-driven models of CO₂ enhanced oil recovery processes to predict oil production and CO₂ retention.

Fig. 1—Postprocessing schematic of the reservoir model used to assess the data-driven predictions of the CO₂ WAG process. Units are in feet.
Fig. 1—Postprocessing schematic of the reservoir model used to assess the data-driven predictions of the CO₂ WAG process. Units are in feet.
Source: SPE 218441.

An open-source framework is presented for the development and evaluation of machine learning- (ML) assisted data-driven models of CO2 enhanced oil recovery (EOR) processes to predict oil production and CO2 retention. The main objective of the authors was to increase the speed, robustness, and accuracy of predicting oil recovery and CO2 retention using a complete open-source approach combining Python programming, reservoir simulation, and ML techniques.

Overview of the Open-Source Framework

The evaluation of the predictive models was performed using two CO2 water-alternating-gas (WAG) simulation cases, which were proposed using the SPE Comparative Solution Project (CSP) 5 simulation model as a reference. First, a reservoir simulation deck template and a configuration file, including variable inputs, were generated to create any number of simulation jobs for each case. The input-value range for the simulations was set in a Python script.

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