一种基于机器学习的非常规油藏气举优化工作流程
A Machine-Learning-Based Gas Lift Optimization Workflow for Unconventional Fields
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中文总结 AI 辅助
针对非常规油藏气举优化问题，提出基于机器学习的自动化数据驱动工作流程，集成预测气举性能曲线的模型与贝叶斯优化框架，利用历史数据求解最佳注气速率，试点及部署效果良好，为相关资产提供有效经济方案。
中文摘要 AI 辅助
本文提出了一种利用机器学习的自动化数据驱动工作流程，用于非常规油藏的气举优化。该工作流程集成了一个能准确预测气举性能曲线的机器学习模型和一个贝叶斯优化框架，以在设施能力约束下求解最佳注气速率。机器学习模型利用历史生产时间序列数据，无需井下仪表或多产量试井。在巴肯地区5个井场的30口井上进行了试点，平均产量提高了5%以上。目前已在巴肯地区200多口气举和柱塞辅助气举井上全面部署。该工作流程对于因成本或设施限制而无法获得井下数据或进行多产量测试的其他资产是一种有效且经济的解决方案。
英文摘要
In this paper, we present an automated data-driven workflow using Machine Learning (ML) for gas lift optimization in unconventional fields. This workflow integrates a ML model that accurately forecasts the Gas Lift Performance Curve, and a Bayesian Optimization Framework to solve for the optimal gas injection rates under the constraints of facility capacity. The ML model leverages the historical production time series data without requiring downhole gauges or multi-rate well tests. We piloted this workflow on 30 wells across 5 well pads in Bakken and obtained >5% production uplift on average. With the success of the pilot, we have now fully-deployed this workflow in Bakken across 200+ gas lift and plunger-assisted gas lift (PAGL) wells. Moreover, the ML-based gas lift optimization workflow presented in this paper is an effective and economic solution for other assets where downhole data or multi-rate testing are not available/feasible due to cost or facility constraints.
