这篇论文用ML加贝叶斯优化，在Bakken油田200多口井实现了5%以上的增产，不用额外下设备，省钱又高效。
论文提出一种自动化数据驱动工作流，集成ML模型预测气举性能曲线，并采用贝叶斯优化框架在设施容量约束下求解最优注气速率。该ML模型仅需历史生产时间序列数据，无需井下压力计或多速率试井。在Bakken油田5个井场30口井的试点中平均增产超过5%，目前已全面部署至200余口气举及柱塞辅助气举井。此方法特别适用于因成本或设施限制无法获取井下数据或多速率测试的资产。
A Machine-Learning-Based Gas Lift Optimization Workflow for Unconventional Fields
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.