A Machine-Learning-Based Gas Lift Optimization Workflow for Unconventional Fields

2024/01/01 by Sha, Miao, Alexandra Vendetti +15
Computer Science · Engineering · #Advanced Control Systems Optimization #Anomaly Detection Techniques and Applications #Reservoir Engineering and Simulation Methods #cs.AI #cs.LG #cs.SE

paper · pdf · doi:10.15530/urtec-2024-4033553

openalex publication_date 2024/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29

Abstract

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.

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