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

2026-07-28

AI Summary

This study addresses the challenge of economically viable real-time gas lift optimization in unconventional reservoirs, where reliance on costly downhole pressure gauges or multi-rate testing hinders widespread implementation. To overcome this limitation, the authors propose a data-driven automated workflow that leverages only historical production time-series data to train machine learning models for predicting gas lift performance curves. Coupled with Bayesian optimization, the framework identifies optimal gas injection rates while respecting operational constraints, eliminating the need for additional downhole instrumentation or complex well tests. Validated in the Bakken region across 30 pilot wells, the approach achieved an average production uplift exceeding 5% and has since been successfully scaled to over 200 gas-lift and plunger-assisted gas-lift wells, demonstrating both effectiveness and scalability in constrained asset environments.

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.

Problem

• gas lift optimization
• unconventional fields
• production uplift
• data scarcity
• facility constraints

Innovation

• Machine Learning
• Gas Lift Optimization
• Bayesian Optimization
• Unconventional Fields
• Production Forecasting