AI Insight
This study presents a machine learning workflow for optimizing gas lift operations in unconventional oil and gas fields. The approach uses historical production data to predict gas lift performance curves and employs Bayesian optimization to determine optimal gas injection rates within facility constraints. A pilot test on 30 wells in the Bakken formation achieved over 5% production increase on average, leading to full deployment across 200+ wells.
Why it matters
This workflow offers a cost-effective alternative to traditional gas lift optimization methods that require expensive downhole sensors or time-consuming multi-rate well tests. The successful deployment demonstrates practical applicability for unconventional fields where conventional optimization approaches are economically unfeasible, potentially improving production efficiency across the industry.
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arXiv:2607.25885v1 Announce Type: cross
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.
Source: A Machine-Learning-Based Gas Lift Optimization Workflow for Unconventional Fields