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

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.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)

arXiv:2607.25885 [cs.LG]

(or arXiv:2607.25885v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.25885 arXiv-issued DOI via DataCite (pending registration)

Proceedings of the Unconventional Resources Technology Conference (URTeC), Houston, TX, USA, June 17-19, 2024, pp. 1600-1610

https://doi.org/10.15530/urtec-2024-4033553 DOI(s) linking to related resources