This paper highlights the demonstration, refinement, and implementation of a machine-learning (ML) algorithm to optimize multiple electrical-submersible-pump (ESP) wells in the Permian Basin. The complete paper presents two case studies for autonomous ESP optimization driven by this ML model. The paper discusses key learnings from each study to assist operators in their digital journey with considerations for effective field implementation.
Digital Maturity Framework
The framework is a two-variable matrix clarifying important milestones for both maturity of capability and solution implementation (Fig. 1 above). For the scope of implementing an ML inference model (MLIM) to source set-point recommendations to optimize a well producing with an ESP, the capability and implementation maturity paths are defined next.
Maturity of Solution Capability
- Measure—Predefined sensor and telemetry data are collected in a timely, consistent, and holistic manner.
- Optimize—Key artificial lift tuning parameters, including ESP frequency and well flowing tubing pressure (FTP) are sourced from the MLIM.
- Automate—MLIM set-point recommendations autonomously write to field control devices.
Maturity of Solution Implementation
- Develop—Configure field-data capture, create MLIM that autonomously generates set-point recommendations of ESP frequency and well FTP with a user interface (UI).
- Demonstrate—Use ML capabilities on a subset of wells producing in the Midland Basin.
- Refine—Improve the MLIM with reinforcement and feedback from domain experts and enhance UI.
- Deploy—Distribute the technology at scale across the field and transition into core business.
MLIM Overview. After building and testing a mature data-acquisition environment, the next step in autonomous ESP optimization was to develop an MLIM to deliver high-quality ESP set‑point recommendations. A previous publication focused on developing an ML model for ESP optimization using an extensive data set from 193 ESP-operated wells.
