Prediction and Optimization of ESP Run Life: A Data-Driven Framework Based on Field Applications in Vietnam

Abstract

Electrical Submersible Pump (ESP) performance is a critical factor in maximizing production from mature fields, yet predicting and ensuring long-term reliability remains a significant challenge due to highly variable operating conditions. Traditional approaches often rely on isolated case studies, lacking a systematic framework for decision-making. This study proposes a novel, data-driven framework for ESP analysis and optimization, leveraging readily available historical operational data. The framework is built upon a comparative analysis of contrasting ESP application scenarios in Vietnam's Cuu Long Basin — from a highly successful, long-run-life case in a beginning environment (X field) to a challenging, high-temperature case that evolved from initial failure to success (Y field). A systematic methodology was employed, involving the quantification of Key Performance Indicators (KPIs) such as Run Life and Failure Mode analysis from historical production data and DIFA reports. A custom-developed screening software, the "ESP Quicklook Tool," was integrated into the initial assessment phase. The analysis revealed that while ESPs consistently deliver production uplift (1.3 to 7.5 times), electrical system integrity is the primary failure point (50-67% of incidents). The successful turnaround at X, which increased average run life from 45 to over 730 days, was directly attributed to a DIFA-driven iterative design process. Based on these findings, a standardized three-phase workflow is presented for optimizing ESP projects. This framework, validated against diverse field data, proves to be a viable and robust approach for enhancing ESP reliability, mitigating risks, and improving economic outcomes.