Innovative Approach to Prolong ESP Run Life Using Algorithmic Models

Abstract

Abstract Over the last few decades, numerous improvements have been made on Electrical Submersible Pumps (ESP) as an economical artificial lift method in the oil industry. However, the ESP reliability has just marginally increased considering the strategic alliances, maintenance fees, driven performances, and production deferrals. Also, the ESP was previously monitored by conventional ways and means (such as frequent and physical well site visits, obtaining ESP related parameters and evaluating the manual amperage (Amps) charts) for the required course of actions after the fact which hindered reaching optimum ESP performance and shortened its run life. The ESP monitoring and surveillance program has been through several enhancements throughout the past years with an objective of timely maintaining the ESP parameters. Part of this process is the initiation of Artificial Lift Teams (ALT) who are tasked to enhance surveillance program, optimize ESP performance, and enhance the ESP best practices which led to prolong ESP run life. The ALT has developed an era of optimized methodology and monitoring system that integrates all ESP parameters and variables in an assimilated platform. The system has a capability of a diagnostic process, production optimization, ESP track and trip prediction alert, ESP dashboard with production rates, and field overview in order to decide on the immediate remedial action. This approach acts as a quality assurance system using algorithmic models to proactively predict ESP trip or failure. This paper will present the innovative methodology, which is being currently used, resulted in optimizing the monthly production, eliminating the deferred production, unifying the operational ESP data, integrating an ESP standalone platform, triggering flags, sending alerts, minimizing ESP downtime, and optimizing ESP maintenance costs. The system led to enhance the ESP daily operations and prolong its run life.