Real-Time Data and Expert System Combine to Develop Artificial Lift Predictive Diagnostics

Abstract

Abstract Electric submersible pumps (ESPs) and progressive cavity pumps (PCPs) are used as lifting methods in many oil fields to improve oil production. Downhole gauges are now installed on most of the ESPs and PCPs, providing relevant pressure and temperature measurements to assess pump performance and potential failures. The volume of high-frequency data overwhelms the capacity of most direct assessment methods based on human observation. To address this challenge, a method using a combination of signal processing tools together with an expert system has been developed to deliver a predictive diagnostic in real time. The data acquired is fed into a signal processing module to analyze and highlight trends and patterns. This module, based on Bayesian theory, segments the signal into a series of homogenous linear models. The segmentation is then used to provide statistical information about downhole measurements. Signal processing outputs have been designed to be probabilistic to account for uncertainties. The probability that pump intake pressure and pump discharge pressure will converge toward the same value is one example of such output. The probability of a noisy signal is another illustration of a result from the processing module. This real-time process is used to analyze all the ESP or PCP measurements available on specific criteria. Those pieces of information allow the expert system to predict pump failures. A Bayesian network with its probabilistic reasoning capabilities has been chosen to reproduce the ESP or the PCP physical knowledge. This Bayesian network contains input nodes gathering artificial lift parameters features and output nodes inferring failure probabilities. During a test in real conditions, this concept showed its capability to predict and correctly diagnose the failure. In addition, this solution confirmed its robustness with accurate failure prediction under numerous operational conditions—even when downhole parameters were missing. This concept could be used to reduce reaction time to a failure, improve the monitoring capacity of each surveillance engineer, and improve the ESP life expectancy by early remediation of failure signs.