Artificial lift system optimization using machine learning applications

Abstract

Currently, in the oil industry, artificial lift optimization (ALO) systems are dealing with different applications including well monitor and control, reservoir management, production optimization, predictive maintenance, artificial lift, and flow assurance, multiphase pumping systems, etc. The scope of this article is to provide a detailed analysis of ALO and predictive pump maintenance applications using machine learning (ML) and artificial intelligence (AI). The oil and gas industry has experienced a lot of improvements that have impacted the businesses and economies associated with the market in recent times. Issues such as unplanned shutdown time and failure of equipment cause a huge impact on many corporations especially with the current fluctuations in hydrocarbon prices. Similarly, advanced modern technologies such as real-time analysis and predictive maintenance are designed to drive ALO systems. This paper covers several applications and techniques in which ML and AI have been applied to optimize hydrocarbon withdrawal from potentially depleted reservoirs that require some external supports to uplift the reservoir fluid from sub surface to surface using artificial lift system. In a nutshell, the applications of AI and ML for the artificial lift selection, their predictive maintenance, equipment malfunctioning detection, etc. using a self-trained system are the main topics of this paper. While reviewing each of these techniques, the workflow is also explained along with the effectiveness of utilizing each application to the current operations.