Today, the Dismantle, Inspection, and Failure Analysis (DIFA) process for electrical submersible pump (ESP) failure analysis is a tedious, human-intensive, and time-consuming activity. The activity involves a set of data and various information formats from several activities in the ESP operation lifecycle. This paper proposes a novel artificial intelligence workflow to improve the efficiency of the DIFA process using an ensemble of machine learning (ML) algorithms. This ensemble of algorithms brings together structured/unstructured data across equipment, production, operations, and failure reports to automate root-cause identification and analysis post breakdown. As a result, the time and human effort required in the process has been reduced, and process efficiency has drastically improved.

Keywords:
machine learning, upstream oil & gas, information, data source, natural language, failure component, algorithm, tracker, difa process, pull date
Subjects:
Information Management and Systems, Artificial intelligence
Copyright 2021, Society of Petroleum Engineers
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