Edge Analytics at the Wellhead: Designing Robust Machine Learning Models for Artificial Lift Failure Detection

Abstract

Abstract This paper outlines the challenges and constraints related to deployment of Machine Learning solutions for rod pump abnormal states recognition and diagnosis at the wellhead. Those abnormal states may lead to a failure or to non-optimized production. Particular focus is on two main aspects: 1) Develop a robust Machine Learning model & IIoT architecture to predict rod pump failure directly at the wellhead, 2) Ensure high level of pump failure prediction through Machine Learning to ensure operator confidence. To the best of our knowledge, this is the first-of-its-kind IIoT Edge Analytics solution which provides operators with the capability of automated Dynagraph Card recognition directly at the wellhead via Machine Learning models. This solution also addresses end-user requirements in terms of confidentiality and communication infrastructure.