ESP Well and Component Failure Prediction in Advance using Engineered Analytics - A Breakthrough in Minimizing Unscheduled Subsurface Deferments

Abstract

A failed Electrical Submersible Pump (ESP) well is generally identified when there is no flow to the surface. The process of reviving well production can take weeks leading to huge unwanted deferment. Through a Proof-Of-Concept (PoC), the objective is to prototype and evaluate the results of an early failure detection for ESP wells using Machine Learning (ML), without reserving focus on implementation. By demonstrating the feasibility of this approach and verifying that the concept has practical potential, the tool can be used to reduce deferment and identify failure prone component to either devise mitigation strategy for extending time-to-failure or work on an improved design before failure. The paper details all the work undertaken to develop a Predictive Analytics model based on ML algorithms using field sensor data, real time physics-based model calculated data and well failure history to predict ESP well failure and identify failed component in advance. The approach of database standardization, data pre-processing, machine-learning algorithm selection, supervised training and validation dataset creation shall be discussed. ESP domain knowledge used for Feature Engineering across multiple modeling iterations to consistently improve well and component level model metrics shall be detailed. After the evaluation by well owners at Petroleum Development Oman (PDO), refered as Operator's blind test, the prediction of the ML algorithm shows a good accuracy in its ability to capture historical failures ranging between days to months in advance. The Well Level Failure model captures failure prone wells with a precision of 90% and accuracy of 76%. The Component Level Failure model correctly identifies pump failure from other failures with a precision of 92% and accuracy of 88%. These numbers show the reliability of future predictions that could enable users to make high stake workover and operating envelope optimization decisions with confidence. Following benefits are estimated from both Well failure and Pump Component failure prediction models metrics respectively: 28.35% savings from total unscheduled ESP deferment1% increase in Overall Mean Time to Failure (MTTF) based on optimization of predicted pump component failure wells. In an organization where over thousand ESP wells are managed by limited production engineers, post ESP failure, the effort invested for hoist scheduling, raising new well proposal, rig mobilization, new ESP installation and commissioning utilizes huge time and leads to long undesired oil deferment. Implementation of engineered analytics to predict ESP failures and failed components in advance can support production engineers to plan early for workover operations, increase well run life and minimize oil deferment losses. Methodologically assessed by Senior Petroleum Engineers in selected clusters (using historical data and in the context of each failure and non-failure cases), the Predictive Analytics journey has started. It is ready to be operationalized at a small scale to build confidence as an advisory tool for Production Engineers in real-time to evaluate multiple wells’ failure probability on a daily basis and generate massive savings from well deferment. This agile journey focused on value generation is achieved with combined efforts between technology, domain knowledge and data.

Predictive Approach to Perform Fault Detection in Electrical Submersible Pump Systems

“…6 Guo et al 7 used the support vector method to predict anomalous operation. Andrade Marin et al 8 analyzed random forest to obtain a high value of accuracy and recall of ESP failure prediction in 165 cases. Bhardwaj et al 9 applied the principal component analysis (PCA) and gradient boosting algorithm (XGBoost) for anomaly detection and failure prediction in six cases and two samples.…”

“…Testing Results and Discussion. A confusion matrix was used to summarize the performance of the robust PCA model, showing what the prediction results were getting right and what types of errors the prediction results were making.There were four possible binary classification outcomes in a confusion matrix 8. …”