Improving Deepwater Facility Uptime Using Machine Learning Approach

Abstract

Deepwater oil and gas facilities typically encounter on an average up to 5% annual production losses due to unplanned downtime, conservatively estimated at billions of dollars impact for the industry. The existing toolkit and systems in place are not always adequate to identify and predict abnormal events that could lead towards unplanned facility shutdown. The interaction amongst process sub-systems and disturbances that propagate across these sub-systems with changing operating conditions are hard to predict without a fit-for-purpose model (or a digital twin). The focus of current work is on deepwater facility having several oil export pipeline pumps in parallel and several gas compressors in series. The alarm database showed records of several unplanned shutdown events around these critical equipements that resulted in undesirable outcomes such as production deferment, complete facility shutdown, loss of sales volumes and increased operational costs. In this work, an intelligent prognostic solution is proposed using machine learning (ML) framework for automatic prediction of impending facility downtime, and identification of key causative process variables. A systematic workflow was developed to identify, cleanse and process real time data for both model training and prediction. Several ML methods were evaluated; anomaly detection based on Principal Component Analysis (PCA) and Autoencoder (AE) algorithms were found performing better for the type of data available for the deepwater facility. The ML framework also supported analysis of underlying downtime causes to propose suitable mitigation steps. Knowledge based on physical understanding of the process was used to select each sub-system boundary and sensor list on which ML model was trained. These models were then cross-validated to test the accuracy of trained models. Finally, the alarm database was used to confirm the accuracy of the machine leaning models and identify root-causes for unplanned shutdowns. If the operating condition changes over time, the anomaly detection based ML models were setup to adapt to changing conditions by automatic model updates, resulting in significant reduction in false alarms. The adaptive ML models, when applied to one of the sub-system (with 30 different sensor data), predicted 24 unplanned events in 6 months of period, while when applied to another sub-system (with 40 sensor data), predicted only 6 unplanned downtime events. Several predictions were found as early as 30 mins to 2 hours, providing adequate early warning to take proactive actions. Case studies shown in the paper present diagnostic charts and identified early indicators were found in agreement with pre-alarms generated by existing alarm system, thus validating the ML solution. Current toolkit available to identify anomalous process behavior is limited to exception based surveillance with fixed min-max limits on each sensor data. Therefore, proposed adaptive ML solution has shown potential to revolutionize the topside process surveillance. This paper also describes how the ML framework can be scaled for a sustainable solution that provides prediction every minute, keeps the model evergreen utilizing cloud-based model deployment platform to train, predict and trigger automatic model updates and also span multiple process systems and facilities. Finally, we present directions for future work, where the current model can keep predicting various events and over time when sufficient events are collected, more advanced machine learning methods based on supervised ML can be developed and deployed.

Unsupervised Methods to Classify Real Data from Offshore Wells

“…Thus, avoiding more complex issues. Several papers tackle machine learning applications for early fault detection [8] [9] [10] [11]. Besides early fault detection, data-driven methods can also perform fault diagnosis.…”