Abstract
Deepwater wells possess a high risk of sand escaping the reservoir into the production systems. Sand production is a common operational issue which results in potential equipment damage and hence product contamination. Excessive sand erosion causes blockage in tubulars and cavities in downhole equipment (subsea valves, chokes, bends etc.), resulting in maintenance costs for subsea equipment that adds up to millions of dollars yearly to operators.
In this work, a scalable Machine Learning (ML) model readily accessing historical and real-time feed of sensor and simulation data is built to develop a predictive solution. Deployed workflow can inform Control Room Operators before significant damage occurs. An anomaly detection architecture, a common unsupervised learning framework for maintenance analytics, is deployed. Anomaly detection models include methods within the scope of dimensionality reduction. Principle Component Analysis (PCA) and Long Short-Term Memory (LSTM) Autoencoders are deployed to tackle the problem through reconstruction of the original input. During the workflow, a threshold is calculated after batch training and passed along with anomaly error scores in real-time. An alarm is triggered once the real-time anomaly score passes the threshold calculated during batch training. ML outputs are streamlined in near real-time to the database.
In this study, deployed ML model performance is benchmarked against a GOM Deepwater well where sanding is known to occur often. The ML Model architecture can process data that is captured by OSI PI historian, predict anomalous sanding events in advance, and is shown to be scalable to other wells in GOM. It is noted from this study that streamlined ML architecture and outputs simplify exploratory data analysis and model deployment across Onshore and Offshore Business Units. In addition, sanding stakeholders are notified in advance and can take early mitigative action before significant damage to wellhead or downhole equipment occurs instead of reacting to a possible sanding event offshore. The novelty of the utilized ML algorithm and process is in the ability to predict sanding anomalies in advance through ML batch training, infer prediction values near real-time, and scale to other assets.
Challenges in Machine Learning based approaches for Real-Time Anomaly Detection in Industrial Control Systems