Lowering Carbon Foot-Print by Increasing Operational Efficiency Using Adaptive Machine Learning

Abstract

It is essential to meet climate goals outlined in the Paris Agreement that the Oil and Gas industry reduces carbon emissions along with achieving production targets. The described body of work herein provided a machine learning (ML) framework to predict upcoming shutdown events which often resulted in event driven flaring with increased carbon emission. Such flaring events are common and many times are not analyzed for root cause until unless they start to become bigger problems or occurred numerous times. One of the primary reasons for event driven flaring is poor process and equipment reliability. Therefore, ML framework is focused towards developing a tool to improve system reliability. Some of the challenges predicting these events are changing process, equipment and operating conditions and highly interactive processes which often results in generating false alarms. To address this issue, this paper proposes an adaptive ML framework where principle component analysis (PCA) is used for anomaly detection. PCA model is updated every hour so that it can update the model with changing operating conditions. PCA model utilizes sensor data from upstream and downstream of the process under consideration along with available equipment data to predict shutdown event. In the current work, three level of alarms are generated using threshold levels corresponding to 95%, 99% and 99.9% of confidence limits for D-statistics on the training data. These alarms correspond to 1) low risk (only reports and no urgency to act), 2) moderate risk (event review is must) and 3) high risk (urgency to act) respectively. The ML model was also able to identify top N sensors which contribute towards shutdown event. The solution was applied to an offshore compressor train and it was found that without using adaptive framework, only 70% of shutdown events can be predicted with lot of false alarms. Using adaptive framework all of the shutdown events were predicted. Although, false alarms were predicted, however the frequency at which false alarms were generated was found low and acceptable for practical application. Two case studies were presented which revealed that adaptive ML framework was found effective to capture events several hours in advance. Root-cause analysis was automated using contribution charts and top 4 key sensors were identified which were contributing towards various shutdown events and were resulting in 1 to 10 hours of flaring. After root-cause analysis for each event, a mitigation solution was presented to avoid similar repeated shutdowns. Several process and operational setting changes were identified to avoid future shutdowns caused by compressor train. Overall, annually 46MMSCF reduction in flare volume and 30-thousand-barrel reduction in lost oil production volume was identified.