Field data analysis and risk assessment of gas kick during industrial deepwater drilling process based on supervised learning algorithm

During industrial offshore deep-water drilling process, gas kick event occurs frequently due to extremely narrow Mud Weight (MW) window (minimum 0.01sg) and negligible safety margins for the well control purposes. Further, traditional gas kick detection methods in such environments have significant time-lag and can often lead to severe well control issues, and occasionally to well blowouts or borehole abandonment. In this study, firstly, the raw field data is processed through data collection, data cleaning, feature scaling, outlier detection, data labeling and dataset splitting. Additionally, a novel data labeling criterion for gas kick risks is proposed where five kick risks (Indicated by different colors in this study) are defined based on three key indicators: differential flow out (DFO), kick gain volume (Vol), and kick duration time (Time). Kick risk status represents one of the following cases: Case 0 - No indicators are activated (Green), Case 1 - Multi-drilling parameters deviation or DFO is activated (Orange), Case 2 - DFO and Vol are simultaneously activated (Light Red), Case 3 - DFO and Time are simultaneously activated (Light Red), Case 4 - DFO, Vol and Time alarms are simultaneously activated (Dark Red). Then, a novel data mining method using Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) is presented for early detection of gas kick events by analyzing time series data from field drilling process. The network parameters such as number of hidden layers and number of neurons are initialized to build the LSTM network. The learned LSTM model is evaluated using the testing set, and the best LSTM model (six (6)-layers eighty (80)-nodes (6 L*80 N)) is optimally selected and deployed. The accuracy of deployed LSTM model is 87 % in the testing dataset, which is reliable enough to identify the kick fault during the deep-water drilling field operation. Lastly, the LSTM model detected the gas kick events earlier than the Tank Volume detection method in several representative case studies to conclude that the application of LSTM model can potentially improve well control safety in the deep-water wells with narrow MW windows. (C) 2020 Published by Elsevier B.V. on behalf of Institution of Chemical Engineers.

Automated summary

A novel data mining method using Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) is proposed for early detection of gas kick events by analyzing time series data from field drilling process. The LSTM model achieved an accuracy of 87% in the testing dataset and detected gas kick events earlier than the Tank Volume detection method, showing potential for improving well control safety in deep-water wells with narrow Mud Weight windows.