Artificial intelligence
(AI) and machine learning (ML) are transforming
industries, where low-cost, big data can utilize computing power to
optimize system performance. Oil and gas (O&G) fields are getting
mature, where well integrity (WI) problems become more common and
field operations are now more challenging. Hence, they are good candidates
for transformation due to the low cost of data storage, highlighting
the oil market decline, along with dynamic risk posed during operations.
This paper is presenting a comprehensive compilation of different
ML applications in diverse disciplines of the petroleum industry.
The pool of AI and ML with respect to different areas of applications
along with publication years has been categorized. The main focus
of this study is classifying well integrity failures where the authors
found that the potential of AI and ML in predicting well integrity
failures has not been efficiently tapped, and there is an explicit
gap in the literature. First, the applications of AI, ML, and data
analytics in the O&G industry are discussed thoroughly, so this
paper can be a comprehensive reference for readers and future researchers.
Then data preprocessing is explained. This includes data gathering,
cleaning, and feature engineering. Next, the different ML models are
compared and discussed. Finally, model performance evaluation and
best model selection are described. This study would be a concrete
foundation in the design and construction of ML programs that can
be deployed for WI risk management. The developed model can be simply
used for any well stock, providing quick and easy assessment instead
of subjective and tedious assessment. The layout can be simply adjusted
to reflect the risk profile of any well type or any field.