Contributions of machine learning to quantitative and real-time mud gas data analysis: A critical review. (December 2022)

• Record Type: Journal Article
• Authors: Anifowose, Fatai; Mezghani, Mokhles; Badawood, Saleh; Ismail, Javed
• Is Part Of: Applied computing and geosciences. Volume 16(2022)
• Publication Date: 2022-12
• Subjects: Mud gas data -- Reservoir characterization -- Machine learning -- Formation evaluation; ppm parts per million -- ML machine learning -- API Americal Petroleum Institute -- GOR gas-oil ratio -- GDM geological differential method -- XRD X-ray diffraction -- PVT pressure-volume-temperature -- ECD equivalent circulating density -- LWD logging-while-drilling -- MWD measurement-while-drilling -- ROP rate of penetration -- NMR nuclear magnetic resonance; Earth sciences -- Data processing -- Periodicals; 550.285
• DOI: 10.1016/j.acags.2022.100095
• Languages: English
• ISSNs: 2590-1974

Abstract

Abstract: The current utility of mud gas data is typically limited to geological and petrophysical correlation, formation evaluation, and fluid typing. A critical and comprehensive review of the literature on mud gas data revealed that the mud gas data is abundantly acquired during drilling but not sufficiently utilized in real time. There is the need to leverage the current advances in machine learning technology and the race towards the digital transformation of the petroleum industry to create new opportunities for more extensive utility of mud gas data. Now that data is the new "oil" or "gold", the utility of the rich and abundant mud gas data could be explored for real-time applications. Such new possibilities are capable of adding more value to the reservoir characterization workflow ahead of geophysical logging, geological core data analysis, and well testing. Achieving this will facilitate early decision-making, improve safety, reduce nonproductive time, and ultimately accelerate the attainment of the digital transformation objective of the petroleum industry. We conclude with identifying possible future directions for the ultimate attainment of maximizing the utility of mud gas data through real-time and more advanced applications.