Contributions of machine learning to quantitative and real-time mud gas data analysis: A critical review
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 sufficient...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-12-01
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| Series: | Applied Computing and Geosciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590197422000179 |
| _version_ | 1811310826298015744 |
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| author | Fatai Anifowose Mokhles Mezghani Saleh Badawood Javed Ismail |
| author_facet | Fatai Anifowose Mokhles Mezghani Saleh Badawood Javed Ismail |
| author_sort | Fatai Anifowose |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T10:05:55Z |
| format | Article |
| id | doaj.art-057e77376f0f4421a7f0a0c11d99676a |
| institution | Directory Open Access Journal |
| issn | 2590-1974 |
| language | English |
| last_indexed | 2024-04-13T10:05:55Z |
| publishDate | 2022-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Applied Computing and Geosciences |
| spelling | doaj.art-057e77376f0f4421a7f0a0c11d99676a2022-12-22T02:51:04ZengElsevierApplied Computing and Geosciences2590-19742022-12-0116100095Contributions of machine learning to quantitative and real-time mud gas data analysis: A critical reviewFatai Anifowose0Mokhles Mezghani1Saleh Badawood2Javed Ismail3Corresponding author.; Saudi Arabian Oil Company Dhahran, Saudi ArabiaSaudi Arabian Oil Company Dhahran, Saudi ArabiaSaudi Arabian Oil Company Dhahran, Saudi ArabiaSaudi Arabian Oil Company Dhahran, Saudi ArabiaThe 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.http://www.sciencedirect.com/science/article/pii/S2590197422000179Mud gas dataReservoir characterizationMachine learningFormation evaluation |
| spellingShingle | Fatai Anifowose Mokhles Mezghani Saleh Badawood Javed Ismail Contributions of machine learning to quantitative and real-time mud gas data analysis: A critical review Applied Computing and Geosciences Mud gas data Reservoir characterization Machine learning Formation evaluation |
| title | Contributions of machine learning to quantitative and real-time mud gas data analysis: A critical review |
| title_full | Contributions of machine learning to quantitative and real-time mud gas data analysis: A critical review |
| title_fullStr | Contributions of machine learning to quantitative and real-time mud gas data analysis: A critical review |
| title_full_unstemmed | Contributions of machine learning to quantitative and real-time mud gas data analysis: A critical review |
| title_short | Contributions of machine learning to quantitative and real-time mud gas data analysis: A critical review |
| title_sort | contributions of machine learning to quantitative and real time mud gas data analysis a critical review |
| topic | Mud gas data Reservoir characterization Machine learning Formation evaluation |
| url | http://www.sciencedirect.com/science/article/pii/S2590197422000179 |
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