Predictive Analytics and Generative AI in Oil & Gas: Transforming Wellbore Stability and Hazard Detection

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@article{1706019,
      author = {Sridevi Kakolu, Muhammad Ashraf Faheem},
      title = {Predictive Analytics and Generative AI in Oil & Gas: Transforming Wellbore Stability and Hazard Detection},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {8},
      number = {1},
      pages = {635-649},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1706019.pdf},
      abstract = {Today is the age of digital transformation in the entire oil and gas industry through analytical prediction and generative AI. These sophisticated technologies are gradually being employed to optimize key problems impacting well construction, amongst them being wellbore stability, danger recognition, and decision-making. This is because predictive analytics make it possible for operators to forecast the occurrence of problems and protect the equipment frequently to reduce incidences of failure, making safety a fundamental aspect of the equipment. On the other hand, generative AI imitates actual-life scenarios, which creates an understanding of the optimal drilling, production, and maintenance strategy. Altogether, the specified technologies provide considerable advantages: increased operational efficiency, decreased cost, and increased safety. However, a set of questions need to be addressed to achieve the integration: data quality issues, cybersecurity issues, and the need to train the employees in new skills. This paper discusses the possibilities, advantages, difficulties, and future of predictive analytics and generative AI in the oil and gas industry and their opportunities for change and value generation.},
      keywords = {Predictive Analytics, Generative AI, Oil and Gas Industry, Wellbore Stability, Hazard Detection, Digital Transformation},
      month = {July},
  }