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Artificial intelligence (AI) and machine learning (ML) offer solutions for mature oil and gas fields. This study explores AI and ML applications for predicting well integrity failures, identifying a significant literature gap.

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Area of Science:

  • Petroleum Engineering
  • Data Science
  • Artificial Intelligence

Background:

  • Mature oil and gas fields face increasing well integrity challenges.
  • Declining oil markets and operational risks necessitate advanced solutions.
  • Big data and computing power are transforming industrial operations.

Purpose of the Study:

  • To comprehensively review AI and ML applications in the petroleum industry.
  • To focus on classifying well integrity failures using AI and ML.
  • To identify and address the gap in predicting well integrity failures with AI/ML.

Main Methods:

  • Categorization of AI and ML applications by discipline and publication year.
  • Discussion of data preprocessing techniques: gathering, cleaning, and feature engineering.
  • Comparison and evaluation of different machine learning models for performance.

Main Results:

  • AI and ML potential in predicting well integrity failures is currently underutilized.
  • A comprehensive reference of AI/ML applications in O&G is provided.
  • A foundation for developing ML programs for well integrity risk management is established.

Conclusions:

  • There is a clear need for more research into AI/ML for well integrity prediction.
  • The developed models offer a simple, quick, and objective alternative to subjective assessments.
  • The framework can be adapted for diverse well types and field-specific risk profiles.