Accurate reservoir characterization remains a critical challenge during the early stages of reservoir development. While traditional methods have dominated the oil and gas industry, recent advancements in Artificial Intelligence (AI) and Machine Learning (ML) techniques have offered transformative potential for addressing the challenges posed by reservoir complexity and the high dimensionality of geological, geophysical, and petrophysical data. This review examines the application of AI and ML in reservoir characterization, with a specific focus on inter-well connectivity and communication, as well as identifying optimal new drilling locations, also known as sweet spots. It discusses how integrating diverse data sources using AI/ML significantly enhances the accuracy of reservoir property analysis and behavior predictions. Additionally, the review discusses coupling methodologies that integrate AI/ML techniques with traditional reservoir characterization methods. It highlights the several benefits and drawbacks of such methods compared to the conventional approaches. Finally, the review discusses emerging AI/ML methods and potential future directions to further improve the accuracy and reliability of reservoir characterization.
Kalule et al. (Fri,) studied this question.
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