Artificial intelligence in geoenergy: bridging petroleum engineering and future-oriented applications

Abstract

This study reviews 254 papers on artificial intelligence (AI) applications in the geoenergy sector, categorized into conventional and future-oriented technologies. Conventional geoenergy includes reservoir, production, and drilling, while future-oriented technologies cover geological CO2 storage (GCS), gas hydrates (GH), and underground hydrogen storage (UHS). The 254 papers were analyzed systematically based on authorship, publication year, key findings, input-output data relationships, applied AI methods, and sample sizes for AI training. Results highlight the extensive use of machine learning (ML) and deep learning (DL) for tasks such as proxy modeling, dimensionality reduction, data generation, and optimization. Proxy modeling was the most commonly applied method, effectively predicting key economic and engineering parameters. Future-oriented applications require integrating multiple factors for safety and efficiency, emphasizing the need for advanced AI techniques and larger datasets. Conventional studies used larger datasets (average 49,794 samples) compared to future-oriented applications (average 13,779 samples), reflecting their longer research history. The average input-output data sizes (log scale) for ML and DL were 2.01 and 0.34 vs. 4.12 and 2.00, respectively, showing DL’s versatility in handling larger datasets. As geoenergy systems grow more complex, advanced AI methods must address interconnected geo-scales from pore to field levels. Future research should focus on developing automated, user-independent AI systems to enhance model selection and broaden AI applications across diverse datasets, driving greater efficiency and innovation in the geoenergy sector. Explored 254 papers published since 2014 analyzing AI applications in geoenergy AI in geoenergy focuses on proxy modeling, dimension handling, and optimization Conventional geoenergy datasets are larger than those in unconventional sectors RF and SVM dominate ML, while MLP leads DL in geoenergy AI applications Future geoenergy AI should focus on enhancing connectivity across scales and systems

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