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. Graphical abstract

This study conducts saturation modeling in a gas hydrate (GH) sand sample with X-ray CT images using the following machine learning algorithms: random forest (RF), convolutional neural network (CNN), and support vector machine (SVM). The RF yields the best prediction performance for water, gas, and GH saturation in the samples among the three methods. The CNN and SVM also exhibit sufficient performances under the restricted conditions, but require improvements to their reliability and overall prediction performance. Furthermore, the RF yields the lowest mean square error and highest correlation coefficient between the original and predicted datasets. Although the GH CT images aid in approximately understanding how fluids act in a GH sample, difficulties were encountered in accurately understanding the behavior of GH in a GH sample during the experiments owing to limited physical conditions. Therefore, the proposed saturation modeling method can aid in understanding the behavior of GH in a GH sample in real-time with the use of an appropriate machine learning method. Furthermore, highly accurate descriptions of each saturation, obtained from the proposed method, lead to an accurate resource evaluation and well-guided optimal depressurization for a target GH field production.

This paper presents a review of 71 research papers related to a distance-based clustering (DBC) technique for efficiently assessing reservoir uncertainty. The key to DBC is to select a few models that can represent hundreds of possible reservoir models. DBC is defined as a combination of four technical processes: distance definition, distance matrix construction, dimensional reduction, and clustering. In this paper, we review the algorithms employed in each step. For distance calculation, Minkowski distance is recommended with even order due to sign problem. In the case of clustering, K-means algorithm has been commonly used. DBC has been applied to various reservoir types from channel to unconventional reservoirs. DBC is effective for unconventional resources and enhanced oil recovery projects that have a significant advantage of reducing the number of reservoir simulations. Recently, DBC studies have been performed with deep learning algorithms for feature extraction to define a distance and for effective clustering.

It is necessary to monitor, acquire, preprocess, and classify microseismic data to understand active faults or other causes of earthquakes, thereby facilitating the preparation of early-warning earthquake systems. Accordingly, this study proposes the application of machine learning for signal–noise classification of microseismic data from Pohang, South Korea. For the first time, unique microseismic data were obtained from the monitoring system of the borehole station PHBS8 located in Yongcheon-ri, Pohang region, while hydraulic stimulation was being conducted. The collected data were properly preprocessed and utilized as training and test data for supervised and unsupervised learning methods: random forest, convolutional neural network, and K-medoids clustering with fast Fourier transform. The supervised learning methods showed 100% and 97.4% of accuracy for the training and test data, respectively. The unsupervised method showed 97.0% accuracy. Consequently, the results from machine learning validated that automation based on the proposed supervised and unsupervised learning applications can classify the acquired microseismic data in real time.