Geoenergy Science and Engineering最新文献

{"title":"Sweet spots discrimination in carbonate reservoirs based on weakly supervised learning","authors":"Han Wang ,&nbsp;Zhiwen Xue ,&nbsp;Shengjuan Cai ,&nbsp;Zhijiang Kang ,&nbsp;Hanqing Wang ,&nbsp;Yitian Xiao","doi":"10.1016/j.geoen.2025.213965","DOIUrl":"10.1016/j.geoen.2025.213965","url":null,"abstract":"<div><div>The spatial distribution of karst caves in carbonate reservoirs plays a key role in guiding well placement. However, field production data reveal a high risk of drilling low-production wells even in cave-dense regions, resulting in low hydrocarbon recovery rates. Consequently, the identification of high-production “sweet spot” reservoirs has become a priority in optimizing well placement and enhancing recovery. This study proposes a two-step method for sweet spot identification using weakly supervised learning. Firstly, a multi-input convolutional neural network (CNN) is employed to detect caves from seismic data, including depth migration data, ant tracking, impedance, and structure tensor seismic attributes. The detection results, along with the seismic data, are then input into a second CNN to predict reservoir effectiveness. Since the effective reservoir classification can only be validated through production data from drilled wells, the available training samples are limited. To address this limitation, we define a random path crossing multiple wells and extract corresponding 2D seismic profiles, cave detection labels, and well-controlled classification labels. Notably, classification labels are only available at well locations, with no labels between wells. In the reservoir classification phase, a weakly supervised 2D CNN is trained using an adaptive loss, which evaluates the output cave classification profiles at partially labeled targets. The CNN can generate consistent 3D sweet spot predictions along both inline and crossline sections. Field tests and case studies demonstrate the prediction accuracy of proposed workflow can reach approximately 80 %, providing a practical solution for drilling risks and optimizing hydrocarbon recovery in carbonate reservoirs.</div></div>","PeriodicalId":100578,"journal":{"name":"Geoenergy Science and Engineering","volume":"252 ","pages":"Article 213965"},"PeriodicalIF":0.0,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143949110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and evaluation of a solid-liquid composite CO2 adsorbent using rice husk from waste distiller's grains","authors":"Renjing Ji ,&nbsp;Xiaorong Yu ,&nbsp;Jingyi Cui ,&nbsp;Xuemei Wu ,&nbsp;Huan Yang ,&nbsp;Gaoshen Su","doi":"10.1016/j.geoen.2025.213967","DOIUrl":"10.1016/j.geoen.2025.213967","url":null,"abstract":"<div><div>As a solid waste in Baijiu production, rice husk of distiller's grains is considered as a low-cost biomass resource. To promote the resource utilization of distiller's grains, the hydrophobic modified rice husk biochar (DRHC) was prepared from solid waste of distiller's grains as precursor. DRHC was doped into hydrophobic silica as a solid phase material and an aqueous solution of 50 wt% K₂CO₃ was chosen as a liquid phase material. The solid-liquid composite adsorbents, prepared using a high-speed stirring method, exhibited a core-shell structure with a particle size of 52.98 μm. The adsorption capacity of the solid-liquid composite adsorbent increased as the addition of DRHC in the solid phase shell rose. The adsorption capacity of the composite adsorbent prepared with pure hydrophobic SiO₂ was 1.49 mmol/g, while the composite adsorbent had a capacity of up to 5.2 mmol/g when the addition of DRHC in the shell was 20 wt%. After 10 absorption-desorption cycles, the adsorption capacity of the composite adsorbent decreased by 13.8 %. The solid-liquid composite adsorbent could achieve the synergistic adsorption of the shell and the core. In this study, liquid absorbent and solid adsorbent were combined in a simple way, and the composite adsorbent effectively improved the adsorption capacity of CO₂, and also provided a new idea for the high-value utilization of distiller's grains.</div></div>","PeriodicalId":100578,"journal":{"name":"Geoenergy Science and Engineering","volume":"252 ","pages":"Article 213967"},"PeriodicalIF":0.0,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigation of formation water evaporation behavior and its impact on CO2 storage within aquifers","authors":"Fuzhen Chen ,&nbsp;Lijuan Yang ,&nbsp;Xiaohu Bai ,&nbsp;Pengfei Liu ,&nbsp;Zhihao He ,&nbsp;Jianwei Gu","doi":"10.1016/j.geoen.2025.213974","DOIUrl":"10.1016/j.geoen.2025.213974","url":null,"abstract":"<div><div>Evaporation is one of the primary mechanisms in subsurface fluid migration, prevalent in gas-liquid multiphase flow processes within porous media. However, less emphasis has been placed on fluid evaporation during its flow from well to formation. This study investigates the behavior of formation water evaporation and its impact on CO₂ storage through high-volume CO₂ displacement experiments coupled with online nuclear magnetic resonance testing. The retrograde crossover phenomenon of water recovery under varying temperatures is observed during high-volume CO₂ displacement. The color change in silica gel provides clear evidence of formation water evaporation which leads to the crossover. Initially, formation water migration is primarily driven by CO₂ displacement; however, as gas saturation exceeds 40 %, evaporation replacing displacement becomes the dominant migration mechanism. The primary migration mechanism shifts during this process. Evaporation typically occurs at inlet of the core, or near-wellbore area in field applications. Pronounced CO₂ override flow phenomenon is observed, which significantly enhances the water evaporation and gas channeling in the upper part of porous media. A sufficient cumulative CO₂ injection volume is necessary for significant formation water evaporation. Increasing temperature within an enclosed space does not significantly enhance water evaporation. Conversely, both isothermal depressurization and vacuum evacuation with an open boundary can markedly increase water evaporation. These outcomes document that open boundary, fluid flow, and high-volume CO₂ injection are prerequisites for effective formation water evaporation. Furthermore, high formation temperature, large pressure difference, and slow injection speed promote earlier and more intense formation water evaporation. The effects of evaporation on filtration are twofold: on one hand, the reduction in irreducible water saturation enhances permeability; on the other hand, salt precipitation resulting from evaporation decreases permeability. Therefore, rationally utilizing formation water evaporation mechanism can lower flow resistance near wellbore, reduce injection pressure, improve sweep efficiency, and increase CO₂ storage capacity.</div></div>","PeriodicalId":100578,"journal":{"name":"Geoenergy Science and Engineering","volume":"252 ","pages":"Article 213974"},"PeriodicalIF":0.0,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143949109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A hybrid model of formation pore pressure prediction based on geological sequence matching","authors":"Chengkai Weng ,&nbsp;Jun Li ,&nbsp;Hongwei Yang ,&nbsp;Zhenyu Long ,&nbsp;Geng Zhang ,&nbsp;Biao Wang ,&nbsp;Yuxuan Zhao","doi":"10.1016/j.geoen.2025.213972","DOIUrl":"10.1016/j.geoen.2025.213972","url":null,"abstract":"<div><div>Formation pore pressure (Pp) is vital to every stage of petroleum exploration and development. However, current prediction methods often overlook geological sequence variations and rely heavily on direct depth alignment from offset wells, resulting in substantial discrepancies between predicted and actual Pp when measured data are scarce. To address these limitations, a novel and interpretable pre-drilling Pp prediction strategy was developed. First, Geological Sequence Matching (GSM) was introduced to align historical well-logging data with the pre-drilling well's stratification, thereby compensating for stratigraphic depth-thickness discrepancies induced by geological evolution—an essential factor systematically neglected in existing approaches. Second, a primary wave velocity (Vp) Error Compensation Hybrid (VECH) model was proposed, which uniquely combines a Vp-based physical model as the primary framework while employing machine learning specifically to correct systematic errors. Unlike purely machine-learning-based or traditional physical methods, VECH maintains robust physical interpretability while effectively incorporating real-world data corrections. By leveraging Vp to calculate effective stress, this approach eliminates the need for post-drilling corrected Pp in model training, overcoming a critical drawback of conventional workflows. Examples from the Bohai Oilfield show that, compared to traditional methods, the proposed hybrid model reduces the mean absolute error in Pp prediction by two-thirds. Furthermore, VECH model interpretation using decision-tree visualization and sensitivity analysis is performed to illustrate the model operation process and the influence of various features on the prediction outcomes. These findings demonstrate the effectiveness of the hybrid model in predicting Pp suggests potential applications in forecasting other geophysical parameters such as density, porosity, and permeability.</div></div>","PeriodicalId":100578,"journal":{"name":"Geoenergy Science and Engineering","volume":"252 ","pages":"Article 213972"},"PeriodicalIF":0.0,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143949118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sand production during hydrocarbon exploitation: Mechanisms, factors, prediction, and perspectives","authors":"Haoze Wu ,&nbsp;Shui-Long Shen ,&nbsp;Annan Zhou","doi":"10.1016/j.geoen.2025.213954","DOIUrl":"10.1016/j.geoen.2025.213954","url":null,"abstract":"<div><div>Sand production poses significant challenges for hydrocarbon extraction, particularly in weakly consolidated reservoirs and unconventional formations. This bibliometric analysis highlights the growing focus on sand production, showcasing the relevant advancements in computational methods, geomechanics, and artificial intelligence (AI) applications. Significant gaps remain in understanding multiphysics coupling, mechanical failure, and erosion, and in integrating risk assessment indices with AI-based approaches. This review paper provides a comprehensive examination of sand production mechanisms. Specifically, it investigates the roles of multiphysics coupling, mechanical failure, and erosion processes. In addition, key influencing factors such as reservoir characteristics, production strategies, and completion methods are evaluated. Key risk assessment indices are summarized to provide guidance for operational decision-making. To address the limitations of the traditional experimental, theoretical, and numerical approaches, this study provides an in-depth evaluation of AI-based methods, including machine learning and expert systems. By validating these methods across production-scale and laboratory-scale datasets, this review demonstrates their superior predictive accuracy and capacity to capture the non-linear interactions governing sand production. A conceptual framework was proposed that emphasises the integration of AI with real-time monitoring to enable adaptive and efficient sand production management. This review bridges the existing knowledge gaps and provides practical insights for improving the safety and sustainability of hydrocarbon recovery.</div></div>","PeriodicalId":100578,"journal":{"name":"Geoenergy Science and Engineering","volume":"252 ","pages":"Article 213954"},"PeriodicalIF":0.0,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Wellbore stability prediction method based on intelligent analysis model of drilling cuttings logging images","authors":"Wenhe Xia ,&nbsp;Yindong Tang ,&nbsp;Gao Li ,&nbsp;Chongxing Yue ,&nbsp;Yujiao Han ,&nbsp;Xiongjun Wu ,&nbsp;Shiyang Fan","doi":"10.1016/j.geoen.2025.213961","DOIUrl":"10.1016/j.geoen.2025.213961","url":null,"abstract":"<div><div>At present, drilling sites usually rely on rock mechanics analysis results to predict wellbore stability, which takes a long time. Therefore, this study attempts to use real-time drilling cuttings logging image data to characterize the results of rock mechanics analysis, so that drilling cuttings logging has the function of predicting wellbore stability. The study established an image sample library consisting of 16 types of drilling cavings shapes and lithology, and improved ShuffleNetV2 network as the basic architecture to form an intelligent prediction model. In order to enhance the network's attention to the iconic feature information of drilling cavings images, XConv convolutional kernel parallel branches and SimAM attention mechanism modules were introduced into the Shuffle unit. In order to preserve key features of drilling cavings contours, a multi-channel feature fusion algorithm was designed for Stage2, Stage3, and Stage 4 stages in ShuffleNetV2 network. The final improved ShuffleNetV2 network model has a recognition accuracy of 90.56 % for the shape and lithology of drilling cavings. The effectiveness of the on-site application of Fenggu ∗ Well has verified the reliability of this method. The time from input of returned cuttings images to output of results is less than 1 s, and the recognition and prediction results are basically consistent with geological data and construction process conditions. This fully demonstrates that this method can meet the needs of rapid perception and prediction of wellbore stability on site.</div></div>","PeriodicalId":100578,"journal":{"name":"Geoenergy Science and Engineering","volume":"252 ","pages":"Article 213961"},"PeriodicalIF":0.0,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143927969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-phase segmentation methods for micro-tomographic images based on deep learning","authors":"Yanbin Yu ,&nbsp;Wei Wei ,&nbsp;Wenting Cui ,&nbsp;Weimin Cheng ,&nbsp;Jie Zang ,&nbsp;Lianxin Fang ,&nbsp;Lei Zheng","doi":"10.1016/j.geoen.2025.213962","DOIUrl":"10.1016/j.geoen.2025.213962","url":null,"abstract":"<div><div>Micro-tomography enables the acquisition of three-dimensional images of the internal microstructure of coal, which provides essential information for reservoir evaluation, mining planning, and coalbed methane extraction. However, the intricate issue of multi-phase segmentation in microscopic tomographic images has significantly hindered the efficient advancement of subsequent research endeavors. Traditional segmentation methodologies, which necessitate manual labor, are not only time-consuming and arduous but also inherently prone to errors, thereby failing to align with the contemporary industrial demands for high precision and efficiency. Therefore, the efficient and accurate segmentation of these complex micro-tomographic images, particularly the achievement of multi-phase segmentation, is of urgent necessity. To accurately and swiftly establish digital core images of multi-component coal, in this paper, we propose a novel multi-phase segmentation system for micro-tomography images, leveraging deep learning algorithms Utilizing coal CT images as the primary dataset and incorporating interactively threshold-segmented images as labels, we innovatively employ the U-Net model for automated segmentation training. Through rigorous experimental validation and analysis, the trained U-Net model demonstrates exceptional performance in mineral content identification, morphological feature extraction, and spatial structure analysis. When compared to traditional methods, the error rate is markedly decreased, and segmentation efficiency is enhanced by an order of magnitude. This innovative approach transcends the constraints of traditional manual segmentation. Leveraging the robust feature-learning capabilities of deep neural networks, it facilitates intelligent and rapid conversion from raw grayscale images to multi-component images, substantially improving segmentation accuracy and efficiency. This technique addresses the technological gap in swiftly and precisely constructing multi-component digital core images, offering a novel technical pathway for detailed reservoir evaluation, scientific mining plan development, and efficient coalbed methane extraction.</div></div>","PeriodicalId":100578,"journal":{"name":"Geoenergy Science and Engineering","volume":"252 ","pages":"Article 213962"},"PeriodicalIF":0.0,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143928013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating the potential distribution of deep geothermal resources in the North Jiangsu Basin, East China, using machine learning","authors":"Jinhui Luo ,&nbsp;Zhenghui Qu ,&nbsp;Junpeng Guan ,&nbsp;Yuhua Chen ,&nbsp;Yibo Wang ,&nbsp;Wangyan Zhou ,&nbsp;Yayun Hu ,&nbsp;Huashi Zhang ,&nbsp;Tian Liang ,&nbsp;Guoqiang Fu ,&nbsp;Jin Qian","doi":"10.1016/j.geoen.2025.213957","DOIUrl":"10.1016/j.geoen.2025.213957","url":null,"abstract":"<div><div>Substantial reserves of deep geothermal resources (DGRs) in the North Jiangsu Basin (NJB) offer considerable potential for energy supply in eastern China. However, the complicated geological structure and miscellaneous influencing factors associated with DGRs present challenges for ascertaining their potential distribution. In this paper, we introduce an enhanced evaluation index system, wherein nine indices are organized into three categories: geophysical presentations, tectonic and magmatic activities, and geothermal indicators, to highlight the distinctive characteristics of deep geothermal energy. Subsequently, we applied a machine learning approach, specifically the MaxEnt model, to quantify the probability distribution of DGRs within the NJB. The results demonstrate that the Jianhu Uplift, situated in the central region of the NJB, is the most favorable area for DGR development. In addition, the southwestern region of Huai'an, the northern area of Taizhou, and the eastern coastal zone of the basin were identified as primary potential areas for DGRs. The distribution of these promising areas was predominantly influenced by the distance from deep-large faults. The depth of high-conductivity and low-velocity bodies emerged as the second most significant factor, followed by the P-wave velocity distribution. Collectively, these three factors account for over 60 % of the impact on DGRs' distribution. These findings provide robust quantitative evidence for the optimization of favorable areas for DGR development. They also suggested that our methodology is effective in maximizing spatial distribution inference with limited data, offering considerable merits and promising prospects for geoscientific research in data-scarce environments.</div></div>","PeriodicalId":100578,"journal":{"name":"Geoenergy Science and Engineering","volume":"252 ","pages":"Article 213957"},"PeriodicalIF":0.0,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143927561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Numerical simulation of liquid-solid two-phase flow in cementing displacement based on kinetic theory of granular","authors":"Xin Yang ,&nbsp;Jinfei Sun ,&nbsp;Guanyi Zheng ,&nbsp;Zaoyuan Li ,&nbsp;Jin Li ,&nbsp;Yue Shi ,&nbsp;Fujie Yang ,&nbsp;Qianmei Luo","doi":"10.1016/j.geoen.2025.213956","DOIUrl":"10.1016/j.geoen.2025.213956","url":null,"abstract":"<div><div>Ensuring displacement efficiency is a prerequisite for improving the quality of primary cementing of horizontal Wells. At present, most of the research focuses on the displacement rule between fluids (displacing fluid and displaced fluid). However, in actual cementing conditions, there are not only fluids but also cuttings in the annular. This paper proposes a liquid-solid two-phase flow displacement model based on the kinetic theory of granular flow. Displacement efficiency and cuttings migration in the horizontal annulus are evaluated by the computational fluid dynamics method. The results show that increasing the casing rotation speed makes the annular fluid and cutting axial velocity distribution more uniform. When the eccentricity is 0.6 and the casing rotation is increased from 0 rpm to 30 rpm, the displacement efficiency reaches 94.21 %, an increase of 6.01 %, and the volume fraction of cuttings is reduced by 2.72 %. When the yield stress of drilling fluid is less than 3.0 Pa, the axial velocity of the narrow annular fluid increases significantly, the displacement efficiency exceeds 90 %, and the volume fraction of cuttings decreases by 2.5 %. With the increase of displacement, the axial velocity of the annular flow field increases significantly. When the displacement reaches 2.4 m³/min, the displacement efficiency increases to 92.8 %, and the volume fraction of cuttings decreases to 0.9 %. The research results are helpful for better understanding the complex flow problems of the liquid-solid phase in the annulus. They can provide a theoretical basis and reference for optimizing the parameters of horizontal well cementing.</div></div>","PeriodicalId":100578,"journal":{"name":"Geoenergy Science and Engineering","volume":"252 ","pages":"Article 213956"},"PeriodicalIF":0.0,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Geometry inversion model for sediment zones of salt cavern used for natural gas storage","authors":"Tingting Jiang ,&nbsp;Ziqi Chi ,&nbsp;Dongzhou Xie ,&nbsp;Tao He ,&nbsp;Dongling Cao","doi":"10.1016/j.geoen.2025.213959","DOIUrl":"10.1016/j.geoen.2025.213959","url":null,"abstract":"<div><div>The utilization of sediment pore space for gas storage is an effective measure to solve the problem of high-impurity bedded salt rock gas storage construction in China. However, the existing detection methods cannot obtain the cavern geometry at the sediment zones within the gas storage, which brings significant uncertainties to the debrining process. In response to this issue, this paper innovatively establishes a model that considers sediment porous media flow for the inversion of cavern geometry and applies the model to analyze the factors that influence the debrining parameters. The results show that the average error obtained from the model inversion calculations is less than 2 % for the cavern diameter, while for predicting the depth of the gas-brine interface, the average error is less than 2.5 %. Moreover, the research indicates that the cavern geometry, the sediment surface position, the debrining rate, and the permeability of the sediments influence the debrining process. The specific change patterns of debrining parameters are mainly influenced by the sediment surface position and the cavern geometry. This paper can provide theoretical support for the utilization of sediment pores for debrining and gas storage.</div></div>","PeriodicalId":100578,"journal":{"name":"Geoenergy Science and Engineering","volume":"252 ","pages":"Article 213959"},"PeriodicalIF":0.0,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143927562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}