Energy Geoscience最新文献

{"title":"Hydrocarbon generation characteristics and potential of liptinite-rich coal in China","authors":"Yuting Yin ,&nbsp;Dongdong Wang ,&nbsp;Lei Lan ,&nbsp;Youchuan Li ,&nbsp;Haiyan Liu ,&nbsp;Zengxue Li ,&nbsp;Yan Liu","doi":"10.1016/j.engeos.2025.100413","DOIUrl":"10.1016/j.engeos.2025.100413","url":null,"abstract":"<div><div>Coal-measure source rocks may play an important role in hydrocarbon generation in petroliferous basins where coal seams are well developed. Hydrocarbon generation characteristics and potential of coal-measure source rocks have been well documented for continental petroliferous basins, while the understanding of coal-measure source rocks in offshore basins is yet to be delved into. Significant oil exploration breakthroughs have been made in the well-developed coal measures of Turpan–Hami Basin (THB), a typical continental petroliferous basin in northwestern China. In this study, a comparative analysis is conducted on the Paleogene coal seams in the Zhu I Depression (ZID), located in the northern part of the South China Sea, and the Jurassic coal seams in the THB in terms of genetic conditions, mineral composition, and hydrocarbon generation potential. The geological understandings are obtained as follows. Both the coal-forming periods during the deposition of the ZID and THB were of a warm and wet climate type. The Paleogene coal-forming environments during the deposition of the ZID mainly include peat swamp in the upper plain and interdistributary bays in the lower plain of the braided river delta, along with littoral shallow lakes. As a whole, the coal seams are characterized by multiple layers, thin single layer thickness and poor stability, while those in the upper plain peat swamp of the braided river delta have relatively larger single layer thickness but relatively fewer number of layers. The Jurassic coal-forming environments in the THB include peat swamp in the upper delta plain, lower delta plain, and inter-delta bay. The coal seams formed in the lower delta plain are the most stable, while those in the inter-delta bay are the thickest. The ZID coal has a higher vitrinite content (averaging 76.11 %) and liptinite content (averaging 10.77 %) compared to its THB counterpart, which has an average vitrinite content of 68.28 % and average liptinite content of 7.61 %. The kerogen of the ZID coal is mainly of type II₁, while that of the THB coal mainly of type II₂, followed by type Ⅲ. Both the ZID and THB coals have entered the oil-generation window, as indicated by their maximum vitrinite reflectance values (<em>R</em>_{o, max}, %), reflecting good oil generation capacity. However, the hydrocarbon generation potential of the ZID coal is higher than that of the THB.</div></div>","PeriodicalId":100469,"journal":{"name":"Energy Geoscience","volume":"6 3","pages":"Article 100413"},"PeriodicalIF":0.0,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943332","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 data-driven PCA-RF-VIM method to identify key factors driving post-fracturing gas production of tight reservoirs","authors":"Yifan Zhao ,&nbsp;Xiaofan Li ,&nbsp;Lei Zuo ,&nbsp;Zhongtai Hu ,&nbsp;Liangbin Dou ,&nbsp;Huagui Yu ,&nbsp;Tiantai Li ,&nbsp;Jun Lu","doi":"10.1016/j.engeos.2025.100411","DOIUrl":"10.1016/j.engeos.2025.100411","url":null,"abstract":"<div><div>Hydraulic fracturing technology has achieved remarkable results in improving the production of tight gas reservoirs, but its effectiveness is under the joint action of multiple factors of complexity. Traditional analysis methods have limitations in dealing with these complex and interrelated factors, and it is difficult to fully reveal the actual contribution of each factor to the production. Machine learning-based methods explore the complex mapping relationships between large amounts of data to provide data-driven insights into the key factors driving production. In this study, a data-driven PCA-RF-VIM (Principal Component Analysis-Random Forest-Variable Importance Measures) approach of analyzing the importance of features is proposed to identify the key factors driving post-fracturing production. Four types of parameters, including log parameters, geological and reservoir physical parameters, hydraulic fracturing design parameters, and reservoir stimulation parameters, were inputted into the PCA-RF-VIM model. The model was trained using 6-fold cross-validation and grid search, and the relative importance ranking of each factor was finally obtained. In order to verify the validity of the PCA-RF-VIM model, a consolidation model that uses three other independent data-driven methods (Pearson correlation coefficient, RF feature significance analysis method, and XGboost feature significance analysis method) are applied to compare with the PCA-RF-VIM model. A comparison the two models shows that they contain almost the same parameters in the top ten, with only minor differences in one parameter. In combination with the reservoir characteristics, the reasonableness of the PCA-RF-VIM model is verified, and the importance ranking of the parameters by this method is more consistent with the reservoir characteristics of the study area. Ultimately, the ten parameters are selected as the controlling factors that have the potential to influence post-fracturing gas production, as the combined importance of these top ten parameters is 91.95 % on driving natural gas production. Analyzing and obtaining these ten controlling factors provides engineers with a new insight into the reservoir selection for fracturing stimulation and fracturing parameter optimization to improve fracturing efficiency and productivity.</div></div>","PeriodicalId":100469,"journal":{"name":"Energy Geoscience","volume":"6 2","pages":"Article 100411"},"PeriodicalIF":0.0,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143868279","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":"An intelligent log-seismic integrated stratigraphic correlation method based on wavelet frequency-division transform and dynamic time warping: A case study from the Lasaxing oilfield","authors":"Mian Lu ,&nbsp;Dongmei Cai ,&nbsp;Xiandi Fu ,&nbsp;Shunguo Cheng ,&nbsp;Yu Sun ,&nbsp;Pengkun Liu ,&nbsp;Yanli Jiao","doi":"10.1016/j.engeos.2025.100412","DOIUrl":"10.1016/j.engeos.2025.100412","url":null,"abstract":"<div><div>Stratigraphic correlations are essential for the fine-scale characterization of reservoirs. However, conventional data-driven methods that rely solely on log data struggle to construct isochronous stratigraphic frameworks for complex sedimentary environments and multi-source geological settings. In response, this study proposed an intelligent, automatic, log-seismic integrated stratigraphic correlation method that incorporates wavelet frequency-division transform (WFT) and dynamic time warping (DTW) (also referred to as the WFT-DTW method). This approach integrates seismic data as constraints into stratigraphic correlations, enabling accurate tracking of the seismic marker horizons through WFT. Under the constraints of framework construction, a DTW algorithm was introduced to correlate sublayer boundaries automatically. The effectiveness of the proposed method was verified through a stratigraphic correlation experiment on the SA0 Formation of the Xingshugang block in the Lasaxing oilfield, the Songliao Basin, China. In this block, the target layer exhibits sublayer thicknesses ranging from 5 m to 8 m, an average sandstone thickness of 2.1 m, and pronounced heterogeneity. The verification using 1760 layers in 160 post-test wells indicates that the WFT-DTW method intelligently compared sublayers in zones with underdeveloped faults and distinct marker horizons. As a result, the posterior correlation of 1682 layers was performed, with a coincidence rate of up to 95.6 %. The proposed method can complement manual correlation efforts while also providing valuable technical support for the lithologic and sand body characterization of reservoirs.</div></div>","PeriodicalId":100469,"journal":{"name":"Energy Geoscience","volume":"6 3","pages":"Article 100412"},"PeriodicalIF":0.0,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143923795","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":"GIS-based multi-criteria predictive modelling for geothermal energy exploration","authors":"Andongma Wanduku Tende ,&nbsp;Mamidak Miner Iiiya ,&nbsp;Serah Habu ,&nbsp;Jiriko Nzeghi Gajere ,&nbsp;Shekwonyadu Iyakwari ,&nbsp;Mohammed Dahiru Aminu","doi":"10.1016/j.engeos.2025.100409","DOIUrl":"10.1016/j.engeos.2025.100409","url":null,"abstract":"<div><div>Renewable energy resources, including geothermal, are crucial for sustainable environmental management and climate change mitigation, offering clean, reliable, and low-emission alternatives to fossil fuels that reduce greenhouse gases and support ecological balance. In this study, geographic information system (GIS) predictive analysis was employed to explore geothermal prospects, promoting environmental sustainability by reducing the dependence on fossil energy resources. Spatial and statistical analysis including the attribute correlation analysis was used to evaluate the relationship between exploration data and geothermal energy resources represented by hot springs. The weighted sum model was then used to develop geothermal predictive maps while the accuracy of prediction was determined using the receiver operating characteristic/area under curve (ROC/AUC) analysis. Based on the attribute correlation analysis, exploration data relating to geological structures, host rock (Asu River Group) and sedimentary contacts were the most critical parameters for mapping geothermal resources. These parameters were characterized by a statistical association of 0.52, 0.48, and 0.46 with the known geothermal occurrences. Spatial data integration reveals the central part of the study location as the most prospective zone for geothermal occurrences. This zone occupies 14.76 % of the study location. Accuracy assessment using the ROC/AUC analysis suggests an efficiency of 81.5 % for the weight sum model. GIS-based multi-criteria analysis improves the identification and evaluation of geothermal resources, leading to better decision-making.</div></div>","PeriodicalId":100469,"journal":{"name":"Energy Geoscience","volume":"6 2","pages":"Article 100409"},"PeriodicalIF":0.0,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143839332","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":"Advancements in hydrogen storage technologies: Integrating with renewable energy and innovative solutions for a sustainable future","authors":"Yasin Khalili ,&nbsp;Sara Yasemi ,&nbsp;Mohammadreza Bagheri ,&nbsp;Ali Sanati","doi":"10.1016/j.engeos.2025.100408","DOIUrl":"10.1016/j.engeos.2025.100408","url":null,"abstract":"<div><div>Hydrogen storage plays a crucial role in achieving net-zero emissions by enabling large-scale energy storage, balancing renewable energy fluctuations, and ensuring a stable supply for various applications. This study provides a comprehensive analysis of hydrogen storage technologies, with a particular focus on underground storage in geological formations such as salt caverns, depleted gas reservoirs, and aquifers. These formations offer high-capacity storage solutions, with salt caverns capable of holding up to 6 TWh of hydrogen and depleted gas reservoirs exceeding 1 TWh per site. Case studies from leading projects demonstrate the feasibility of underground hydrogen storage (UHS) in reducing energy intermittency and enhancing supply security. Challenges such as hydrogen leakage, groundwater contamination, induced seismicity, and economic constraints remain critical concerns. Our findings highlight the technical, economic, and regulatory considerations for integrating UHS into the oil and gas industry, emphasizing its role in sustainable energy transition and decarbonization strategies.</div></div>","PeriodicalId":100469,"journal":{"name":"Energy Geoscience","volume":"6 2","pages":"Article 100408"},"PeriodicalIF":0.0,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143839331","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":"Physics-integrated neural networks for improved mineral volumes and porosity estimation from geophysical well logs","authors":"Prasad Pothana,&nbsp;Kegang Ling","doi":"10.1016/j.engeos.2025.100410","DOIUrl":"10.1016/j.engeos.2025.100410","url":null,"abstract":"<div><div>Accurate estimation of mineralogy from geophysical well logs is crucial for characterizing geological formations, particularly in hydrocarbon exploration, CO₂ sequestration, and geothermal energy development. Current techniques, such as multimineral petrophysical analysis, offer details into mineralogical distribution. However, it is inherently time-intensive and demands substantial geological expertise for accurate model evaluation. Furthermore, traditional machine learning techniques often struggle to predict mineralogy accurately and sometimes produce estimations that violate fundamental physical principles. To address this, we present a new approach using Physics-Integrated Neural Networks (PINNs), that combines data-driven learning with domain-specific physical constraints, embedding petrophysical relationships directly into the neural network architecture. This approach enforces that predictions adhere to physical laws. The methodology is applied to the Broom Creek Deep Saline aquifer, a CO₂ sequestration site in the Williston Basin, to predict the volumes of key mineral constituents—quartz, dolomite, feldspar, anhydrite, illite—along with porosity. Compared to traditional artificial neural networks (ANN), the PINN approach demonstrates higher accuracy and better generalizability, significantly enhancing predictive performance on unseen well datasets. The average mean error across the three blind wells is 0.123 for ANN and 0.042 for PINN, highlighting the superior accuracy of the PINN approach. This method reduces uncertainties in reservoir characterization by improving the reliability of mineralogy and porosity predictions, providing a more robust tool for decision-making in various subsurface geoscience applications.</div></div>","PeriodicalId":100469,"journal":{"name":"Energy Geoscience","volume":"6 2","pages":"Article 100410"},"PeriodicalIF":0.0,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143816086","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":"Microscopic pore-throat structure and fluid mobility of tight sandstone reservoirs in multi-provenance systems, Triassic Yanchang formation, Jiyuan area, Ordos basin","authors":"Quanpei Zhang ,&nbsp;Chen Yang ,&nbsp;Ye Gu ,&nbsp;Yu Tian ,&nbsp;Hui Liu ,&nbsp;Wen Xiao ,&nbsp;Zhikun Wang ,&nbsp;Zhongrong Mi","doi":"10.1016/j.engeos.2025.100407","DOIUrl":"10.1016/j.engeos.2025.100407","url":null,"abstract":"<div><div>The tight sandstone reservoirs in the first sub-member of Chang 7 member (Chang 7₁) of Triassic Yanchang Formation in the Jiyuan area, Ordos Basin, show significant variations in microscopic pore-throat structure (PTS) and fluid mobility due to the influences of the northeast and northwest dual provenance systems. This study performed multiple experimental analyses on nine samples from the area to determine the petrological and petrophysical properties, as well as the PTS characteristics of reservoirs in different provenance-controlled regions. On this basis, the pore-throat size distribution (PSD) obtained from high-pressure mercury injection (HPMI) was utilized to convert the NMR movable fluid <em>T</em>₂ spectrum, allowing for quantitative characterization of the full PSD and the occurrence characteristics of movable fluids. A systematic analysis was conducted on the primary controlling factors affecting fluid mobility in the reservoir. The results indicated that the lithology in the eastern and western regions is lithic arkose. The eastern sandstones, being farther from the provenance, exhibit higher contents of feldspar and lithic fragments, along with the development of more dissolution pores. The reservoir possesses good petrophysical properties, low displacement pressure, and high pore-throat connectivity and homogeneity, indicating strong fluid mobility. In contrast, the western sandstones, being nearer to the provenance, exhibit poor grain sorting, high contents of lithic fragments, strong compaction and cementation effects, resulting in poor petrophysical properties, and strong pore-throat heterogeneity, revealing weak fluid mobility. The range of full PSD in the eastern reservoir is wider than that in the western reservoir, with relatively well-developed macropores. The macropores are the primary space for occurrence of movable fluids, and controls the fluid mobility of the reservoir. The effective porosity of movable fluids (EPMF) quantitatively represents the pore space occupied by movable fluids within the reservoir and correlates well with porosity, permeability, and PTS parameters, making it a valuable parameter for evaluating fluid mobility. Under the multi-provenance system, the eastern and western reservoirs underwent different sedimentation and diagenesis processes, resulting in differential distribution of reservoir mineral components and pore types, which in turn affects the PTS heterogeneity and reservoir quality. The composition and content of reservoir minerals are intrinsic factors influencing fluid mobility, while the microscopic PTS is the primary factor controlling it. Low clay mineral content, well-developed macropores, and weak pore-throat heterogeneity all contribute to the storage and seepage of reservoir fluids.</div></div>","PeriodicalId":100469,"journal":{"name":"Energy Geoscience","volume":"6 2","pages":"Article 100407"},"PeriodicalIF":0.0,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143816085","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":"Dominant performance parameter and technical limits of surfactants for oil displacement","authors":"Yabing Guo ,&nbsp;Youqi Wang ,&nbsp;Zengmin Lun ,&nbsp;Maolei Cui","doi":"10.1016/j.engeos.2025.100406","DOIUrl":"10.1016/j.engeos.2025.100406","url":null,"abstract":"<div><div>In chemical flooding, emulsification and interfacial tension (IFT) reduction are crucial for enhanced oil recovery (EOR). However, the dominant performance parameter and technical limits of surfactants for oil displacement remain underexplored. This study investigated the relationship between the emulsification capability and IFT. Accordingly, the dominant performance parameter and the technical limits of surfactants were determined using oil displacement experiments. Specifically, an analysis of 74 sets of experimental results revealed a shift in the significant correlation between <em>EI</em> (a quantitative measure of emulsification capability) and <em>σ</em> at an <em>σ</em> value of 8.5 × 10⁻² mN/m (i.e., critical value <em>σ</em>_c). For <em>σ</em> &lt; <em>σ</em>_c, emulsification capability and IFT function as independent performance parameters. The oil displacement experiments using two surfactants with contrasting <em>EI</em> and <em>σ</em> values demonstrate that emulsification capability, rather than ultra-low IFT, is the dominant performance parameter. This study determined the technical limit of <em>EI</em> using oil displacement experiments via in-situ emulsification. The experimental results indicate strong correlations of <em>EI</em> with oil displacement and recovery efficiencies. The incremental displacement and recovery efficiencies were employed to quantify the potential of surfactants to enhance oil displacement and recovery efficiencies, respectively. The incremental displacement and recovery efficiencies versus <em>EI</em> curves revealed a critical <em>EI</em> (<em>EI</em>_c) value of 0.53. When <em>EI</em> &lt; <em>EI</em>_c, the incremental displacement and recovery efficiencies increased significantly with <em>EI</em>. In contrast, when <em>EI</em> &gt; <em>EI</em>_c, their increasing rates slowed down markedly. Therefore, the technical limits of the emulsification capability and IFT of surfactants used in this study are determined at <em>EI</em> ≥ 0.53 and <em>σ</em> ≤ 8.5 × 10⁻² mN/m, respectively.</div></div>","PeriodicalId":100469,"journal":{"name":"Energy Geoscience","volume":"6 2","pages":"Article 100406"},"PeriodicalIF":0.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143799396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A data-driven approach to predict fracture intensity using machine learning for presalt carbonate reservoirs: A feasibility study in the Mero Field, Santos Basin, Brazil","authors":"Eberton Rodrigues de Oliveira Neto ,&nbsp;Fábio Júnior Damasceno Fernandes ,&nbsp;Tuany Younis Abdul Fatah ,&nbsp;Raquel Macedo Dias ,&nbsp;Zoraida Roxana Tejada da Piedade ,&nbsp;Antonio Fernando Menezes Freire ,&nbsp;Wagner Moreira Lupinacci","doi":"10.1016/j.engeos.2025.100404","DOIUrl":"10.1016/j.engeos.2025.100404","url":null,"abstract":"<div><div>Predicting fracture intensity is essential for optimising reservoir production and mitigating drilling risks in the Brazilian pre-salt layer. However, previous studies rely excessively on conceptual models and typically do not integrate multiple types of data to perform such task. Moreover, to date, no feasibility-like studies have assessed the reasonableness of such approaches. We propose a data-driven approach that utilises upscaled well logs (Young's modulus, Poisson's ratio, and silica content) alongside seismic attributes (curvature, distance to fault) to predict fracture intensity. The distance to fault is measured using the fault probability volume estimated by a pre-trained convolutional neural network (CNN). We evaluate the effectiveness of this data-driven approach employing two tree-ensemble models, eXtreme Gradient Boosting (XGBoost) and Random Forest, to estimate the volumetric fracture intensity (P32) in the wells. Regression and residual analyses indicate that XGBoost outperforms Random Forest. Results from feature importance methods, such as permutation importance and Shapley Additive explanations (SHAP), highlight curvature as the most important feature, followed by distance to fault, Young's modulus (or P-Impedance), silica content, and Poisson's ratio. The approach has been validated with rock sampling information and two blind tests. Consequently, we believe this workflow can be applied to other wells in nearby fields. The study offers a valuable tool for quantitatively estimating fracture intensity in pre-salt reservoirs. Future research may use this study as a reference for estimating fracture intensity within a seismic volume. The predicted fracture intensity estimates can enhance the reliability of reservoir porosity models and serve as a geohazard indicator to mitigate drilling risks.</div></div>","PeriodicalId":100469,"journal":{"name":"Energy Geoscience","volume":"6 2","pages":"Article 100404"},"PeriodicalIF":0.0,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143816084","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":"Optimizing the development plan for oil production and CO2 storage in target oil reservoir","authors":"Xiliang Liu ,&nbsp;Hao Chen ,&nbsp;Yang Li ,&nbsp;Weiming Cheng ,&nbsp;Yangwen Zhu ,&nbsp;Hongbo Zeng ,&nbsp;Haiying Liao","doi":"10.1016/j.engeos.2025.100405","DOIUrl":"10.1016/j.engeos.2025.100405","url":null,"abstract":"<div><div>Carbon dioxide enhanced oil recovery (CO₂-EOR) technology is used for oil production and CO₂ storage in reservoirs. Methods are being constantly developed to optimize oil recovery and CO₂ storage during the CO₂ displacement process, especially for low-permeability reservoirs under varying geological conditions. In this study, long-core experiments and trans-scale numerical simulations are employed to examine the characteristics of oil production and CO₂ storage. Optimal production parameters for the target reservoir are also proposed. The results indicate that maintaining the pressure at 1.04 to 1.10 times the minimum miscible pressure (MMP) and increasing the injection rate can enhance oil production in the early stage of reservoir development. In contrast, reducing the injection rate at the later stages prevents CO₂ channeling, thus improving oil recovery and CO₂ storage efficiency. A solution-doubling factor is introduced to modify the calculation method for CO₂ storage, increasing its accuracy to approximately 90 %. Before CO₂ breakthrough, prioritizing oil production is recommended to maximize the economic benefits of this process. In the middle stage of CO₂ displacement, decreasing the injection rate optimizes the coordination between oil displacement and CO₂ storage. Further, in the late stage, reduced pressure and injection rates are required as the focus shifts to CO₂ storage.</div></div>","PeriodicalId":100469,"journal":{"name":"Energy Geoscience","volume":"6 2","pages":"Article 100405"},"PeriodicalIF":0.0,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143768353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}