Geoenergy Science and Engineering最新文献_第5页

Feasibility analysis of strategic petroleum reserve in ultra-deep salt strata with thick interlayers 超深厚夹层盐层战略石油储备可行性分析

IF 4.6 0 ENERGY & FUELS

Geoenergy Science and Engineering

Pub Date : 2026-04-01 Epub Date: 2026-01-03 DOI: 10.1016/j.geoen.2025.214356

Rui Liang , Hang Li , Hongling Ma , Xuan Wang , Jiangyu Fang , Zhen Zeng , Zhuyan Zheng , Wentao Li

Deep underground energy storage is a crucial approach to ensure energy security. In response to the challenge posed by insoluble thick interlayers within the rock salt in Yulin, Shanxi Province, China, this study proposes constructing double-layer strategic petroleum reserve (SPR) caverns in ultra-deep salt strata with thick interlayers. To assess the feasibility of this method, a geomechanical model is developed. Eighteen calculation cases are designed, considering wellhead pressure, cavern long-axis length, and pillar width. Simulation results indicate that thick interlayers significantly limit cavern deformation, enhancing pillar stability. Based on four evaluation indicators of volume shrinkage (VS), displacement, safety factor (SF), and plastic zone volume ratio, it is recommended that the wellhead pressure for ultra-deep SPR salt caverns in Yulin should not be lower than 9 MPa, with the cavern length not exceeding 350 m. Compared to utilizing only a single salt layer, utilizing two adjacent salt layers to construct double-layer SPR caverns can reduce the long-axis pillar width to 0.3L (L is the long-axis length) and the short-axis pillar width to 1.5S (S is the short-axis length), thus improving the utilization rate of salt mine resources by at least 1/4. Double-layer SPR caverns in ultra-deep salt strata with thick interlayers utilize the advantages of thick interlayers and enhance the utilization of salt mine resources, providing substantial guidance for the large-scale developments of energy storage salt caverns.

深层地下储能是保障能源安全的重要途径。针对山西榆林地区岩盐中不溶性厚夹层的挑战，提出在具有厚夹层的超深盐地层中构建双层战略石油储备（SPR）洞穴。为了评估该方法的可行性，建立了一个地质力学模型。考虑井口压力、洞室长轴长度和矿柱宽度，设计了18种计算工况。模拟结果表明，厚夹层明显地限制了洞室变形，提高了矿柱的稳定性。基于体积收缩率（VS）、位移、安全系数（SF）、塑性区体积比4个评价指标，建议榆林超深SPR盐洞井口压力不低于9 MPa，洞室长度不超过350 m。与仅利用单一盐层相比，利用相邻两层盐层构建双层SPR洞室可将长轴矿柱宽度减小至0.3L （L为长轴长度），将短轴矿柱宽度减小至1.5S （S为短轴长度），从而使盐矿资源利用率提高至少1/4。超深厚夹层盐层双层SPR洞室利用了厚夹层的优势，提高了盐矿资源的利用率，为储能盐洞室的大规模开发提供了实质性的指导。

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引用次数: 0

Evaluation and optimization of nonisothermal CO2 injection for improving geologic carbon storage with physics-based and deep learning-based approaches 基于物理和深度学习方法的非等温CO2注入提高地质碳储量的评价与优化

IF 4.6 0 ENERGY & FUELS

Geoenergy Science and Engineering

Pub Date : 2026-04-01 Epub Date: 2026-01-03 DOI: 10.1016/j.geoen.2026.214358

Woojong Yang , Weon Shik Han , Jize Piao , Kue-Young Kim , Won Woo Yoon , Curtis M. Oldenburg

During geologic carbon storage (GCS), Temperature Swing Injection (TSI), a method involving the periodic changes in injection temperature at the wellhead, has been proposed to enhance storage performance. Accurate characterization of TSI requires a coupled wellbore-reservoir model, but solving the coupled partial differential equations is computationally intensive. To overcome this challenge, this study presents an integrated framework that combines physics-based simulation for quantitatively evaluating the impact of TSI on CO₂ storage with deep learning-based surrogate model. A comprehensive dataset was generated by parameterizing two representative TSI strategies including Gradual Temperature Swing (GTS) and Stepwise Temperature Swing (STS). These strategies were simulated for an approximately one-year CO₂ injection period under various formation properties using the coupled wellbore-reservoir simulator (T2Well/ECO2N). The simulation results were then used to develop and train deep learning-based surrogate models that accurately reproduced simulation results (R² > 0.995). On average, TSI increased CO₂ storage by 16.3–31.2 %. Permutation feature importance (PFI) and feature sensitivity (FS) analyses identified mean temperature, swing amplitude, injection pressure, and formation permeability as the most influential parameters. Finally, optimized injection strategies identified by the genetic algorithm (GA) increased stored CO₂ mass up to 22.1–32.6 % compared to pre-optimization cases. This work provides a unified framework that integrates physics-based simulation, deep-learning surrogates, and optimization, offering a computationally efficient pathway for designing complex non-isothermal injection strategies that maximize CO₂ storage efficiency.

在地质储碳（GCS）过程中，为了提高储碳性能，提出了一种涉及井口注入温度周期性变化的变温注入（TSI）方法。准确表征TSI需要一个耦合的井筒-油藏模型，但求解耦合的偏微分方程需要大量的计算。为了克服这一挑战，本研究提出了一个综合框架，将基于物理的模拟与基于深度学习的替代模型相结合，用于定量评估TSI对二氧化碳储存的影响。通过参数化渐进式温度波动（GTS）和逐步温度波动（STS）两种具有代表性的TSI策略，生成了一个全面的数据集。利用井-储耦合模拟器（T2Well/ECO2N）对这些策略进行了大约一年的CO2注入周期的模拟，并对不同的地层性质进行了模拟。然后将仿真结果用于开发和训练基于深度学习的代理模型，该模型可以准确地再现仿真结果（R2 > 0.995）。平均而言，TSI增加了16.3 - 31.2%的二氧化碳储存量。排列特征重要性（PFI）和特征敏感性（FS）分析发现，平均温度、摆动幅度、注入压力和地层渗透率是影响最大的参数。最后，通过遗传算法（GA）确定的优化注入策略与优化前相比，增加了22.1 - 32.6%的CO2储存量。这项工作提供了一个统一的框架，集成了基于物理的模拟、深度学习替代和优化，为设计复杂的非等温注入策略提供了一个计算高效的途径，从而最大限度地提高二氧化碳储存效率。

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引用次数: 0

EHTGNN: An Explainable Hybrid Temporal Graph Neural Network for robust rate of penetration prediction in drilling operations EHTGNN：一种可解释的混合时间图神经网络，用于钻井作业中稳健的渗透速度预测

IF 4.6 0 ENERGY & FUELS

Geoenergy Science and Engineering

Pub Date : 2026-04-01 Epub Date: 2025-12-19 DOI: 10.1016/j.geoen.2025.214347

Rui Zhang , Zhaopeng Zhu , Zhi Yan , Tao Pan , Xianzhi Song , Gensheng Li , Hui Xia , Chaochen Wang

Accurately predicting rate of penetration (ROP) is crucial for optimizing drilling efficiency and reducing costs. While many studies have introduced various intelligent models, most still struggle with weak interpretability and inadequate modeling of complex temporal dependencies and cross-feature interactions. This paper proposes an Explainable Hybrid Temporal Graph Neural Network (EHTGNN) that autonomously captures multivariate dependencies and temporal patterns in drilling processes without relying on predefined physical priors. The model comprises four key modules: (1) a sequence reconstruction module for structuring temporal contexts, (2) an automatic graph generation module for mining feature interdependencies, (3) a temporal graph convolution module to jointly model intra- and inter-variable relations, and (4) an enhanced temporal memory network. Experimental validation using field drilling datasets demonstrates that EHTGNN achieves a mean absolute percentage error of 16.6 % and a correlation coefficient of 0.994, significantly outperforming several state-of-the-art models. Beyond performance, the model provides interpretable insights into key contributors to ROP variation, including delayed responses from RPM and bit-rock interaction dynamics. This study provides a robust, interpretable, and scalable solution for real-time drilling optimization in petroleum engineering scenarios.

准确预测钻速（ROP）对于优化钻井效率和降低成本至关重要。虽然许多研究已经引入了各种智能模型，但大多数仍然存在可解释性差和复杂时间依赖性和跨特征交互建模不足的问题。本文提出了一种可解释的混合时间图神经网络（EHTGNN），该网络可以自主捕获钻井过程中的多变量依赖关系和时间模式，而不依赖于预定义的物理先验。该模型包括四个关键模块：(1)构建时间上下文的序列重构模块，(2)挖掘特征相互依赖关系的自动图生成模块，(3)联合建模变量内和变量间关系的时间图卷积模块，以及(4)增强的时间记忆网络。使用现场钻井数据集进行的实验验证表明，EHTGNN的平均绝对百分比误差为16.6%，相关系数为0.994，显著优于几种最先进的模型。除了性能之外，该模型还提供了对ROP变化的关键影响因素的解释，包括RPM的延迟响应和钻头-岩石相互作用动力学。该研究为石油工程场景中的实时钻井优化提供了一个强大的、可解释的、可扩展的解决方案。

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引用次数: 0

A Multi-Scale Gradient Boosting Framework for High-Precision Well-Log Curve Prediction: A Time-Frequency Integrated Machine Learning Approach 高精度测井曲线预测的多尺度梯度增强框架：时频集成机器学习方法

IF 4.6 0 ENERGY & FUELS

Geoenergy Science and Engineering

Pub Date : 2026-03-18 DOI: 10.1016/j.geoen.2026.214464

Zhongchuang Wang , Jingzhe Li , Xiufan Zhang , Hanhan Yang

Conventional models often struggle to accurately capture the complex nonlinear characteristics of stratigraphic layers, which may compromise the fidelity of geological interpretation. To overcome these challenges, this study proposes an advanced predictive framework based on an optimized LightGBM approach. The framework integrates multi-scale feature extraction and LightGBM optimization to enhance modeling performance for complex formation interfaces. The proposed framework was validated through two case studies. Results from Case 1 demonstrated that the optimized LightGBM model significantly outperformed linear regression, random forest, XGBoost, and Bidirectional Long Short-Term Memory (Bi-LSTM) models in predictive accuracy, achieving the lowest root mean square error (RMSE = 0.003755) and the highest coefficient of determination (R² = 0.989894). In comparison, the second-best performing model, XGBoost, attained an R² of only 0.936319. To further verify the generalizability of the framework, a public dataset was employed as Case 2 for additional validation. The LightGBM framework continued to deliver excellent performance, achieving the highest R² value (0.944896) among all models. The proposed method facilitates deep integration of machine learning with geological interpretation and offers an efficient, reliable technical pathway for detailed hydrocarbon reservoir characterization and subsurface resource assessment. This approach holds considerable engineering value for resource exploration in complex geological settings.

传统的模型往往难以准确地捕捉地层复杂的非线性特征，这可能会影响地质解释的保真度。为了克服这些挑战，本研究提出了一种基于优化的LightGBM方法的先进预测框架。该框架集成了多尺度特征提取和LightGBM优化，以提高复杂地层界面的建模性能。通过两个案例研究验证了所提议的框架。案例1的结果表明，优化后的LightGBM模型在预测精度上显著优于线性回归、随机森林、XGBoost和双向长短期记忆（Bi-LSTM）模型，其均方根误差最小（RMSE = 0.003755），决定系数最高（R2 = 0.989894）。相比之下，性能第二好的模型XGBoost的R2仅为0.936319。为了进一步验证该框架的可泛化性，使用公共数据集作为案例2进行额外验证。LightGBM框架继续表现优异，在所有模型中获得最高的R2值（0.944896）。该方法促进了机器学习与地质解释的深度融合，为详细的油气藏表征和地下资源评价提供了高效、可靠的技术途径。该方法对复杂地质环境下的资源勘探具有重要的工程价值。

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引用次数: 0

Distribution and evolution mechanisms of deep sandstone pore-fracture system by using 3D CT reconstruction 基于三维CT重建的深部砂岩孔隙-裂缝系统分布及演化机制

IF 4.6 0 ENERGY & FUELS

Geoenergy Science and Engineering

Pub Date : 2026-03-01 Epub Date: 2025-12-21 DOI: 10.1016/j.geoen.2025.214354

Shuang You , Qixing Feng , Davide Elmo , Qiancheng Geng , Yitong Wang , Yu Gao

As a porous medium, deep sandstone exhibits a complex pore and fracture structure that influences fluid transport properties. Accurately characterizing pore-fracture dual structures is essential for understanding and preventing water inrush disasters. CT-based 3D reconstruction was employed to develop quantitative methods for characterizing pore–fracture structures in deep sandstone and to analyze their evolution under external water infiltration. An integrated fractal dimension approach was proposed, combining volumetric (3D) fractal dimensions with slice-based (2D) fractal dimensions. Results show that the volumetric fractal dimension consistently characterizes global heterogeneity and connectivity, with S3 exhibiting the highest values and S1 the lowest. The 2D fractal dimension captures local pore-boundary irregularities and intra-sample variability, revealing that S1 displays wide heterogeneity with low-end outliers, whereas S3 maintains high local complexity. These findings demonstrate that integrated fractal dimensions provide a more comprehensive description of pore complexity than traditional single-parameter approaches. Furthermore, water infiltration into the sandstone induces the expansion and transformation of the pore–fracture network, promoting the growth of small pores and fractures into larger and better-connected ones. Overall, this study provides a microstructural basis for risk assessment and prevention of water inrush disasters in deep sandstone formations.

深层砂岩作为一种多孔介质，具有复杂的孔隙和裂缝结构，影响流体的输运特性。准确表征孔-破裂二元结构对认识和预防突水灾害至关重要。采用基于ct的三维重建技术，建立了表征深层砂岩孔隙-裂缝结构的定量方法，并分析了其在外部水入渗作用下的演化过程。提出了一种综合分形维数方法，将三维分形维数与二维分形维数相结合。结果表明，体积分形维数与全球异质性和连通性一致，其中S3值最高，S1值最低。二维分形维数捕获了局部孔隙边界不规则性和样本内变异性，表明S1具有广泛的异质性和低端异常值，而S3保持了较高的局部复杂性。这些发现表明，与传统的单参数方法相比，综合分形维数可以更全面地描述孔隙复杂性。此外，水对砂岩的入渗诱导了孔隙-裂缝网络的扩展和转变，促使小孔隙和裂缝发育成更大、连接更好的孔隙和裂缝。研究结果为深部砂岩突水灾害风险评价和防治提供了微观结构依据。

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引用次数: 0

Multiscale mechanical characterization of Wufeng-Longmaxi shale through integrated triaxial compressive test, microindentation, and modulus mapping 通过综合三轴压缩试验、微压痕和模量图表征五峰—龙马溪页岩多尺度力学特征

IF 4.6 0 ENERGY & FUELS

Geoenergy Science and Engineering

Pub Date : 2026-03-01 Epub Date: 2025-12-09 DOI: 10.1016/j.geoen.2025.214337

Pan Mou , Chunxiao Li , YiDi Mi , Lingyun Kong , Yansong Liu , Liang Zou

The mechanical properties of shale rocks exhibit distinct multiscale characteristics. However, studies that systematically investigate shale mechanics across the macro-, meso-, and micro-scales using the same set of samples remain limited. In this study, a comprehensive multiscale experimental approach was applied to shale specimens from the Wufeng–Longmaxi Formation in the Sichuan Basin. At the macroscale, uniaxial and triaxial compression tests were performed to determine bulk mechanical properties. At the mesoscale, microindentation was employed to evaluate the mechanical behavior of representative elementary volumes (REV). At the microscale, the elastic moduli of individual mineral phases were accurately quantified using modulus mapping in conjunction with SEM-EDS-based mineral identification. Furthermore, homogenization techniques, including the Mori–Tanaka (MT) and self-consistent scheme (SCS) methods, were utilized to upscale microscale mechanical properties to the mesoscale. The upscaled results were then compared with mesoscale experimental measurements, and potential sources of discrepancy were discussed.

The results demonstrate: (1) Shale mechanical properties exhibit pronounced multiscale characteristics. At the macroscale, mechanical behavior varies with confining pressure; At the mesoscale, microindentation results are primarily influenced by microstructural features and mineralogical composition; At the microscale, pyrite exhibits the highest Young's modulus, followed by dolomite and quartz, while clay minerals show the lowest values. (2) The upscaled mechanical properties obtained through homogenization methods are considerably higher than the experimentally measured mesoscale values, indicating that conventional homogenization approaches are insufficient for accurately bridging microscale properties to the mesoscale. To improve the reliability of upscaling, more advanced simulations that incorporate the effects of microfractures, interfacial weaknesses, and bedding planes are necessary.

页岩力学性质具有明显的多尺度特征。然而，利用同一组样品系统地研究页岩宏观、中观和微观力学的研究仍然有限。以四川盆地五峰组—龙马溪组页岩为研究对象，采用多尺度综合实验方法。在宏观尺度上，进行了单轴和三轴压缩试验，以确定整体力学性能。在中尺度上，采用微压痕法评价了典型基本体积（REV）的力学行为。在微观尺度上，结合基于sem - eds的矿物识别，利用模量映射精确量化了单个矿物相的弹性模量。此外，采用均匀化技术，包括Mori-Tanaka （MT）和自洽方案（SCS）方法，将微尺度的力学性能提升到中尺度。然后将升级后的结果与中尺度实验测量结果进行了比较，并讨论了可能的差异来源。结果表明：(1)页岩力学性质具有明显的多尺度特征。在宏观尺度上，力学行为随围压的变化而变化；在中尺度上，微压痕结果主要受微观结构特征和矿物组成的影响；在微观尺度上，黄铁矿的杨氏模量最高，其次是白云石和石英，粘土矿物的杨氏模量最低。(2)均质化方法获得的尺度尺度力学性能明显高于中尺度实验测量值，表明传统的均质化方法不足以准确地将微观尺度的力学性能与中尺度的力学性能连接起来。为了提高升级的可靠性，需要更先进的模拟，包括微裂缝、界面弱点和层理平面的影响。

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引用次数: 0

Review of recent advances in geothermal energy production processes and applications 综述了地热能生产工艺和应用的最新进展

IF 4.6 0 ENERGY & FUELS

Geoenergy Science and Engineering

Pub Date : 2026-03-01 Epub Date: 2025-12-08 DOI: 10.1016/j.geoen.2025.214329

Ekrem Alagoz , Aleksei Zhurkevich , Artur Davletshin , Moises Velasco

Geothermal energy is a renewable and clean energy source that harnesses the Earth's internal heat for electricity generation and direct applications. The advancing of geothermal energy production is critical for establishing stable energy supply and diversifying the global energy market. This comprehensive review explores the key advances in geothermal energy generation, with a focus on the operational mechanisms of geothermal power plants (GPPs). In addition, it examines the direct applications of geothermal energy in various sectors, including space heating and cooling, aquaculture, drying processes, and recreational facilities. Specifically, we review geothermal heat pumps (GHPs), discussing ground-coupling techniques and underlining innovations such as energy piles and geothermal baskets. The review provides a detailed analysis of the critical components of GHP systems and their role in sustainable energy production. By highlighting the practical applications and technical advancements in geothermal energy, this study offers valuable insights into current state of the technology.

地热能是一种可再生的清洁能源，它利用地球内部的热量来发电和直接应用。地热能生产的发展对于建立稳定的能源供应和实现全球能源市场的多元化至关重要。本文综述了地热能发电的主要进展，重点介绍了地热发电厂（GPPs）的运行机制。此外，它还审查了地热能在各个部门的直接应用，包括空间加热和冷却、水产养殖、干燥过程和娱乐设施。具体来说，我们回顾了地热热泵（GHPs），讨论了地面耦合技术和强调创新，如能源桩和地热篮。本报告详细分析了全球水源系统的关键组成部分及其在可持续能源生产中的作用。通过强调地热能的实际应用和技术进步，本研究为地热能技术的现状提供了有价值的见解。

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引用次数: 0

An FMI image fracture segmentation approach based on a two-stage decoupling strategy 基于两阶段解耦策略的FMI图像断裂分割方法

IF 4.6 0 ENERGY & FUELS

Geoenergy Science and Engineering

Pub Date : 2026-03-01 Epub Date: 2025-12-12 DOI: 10.1016/j.geoen.2025.214319

Wenqiang Tang , Li Hou , Songtao Wu , Kunyu Wu , Hanting Zhong , Mingcai Hou , Daowei Zhang , Chao Ma

The study of fractures constitutes a critical phase in oil and gas exploration and development, encompassing the analysis of their distribution, morphology, and density, which crucially influence subsequent exploration efforts. While core samples traditionally offer a direct reflection of well fracture characteristics, their high cost has led researchers to rely on Formation MicroImager (FMI) logging images increasingly. However, conventional methods of fracture analysis using FMI images typically require manual extraction and analysis by researchers, a process that is not only time-consuming and labor-intensive but also prone to subjective biases. To address these challenges, this study introduces a fracture segmentation model based on a two-stage decoupling strategy, named the TSNet (Two Stage Network). This model initially segments fractures accurately within the information-rich areas of the FMI image and subsequently connects disjointed fracture fragments through simulated generation in the blank bands, thereby effectively reconstructing the fracture's integrity and enhancing the continuity of segmentation. Furthermore, the TSNet model incorporates a deformable convolution operator that adaptively conforms to the varying morphologies of fractures, thus enhancing the segmentation of complex fracture structures and improving both the accuracy and efficiency of fracture analysis. Achieving a 72.41 % IoU in fracture segmentation accuracy with FMI image data from the Qaidam Basin, the TSNet model not only offers a novel approach to fracture segmentation but also provides researchers with more feasible options for conducting detailed fracture analyses.

裂缝研究是油气勘探开发的一个关键阶段，包括裂缝的分布、形态和密度分析，这对后续的勘探工作至关重要。虽然岩心样品通常可以直接反映井的裂缝特征，但其高昂的成本使得研究人员越来越依赖地层微成像仪（FMI）测井图像。然而，使用FMI图像进行断裂分析的传统方法通常需要研究人员手工提取和分析，这一过程不仅耗时费力，而且容易产生主观偏差。为了应对这些挑战，本研究引入了一种基于两阶段解耦策略的裂缝分割模型，称为TSNet（两阶段网络）。该模型首先在FMI图像信息丰富的区域内对裂缝进行准确的分割，然后在空白带内通过模拟生成的方法连接断裂处的碎片，从而有效地重建裂缝的完整性，增强分割的连续性。此外，TSNet模型引入了自适应适应裂缝形态变化的可变形卷积算子，增强了对复杂裂缝结构的分割，提高了裂缝分析的精度和效率。利用柴达木盆地FMI图像数据，TSNet模型的裂缝分割精度达到72.41%，不仅为裂缝分割提供了一种新的方法，也为裂缝详细分析提供了更可行的选择。

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引用次数: 0

Study on the detection method of physical anomaly parameters of tight reservoirs based on the isolated forest algorithm of attention mechanism 基于注意力机制孤立森林算法的致密储层物性异常参数检测方法研究

IF 4.6 0 ENERGY & FUELS

Geoenergy Science and Engineering

Pub Date : 2026-03-01 Epub Date: 2025-12-06 DOI: 10.1016/j.geoen.2025.214310

Yuhao Zhang , Meng Du , Hanmin Xiao , Qingjie Liu , Jingwei Tao , Liguo Zhou

Evaluating petrophysical properties in tight sandstone reservoirs is crucial for formulating reservoir development strategies. However, due to the complex and variable geological structure of these reservoirs, significant anomalies often arise in assessing petrophysical parameters, which do not fully meet the requirements for accurate reservoir evaluation and development. Therefore, to improve the quality of the dataset, this study proposes an anomaly removal method based on an Isolation Forest algorithm enhanced with an attention mechanism. This model dynamically adjusts feature weights through the attention mechanism, enabling the algorithm to focus more precisely on features critical for anomaly detection. The results demonstrate that compared to traditional Isolation Forest algorithms and other common anomaly detection methods, the Attention-based Isolation Forest algorithm achieves superior F1 scores in detecting outliers in datasets such as mercury intrusion porosimetry data. This enhanced method more accurately identifies and targets potential anomalous parameters, effectively removing and cleansing outliers from the dataset. Consequently, it provides higher-quality data for reservoir evaluation, facilitating the precise identification of sweet spots and optimal development areas in tight reservoirs. The developed algorithm offers an innovative and effective solution for anomaly detection in datasets used for the development evaluation of tight oil reservoirs, providing a scientific basis and practical value for optimizing resource development strategies and improving development efficiency.

致密砂岩储层物性评价是制定储层开发策略的关键。但由于储层地质构造复杂多变，在岩石物性参数评价中经常出现重大异常，不能完全满足储层准确评价和开发的要求。因此，为了提高数据集的质量，本研究提出了一种基于隔离森林算法和注意机制增强的异常去除方法。该模型通过注意机制动态调整特征权重，使算法能够更精确地关注异常检测的关键特征。结果表明，与传统的隔离森林算法和其他常见的异常检测方法相比，基于注意力的隔离森林算法在检测汞侵入孔隙度等数据集的异常点方面取得了更高的F1分数。这种增强的方法更准确地识别和定位潜在的异常参数，有效地从数据集中去除和清除异常值。从而为储层评价提供了更高质量的数据，有利于致密储层甜点和最佳开发区域的精确识别。该算法为致密油储层开发评价数据集异常检测提供了创新有效的解决方案，为优化资源开发策略、提高开发效率提供了科学依据和实用价值。

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引用次数: 0

Building 3D porosity model and site screening assessment for CO2 storage resource evaluation of hydrocarbon reservoirs in offshore Central Gulf of Mexico 墨西哥湾中部近海油气储层CO2储量评价的三维孔隙度模型建立及现场筛选评价

IF 4.6 0 ENERGY & FUELS

Geoenergy Science and Engineering

Pub Date : 2026-03-01 Epub Date: 2025-12-22 DOI: 10.1016/j.geoen.2025.214355

Joshua Adeyemi Ademilola, Jack C. Pashin

The large number of mature oil and gas reservoirs in the offshore Gulf of Mexico makes large-scale CO₂-enhanced oil recovery (EOR) very promising. However, reservoir heterogeneity, porosity, permeability, pressure, and temperature, among other factors, are fundamental considerations for CO₂-EOR storage operations. This study uses an empirical Bayesian kriging 3D technique to create a three-dimensional porosity model from the 2019 BOEM sand database, perform site screening of the 1615 active and depleted sands in seven protraction areas of the Central Gulf of Mexico and rate the CO₂ storage resource of oil and gas fields in the study area. Porosity volume depth slices at 2239 and 5946 m subsea show lateral and vertical variability in porosity which decreases from a depth 2239 m to 5956 m and increases towards the eastern and southern part of the study area. Permeability is sensitive to effective porosity and hydrocarbon saturation in the study area, indicating high CO₂ storage potential in concert with EOR operations. Only 34 % of the active and depleted sands meet all screening criteria in the study area. However, the total P50 storage resource of the 552 sands that satisfy all the screening criteria used in this study is ∼893 Mt. Field GC826 in the Green Canyon protraction area has the largest P50 CO₂ storage resource in the study area, which is estimated to be ∼134 Mt. Future research in the study area should focus on fault seal assessment to better understand CO₂ storage potential in the study area.

墨西哥湾近海大量成熟油气藏使得大规模二氧化碳提高采收率（EOR）非常有前景。然而，储层的非均质性、孔隙度、渗透率、压力和温度等因素是CO2-EOR储存作业的基本考虑因素。本研究利用经验贝叶斯克里格三维技术，从2019年BOEM砂岩数据库中创建三维孔隙度模型，对墨西哥湾中部7个延伸区1615种活性砂和枯竭砂进行现场筛选，并对研究区油气田的二氧化碳储存资源进行评级。2239和5946 m海底孔隙度体积深度切片显示孔隙度的横向和纵向变化，从2239 m到5956 m，孔隙度减小，向研究区东部和南部增加。渗透率对研究区域的有效孔隙度和油气饱和度敏感，表明在提高采收率的同时具有较高的二氧化碳储存潜力。在研究区域，只有34%的活性砂和衰竭砂符合所有筛选标准。然而，满足本研究所有筛选标准的552个砂的总P50存储资源为~ 8.93 Mt。绿峡谷延伸区GC826油田的P50 CO2存储资源在研究区最大，估计为~ 1.34 Mt。研究区未来的研究应侧重于断层封评价，以更好地了解研究区CO2存储潜力。

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引用次数: 0