The global pursuit of clean and sustainable energy has increased interest in hydrogen as a key energy carrier for achieving carbon neutrality. Consequently, global demand for hydrogen is anticipated to grow significantly in both the near and long term, necessitating the development of hydrogen production methods. While several researches have examined hydrogen generation through inorganic processes such as serpentinization, the potential of organic-rich sedimentary formations remains underexplored. This study investigates hydrogen-rich gas generating potential and fracture evolution of organic-rich rocks, with a particular focus on immature shales under controlled thermal treatment, aiming to enhance the yield of clean hydrogen gas. Pyrolysis experiments were conducted to simulate subsurface geological conditions, supported by comprehensive characterization using X-ray diffraction (XRD), X-ray fluorescence (XRF), thermogravimetric analysis (TGA). Gas Chromatography (GC) was used to analyze the gases generated at various heating temperatures and micro-CT imaging was used to examine the samples subjected to varying temperatures. The results show that hydrogen generation increases with temperature, with yields rising from 0.31% at 100°C to 36.02% at 450°C. High-resolution micro-CT imaging shows that thermally induced fractures developed predominantly parallel to bedding planes, enhancing permeability and facilitating gas migration. The progressive decomposition of organic matter, coupled with fracture development, significantly improved hydrogen release efficiency. These findings highlight the potential of organic-rich rocks, as viable and cost-effective targets for natural hydrogen exploration and in situ hydrogen gas generation and offering a pathway toward sustainable subsurface hydrogen exploitation strategies.

How to cite: Kimayim, A., Tawabini, B., Abu-Mahfouz, I., and Yaseri, A.: Exploring the Potential of Organic-Rich Shales for In Situ Hydrogen Production through Thermal Stimulation and Fracturing., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-708, https://doi.org/10.5194/egusphere-egu26-708, 2026.

Oil and gas reserves are crucial resources for human survival, directly affecting the sustainable development and utilization of future energy. In order to protect the Earth we live on, it is crucial to enhance our understanding and judgment of the trends in oil and gas reserves and to use these resources wisely. To explore new methods for predicting oil and gas reserves, promote sustainable energy development, and provide a theoretical basis for oil and gas exploration and development, this study takes the Neuquén Basin in South America as an example. By combining oil and gas reserve growth data with various geological characteristics and other comprehensive information, a Monte Carlo search + ARIMA algorithm-based method for predicting oil and gas reserves is proposed and applied to the Neuquén Basin for predictive validation. This method analyzes the structural background and divides the basin into structural units to decompose the basin’s reserves into reserves within each structural unit. The reserve growth data from each unit are input into the model, and the parameters required by the model are obtained through Monte Carlo search to produce predictive results. This approach successfully captures the inherent trends of reserve changes and the dynamic features of reserve growth. The method shows significant effectiveness in predicting reserves in the Neuquén Basin, with the predictive model demonstrating high accuracy in fitting.

How to cite: Li, H. and Zhang, L.: oil and gas reserve prediction method based on Monte Carlo Search + ARIMA algorithm: A case study of the Neuquén basin in South America, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2263, https://doi.org/10.5194/egusphere-egu26-2263, 2026.

Abstract

The buried depth of Archean metamorphic buried hill in Bozhong L Oilfield of Bohai Bay Basin is more than 4000 meters, and the matrix porosity and permeability are extremely low. As an effective reservoir space and seepage channel, natural fractures are the core controlling factors for oil and gas enrichment and high yield in buried hills of tight metamorphic rocks. Due to the influence of multi-stage tectonic movement and weathering, the development of buried hill fractures is complex. At the same time, due to the lack of core and imaging logging data, the study of fracture regularity is not systematic. In this study, the data of core, thin section, scanning electron microscope, imaging logging, conventional logging and production performance were comprehensively used to carry out conventional logging fracture identification, and the vertical and plane distribution of fractures and their influence on productivity were clarified. The conventional logging is calibrated by core and imaging logging, and four logging curves sensitive to fractures, such as resistivity difference, density, acoustic time difference and natural gamma, are optimized. The correlation degree between each parameter and fracture is calculated and weighted, and the fracture indication parameter curve is constructed. Compared with the results of fracture identification such as core and imaging logging in the study area and the dynamic data such as leakage, the accuracy of fracture identification based on conventional logging is more than 85 %. In the longitudinal direction, the strong weathered zone is dominated by weathering fractures, with high degree of fracture filling and strong reservoir heterogeneity. The sub-weathered zone develops structures and weathering fractures, which are transformed by dissolution and have high porosity and high permeability. The tight zone only develops regional structural fractures with low porosity and low permeability. The inner fracture zone is dominated by fault-related structural fractures, with low porosity and high permeability. The degree of fracture development is sub-weathered zone > strong weathered zone > inner fracture zone > tight zone. On the plane, fracture development is mainly controlled by faults. With the increase of distance from faults, the degree of fracture development decreases exponentially. The degree of fracture development is significantly positively correlated with productivity. The more developed the fracture is, the higher the productivity is. The research results can provide reference for the characterization of natural fractures in deep metamorphic buried hill reservoirs, and provide geological basis for the efficient development of deep metamorphic buried hill reservoirs in this area.

Key words

Metamorphic buried hills; Conventional logging; Fracture identification; Distribution law; Capacity; Bohai Sea

How to cite: Guo, R., Lv, W., Zeng, L., Du, X., Li, H., and Yin, J.: Study on fracture distribution law of buried hill in deep metamorphic rock : A case study of Bozhong L oilfield in Bohai Bay Basin, China, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3007, https://doi.org/10.5194/egusphere-egu26-3007, 2026.

Abstract: Natural fractures in tight sandstone reservoirs play an important role in hydrocarbon migration and accumulation. Fracture identification remains challenging due to the scarcity of labeled data and the complex logging responses of fractures. To address these problems, we propose a novel hybrid deep learning framework (CNN-Attention-BiLSTM). First, labeled fracture classification based on Full waveform sonic logs characteristics is employed to screen unlabeled data, replacing sampling algorithms for data balancing. This approach provides more fracture labels that align with authentic geological information. Subsequently, one-dimensional convolution is applied to construct multi-dimensional fracture logging response patterns that characterize fracture development. A Channel Self-Attention is introduced to assign optimal weights to response patterns across different dimensions, achieving an optimized pattern combination. A double-layer BiLSTM is then utilized to mitigate the impact of sedimentary cycles on logging identification, while capturing both short- and long-term dependencies of fracture responses across different network layers. The identification method is applied to the H1 member of the Lower Shihezi Formation in the Hangjinqi area, China. The test set accuracy is higher than 90%, and blind wells verification demonstrates an improvement of over 8% in accuracy compared to conventional methods. The identification results reveal that fractures are the most developed in H1-2, followed by H1-1 and H1-3, while H1-4 is the least developed layer. The fracture distribution pattern is evidently controlled by sedimentary rhythms, with fracture density decreasing in the order: interbedded sandstone and mudstone layers, thick sandstone and thick mudstone, thick mudstone and poorly developed sandstones. This trend is primarily attributed to the thickness of mechanical stratigraphy. Under equivalent tectonic stress conditions, thin sandstone layers are more prone to fracturing due to stress concentration, resulting in higher fracture density. Additionally, the proposed method deepens the correlation between the log response types of fractures and their development degree. It clarifies that fractures occur in varied patterns across different regions. In sandy conglomerates and gravel coarse sandstone intervals with high porosity and permeability, fractures tend to occur as single or multiple parallel fractures and are relatively less developed. fractures are more prevalent in the overlying and underlying intervals. Conversely, in tight sandstone intervals with poor porosity and permeability, the rock is more brittle, leading to the development of dense, interconnected fracture networks. And gas distribution shows correlate strongly with fracture-developed intervals. Therefore, it can be inferred that in intervals with high-quality sandstone reservoirs in the study area, fractures likely serve as vertical conduits connecting upper and lower gas-bearing zones, acting as preferential migration pathways. In contrast, within tight sandstone intervals, fractures primarily enhance matrix reservoir quality, thereby facilitating gas migration and accumulation. The intelligent fracture identification method proposed in this study can provide guidance for the migration, accumulation and efficient development of tight sandstone gas, Further, it can also offer a basis for the later carbon dioxide storage and the construction of underground gas storage of tight sandstone.

Keywords: Fracture identification; Tight reservoirs; Full waveform sonic logs; Conventional logs; Deep learning 

How to cite: Liang, B., Zeng, L., and Dong, S.: Intelligent fracture identification and its geological significance in tight sandstone reservoirs., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4140, https://doi.org/10.5194/egusphere-egu26-4140, 2026.

Free hydrocarbon content (S1) is a key parameter for source rock evaluation and sweet spot identification in organic-rich shale reservoirs. Accurate determination of S1 is essential for petroleum exploration; however, traditional measurements rely on expensive core acquisition and Rock-Eval pyrolysis, limiting spatial coverage and operational efficiency. Empirical and log-based models often fail to capture complex non-linear relationships between S1, petrophysical logs, and geochemical properties. This study presents an integrated, physics-informed machine learning workflow for predicting S1 from well logs, mineralogical, and geomechanical data in the upper Shahejie Formation. The dataset comprises 357 S1 core measurements matched to high-resolution well logs (gamma ray, acoustic travel time, density, neutron porosity, and resistivity) over a 799 m stratigraphic interval. To address the inherent depth mismatch between irregularly spaced cores and regularly sampled logs, a constrained nearest-neighbor depth-matching strategy was implemented and validated.  Quality control confirmed minimal bias and high precision, ensuring that observed log S1 correlations represent true petrophysical trends rather than alignment-related biases. Physics-informed feature engineering was applied to capture geological ratios, porosity interactions, and depth trends. Interaction terms, including resistivity-TOC combinations, were incorporated to reflect hydrocarbon saturation and organic matter effects. Six ML algorithms were evaluated, including tree-based ensembles, kernel-based methods, and neural networks. The gradient boosting model achieved the best performance, with a correlation coefficient of 0.92 on independent test data and an RMSE of 0.58, representing a 33% improvement over a logs-only baseline. Cross-validation based on unique S1 measurements was used to prevent data leakage and demonstrated stable generalization across the dataset. Feature importance analysis highlights the dominant contribution of physics-informed terms, confirming that physically constrained predictors outperform individual logging or geochemical parameters. The proposed workflow enables continuous S1 profiling with minimal core measurements, supporting reservoir characterization and sweet-spot identification while reducing reliance on expensive geochemical analyses. This study demonstrates how combining rigorous depth alignment, physics-guided feature engineering, and machine learning can deliver reliable continuous S1 prediction for shale energy resources.

How to cite: Bennani, R. and Wang, M.: Physics-Informed Machine Learning Workflow for Free Hydrocarbon Content (S1) Prediction in Organic-Rich Shale Formation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5306, https://doi.org/10.5194/egusphere-egu26-5306, 2026.

With the continuous advancement of oil and gas exploration technologies and associated theoretical frameworks, deep to ultra-deep oil and gas exploration has emerged as a pivotal component of contemporary petroleum geology research and currently seems as a focal area of interest. But, overpressure is prevalent in deep to ultra-deep formations, and there are substantial discrepancies in studies regarding the impact of overpressure on the maturity of hydrocarbon source rocks and hydrocarbon generation. These discrepancies have hindered a comprehensive understanding of hydrocarbon formation and phase states in ultra-deep settings, thereby constraining deep oil and gas exploration endeavors.

The Shawan Sag in the Junggar Basin is characterized by widespread and high-intensity overpressure development, coupled with huge hydrocarbon resources in the lower stratigraphic assemblage. Among these, The Fengcheng Formation, serving as the primary hydrocarbon source rock, is the main source of oil and gas for the reservoirs within the sag. Consequently, this study focuses on the Permian hydrocarbon source rocks in the Shawan Sag of the Junggar Basin as the primary research target. After ascertaining the overpressure development in the Permian strata, typical low-maturity samples were selected to conduct hydrocarbon generation physical simulation experiments under varying pressure conditions. Based on these experiments, maturity evolution and hydrocarbon generation kinetic models that account for pressure were established, and thermal-maturity-hydrocarbon generation evolutionary history simulations were performed for typical wells and a 2D cross-section.

The results reveal the following: (1) There is a negative correlation between vitrinite reflectance and pressure in the vertical direction, with Ro evolution being lower than the normal trend in overpressure zones. (2) Thermal simulation experiments confirm that, under identical temperature conditions, higher pressure leads to a lower equivalent Ro and a greater proportion of medium-to-heavy components in the generated hydrocarbon products, demonstrating the inhibitory effect of pressure on hydrocarbon source rock maturity and hydrocarbon generation products. (3) Based on the results of physical simulation experiments and measured geological data, a 2D thermal-maturity-hydrocarbon generation evolutionary history for the Shawan Sag was simulated. It is concluded that the hydrocarbon source rocks in the Fengcheng Formation of the Shawan Sag are predominantly Type II highly mature hydrocarbon source rocks. The hydrocarbon generation threshold is suppressed until the end of the Triassic, with significant oil generation commencing in the late Jurassic and entering the highly mature stage by the end of the Cretaceous. Regarding hydrocarbon generation products, the cracking of heavy hydrocarbons to generate gas in the Fengcheng Formation is inhibited by overpressure. This study contributes to enhancing the theoretical understanding of overpressure-inhibited hydrocarbon generation and holds practical significance for hydrocarbon exploration in ultra-deep formations within the Shawan Sag.

How to cite: He, B. and Liu, J.: Maturity Evolution History and Hydrocarbon Generation Evolution History of Hydrocarbon Source Rocks in the Fengcheng Formation, Shawan Sag, Under the Influence of Overpressure, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6097, https://doi.org/10.5194/egusphere-egu26-6097, 2026.

Reservoir quality in carbonate systems is commonly controlled by secondary porosity associated with fractures and karst. Accurate characterisation of these features is critical for predicting fluid storage and permeability distribution, yet remains challenging using conventional downhole geophysical logging techniques. Interpretation is typically performed manually using resistivity borehole images (BHIs), which resolve rock texture at millimetre scale. However, this approach is time-consuming and yields only a limited and largely qualitative representation of the true porosity distribution, as only a small number of features can be mapped.

To obtain a more comprehensive picture of the macroscopic porosity distribution, we developed a semi-automated workflow for high-resolution pore space mapping and classification in BHIs. We focus on greyscale-converted images rather than raw resistivity data because they are more commonly available for legacy wells. Our workflow applies simple thresholding to generate binary porosity maps from both static (linear conversion of resistivity to brightness) and dynamic images (with histogram equalisation). The dynamic map is grafted onto the static map in areas identified as dark or bright in the blurred static image, resulting in a millimetre-scale porosity map of the borehole wall. Following interpolation between the imager pads and/or flaps, geometric properties are extracted for each connected cluster of mapped pixels, allowing classification of pore types as fractures, vugs, or karst features. The workflow performs well in limestone and dolostone sequences with high resistivity contrast between matrix and pore space, but is less reliable in marly intervals and in sections affected by poor borehole or data quality. We are currently developing an updated, fully automated workflow leveraging machine learning algorithms for pore space segmentation and classification.

As a first application, we analysed BHIs from the North Alpine Foreland Basin in Bavaria, where the Upper Jurassic hosts a hydrothermal reservoir. Of the 16 good-quality BHIs analysed, visual inspection indicates that 13 produced reliable results. By combining the derived macroscopic porosity with available matrix porosity measurements, total porosity can be estimated along the well path. Integration with additional well data enables us to define porosity–permeability trends for active flow zones, elucidate controls on pore space distribution, and derive realistic porosity ranges for reservoir model parameterisation.

How to cite: Bamberg, B., Gajjala, G. R., and Zosseder, K.: Semi-automated porosity mapping in carbonate reservoirs using borehole images, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12538, https://doi.org/10.5194/egusphere-egu26-12538, 2026.

Understanding the mechanisms of hydrocarbon migration, accumulation, and alteration, particularly how evolution controls these processes, is critical for exploring lithologic hydrocarbons in reservoirs. In the complex tectonic settings of the continental margin of the stable North China Craton, there is a significant presence of small yet highly prolific hydrocarbon reservoirs. The processes of hydrocarbon migration and accumulation are complex and thus represent an important research focus in geology. This study, based on core, logging, and seismic data and integrating fluid inclusion analysis, quantitative fluorescence techniques, and geochemical experiments, combines the shale smear factor and paleotectonic reconstructions to clarify the hydrocarbon accumulation episodes, migration pathways, and factors controlling reservoir adjustments in the Yanwu area of the Tianhuan Depression in the Ordos Basin, China. The results reveal three types of NE-trending left-lateral strike-slip faults: linear, left-stepping, and right-stepping. Shale Smear Factor (SSF) analysis confirms that these faults exhibit segmented opening behaviors, with SSF > 1.7 identified as the threshold for fault openness. Multiparameter geochemical tracing based on terpanes and steranes shows that lateral migration along fault zones dominates the preferential migration pathways for hydrocarbons. Fluid inclusion thermometry revealed homogenization temperatures within the 100–110°C and 80–90°C intervals, while the oil inclusions exhibit blue or blue-and-white fluorescence, reflecting early hydrocarbon charging and late-stage secondary migration. Integrated analysis indicates that during the late Early Cretaceous (105–90 Ma), hydrocarbons were charged upward through open segments of linear strike-slip fault zones in the northern study area, experiencing lateral migration and accumulation along high-permeability sand bodies and unconformities in the shallow strata. Since the Late Cretaceous (65 Ma–present), the regional tectonic framework has evolved from a west-high, east-low to a west-low, east-high configuration, inducing secondary hydrocarbon migration and leading to the remigration or even destruction of early-formed oil reservoirs. This study systematically demonstrates that fault activity and tectonic evolution control the accumulation and distribution of hydrocarbons in the region. These findings provide theoretical insights for hydrocarbon exploration in regions with complex tectonic evolution within stable cratonic basins.

How to cite: Yang, Z.: Influencing factors of hydrocarbon migration and adjustment at the edge of a stable cratonic basin: Implications from fluid inclusions, quantitative fluorescence techniques, and geochemical tracing, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15641, https://doi.org/10.5194/egusphere-egu26-15641, 2026.