Image-based deep learning methods, especially convolutional neural networks (CNNs), are gaining traction in seismic interpretation, but their application still demands manual validation. This study compares a U-Net structured CNN, called Fault-Net, with conventional edge-enhancing seismic attributes of variance and chaos that serve as a scientific baseline. We adopted two seismic interpretation workflows: (1) conventional attributes to enhance fault features; and (2) a deep learning-based workflow for fault segmentation using CNNs. Both workflows were applied to a high-resolution 3D seismic dataset from the structurally complex deep-water Orange Basin (offshore South Africa). While deep learning-based software packages are commercially available, it is unclear whether they are suitable for the Orange Basin and for use in an academic setting due to their proprietary architectures and generally closed training data. This study provides public evidence of the feasibility of automated structural interpretation in complex seismic datasets using deep learning, revealing both key benefits and limitations. Where high-quality labelled data are available, the deep learning approach is faster and tends to produce a cleaner and more accurate depiction of larger faults compared to conventional methods. The open availability of Fault-Net makes deep learning-based interpretation particularly advantageous for academic settings, offering significant time and resource efficiency while enhancing the understanding of complex subsurface structures.

Fault permeability prediction typically relies on empirical relationships between permeability and clay content or burial depth. Calibration of such methods relies on either core data or subsurface observations of fluid pressure and hydrocarbon contact offsets across faults. Published core data suggest that no relationships exist between host rock clay content and fault permeability for phyllosilicate-framework fault rocks, whereas published subsurface calibration data suggest otherwise. We here present a new method for calibration of fault rock permeabilities to subsurface pressure data. This approach is an analogue from electrical engineering to compute fault permeabilities across all major faults in a study area and optimize depth and clay content dependence of fault permeabilities. We tested this method at the Jurassic section of the Norwegian Njord Field area, where faults span the depth range of 2.4–4.5 km and the lowest clay content in individual faults varies between 8.9 to 25.7%. The flow-restricting faults at Njord are phyllosilicate-framework fault rocks. Our modelling shows that fault rock permeability decreases with burial depth, whereas the clay content has nearly no impact. Sensitivity analyses show that these results are robust. Therefore, SGR-based algorithms for fault seal prediction cannot be expected to give good fault permeability predictions for seismic-scale faults at Njord.

The oil and gas industry faces challenges in estimating reservoir properties in regions without well data. To overcome this, a combination of seismic attributes and drilled well information is used to predict the unknown drilled portion of the reservoir. Integrating these datasets enhances geological and flow models, leading to better reservoir predictability and improved strategies for reservoir development and production. This study focuses on a lacustrine carbonate environment and employs a stratigraphic-sedimentological modelling approach. The aim is to understand sedimentation times and propose a growth model for carbonate build-up based on prior integrated studies using seismic attributes. The modelling methodology was applied to the BM-C-33 Exploratory Hydrocarbon Block in the Campos Basin, offshore Brazil. The Macabu Formation was chosen as the target unit. Seismic attribute-driven carbonate mound features were identified and categorized into different sedimentation periods. Geological processes were then modelled, incorporating parameters such as topographical and bathymetric surfaces, lake-level variations, subsidence mapping, and rates of carbonate deposition. Four depositional domains for lacustrine carbonates were considered: a high-energy sediment domain, a build-up domain, a low-energy sediment domain and a clayey sediment domain. By integrating seismic attributes and well data, more reliable growth models of the carbonate mounds were developed. The results demonstrate the efficiency of the methodology in improving the understanding and representation of carbonate reservoirs, facilitating the characterization of the studied region, and mitigating associated project risks such as identifying new drilling locations.

The main Aptian pre-salt in situ facies were modelled in 3D pore scale to evaluate the impact of diagenetic textures; specifically, the influence of matrix-replacive dolomite on the pore-system development. Our objective is to evaluate how these textures affect residual oil saturation ( S or ) under water- and oil-wet conditions through pore-scale simulations. We developed 12 models with varying proportions of dolomite and calcite spherulites, three models with calcite shrubs, 21 models with shrubs and regularly spaced dolomite, and nine models with shrubs and heterogeneously arranged dolomites. The methodology involved evaluating the tortuosity, surface area and size distribution of pores and throats. Additionally, the quasi-static morphology method was used to estimate the S or . The results indicated that dolomite significantly affects the pore system, leading to a more uniform medium, a decrease in the throat/pore size ratio and an increase in surface area. An increase of dolomite decreases S or in water-wet conditions. Conversely, in oil-wet simulations, increasing dolomite leads to an increase in oil entrapment. Previous research on waterflood experiments concluded that in situ facies with a high content of replacive dolomite tend to show low S or . Hence, it is probable that much of the oil trapped in these rocks is a result of the snap-off under water- or mixed-wet conditions.

The geochemical data of the gas sourced from the Majiagou Formation indicate low organic content and high thermal maturity, which make the evaluation of the hydrocarbon generative potential difficult. However, the low organic abundance and high thermal maturity of the Majiagou Formation make it difficult to evaluate its hydrocarbon generation potential. The Study of the genesis of pyrite can provide a better understanding of a source rock depositional environment and its evolution. In this paper, we report a significant amount of pyrite linked to the hydrocarbon source in the Majiagou Formation. Light and electron microscopy were used to observe the morphology pyrite while sulfur, carbon, and oxygen isotope were used to investigate the geochemical characteristics of pyrite and the surrounding rock matrix. The results showed that macroscopic stellate pyrite filled cracks and is found in veins or along the laminar surface, as well as agglomerated pyrite; microscopically, pyrite is dominated by autotypic pyrite in the form of cubes, pentagonal dodecahedrons, and columns. The δ 34 S CDT ranged from 4.6‰ to 27.5‰, δ 13 C PDB values ranged from −3.4‰ to −2.3‰, and δ 18 O PDB values ranged from −10.5‰ to −7.3‰. Our findings, combined with the geological background and thermal history of the region, the pyrite in the Majiagou Formation was formed by thermochemical sulfate reduction. Indicating that large-scale hydrocarbon generation events have occurred. The rocks of the Majiagou Formation had the ability to generate hydrocarbons. The research can be utilized as supporting material for the exploration and development of natural gas fields in the Ordos Basin.

Carbonate ramp systems present significant seismic interpretation challenges due to their pronounced facies heterogeneity, which frequently results in chaotic seismic outputs that obscure the underlying geological structures. The Porto Badisco Calcarenite in Salento, southern Italy, an Oligocene carbonate ramp, serves as the case study for this research, offering an analogue for understanding similar geological systems. By integrating fieldwork, laboratory analysis and MATLAB modelling, this study pioneers the use of detailed petrophysical data to construct innovative velocity models based on the velocity ranges of the different lithofacies analysed. These models distinctly illustrate the impact of facies heterogeneity on seismic velocities, providing fresh insights into acoustic impedance and variable propagation velocities across different facies constituting the carbonate ramp. Through advanced high-resolution synthetic seismic modelling conducted on carefully fine-tuned unmigrated stack sections, the research demonstrates how variations in petrophysical characteristics within measured ranges reflecting carbonate textures can dramatically alter seismic imaging. The innovative models, based on propagation velocity ranges, not only deepen the understanding of the seismic representation of lithofacies but also act as a potent tool for probing the subsurface architecture of complex carbonate systems, providing an interpretative key for the analysis of seismic images. This approach signifies a substantial advancement in seismic modelling that is aimed at refining interpretations and enhancing exploration strategies in carbonate ramp environments globally.

This study investigates the petroleum potential of mudstones of the Late Cretaceous Haymana Formation in the Haymana Basin, Turkey. The lithofacies, pore structure and source-rock characteristics of the mudstones are examined using stratigraphic, sedimentological, petrophysical and organic geochemical methods along four stratigraphic sections and other sampling sites. The depositional model presents a facies distribution within a submarine fan system. According to the bulk mineralogy, the identified lithofacies are mixed mudstone, mixed siliceous mudstone, marl, mixed carbonate mudstone, argillaceous/siliceous mudstone and clay-rich siliceous mudstone. XRD and mercury intrusion measurements suggest that the macropores (>50 nm) of the mudstones formed by dissolution of calcite, while mesopores (2–50 nm) developed around the clay–quartz/feldspar. Of the analysed samples, no lithofacies class is distinct with any specific range of porosity or permeability, which suggests a strong heterogeneity in pore throat size, mineral content and grain size. The black shale from the NW of the basin with a total organic carbon (TOC) content of 1.19%, S1 value of 0.07 mg g −1 , S2 value of 1.01 mg g −1 and a T max value of 441°C is a relatively more mature source rock, although it still exhibits a poor petroleum potential. Overall, the TOC values (average of 0.38%) of the mudstones suggest organic-poor rock characteristics for the Haymana Formation in the studied parts of the Haymana Basin.

This contribution is an introduction to the thematic collection ‘Digitally enabled geoscience workflows: Unlocking the power of our data’. The goal of the collection is to show how advances in data-science are transforming the process of scientific research and fueling a new generation of energy geoscience workflows. These workflows are providing game-changing advances in terms of time saving on complex tasks, improved consistency and repeatability of interpretation, and utilization of scarce experienced geoscientists. Eight articles have been accepted for publication as part of this thematic collection, five in Petroleum Geoscience and three in Geoenergy . We provide a short summary of each of these contributions and hope that this collection will provide inspiration and examples of the breadth of workflows that can be transformed by embracing the coming wave of digital technologies. This thematic collection resulted from an open call for papers on the theme of ‘Digitally enabled geoscience workflows: Unlocking the power of our data’. Eight contributions have been accepted for publication, five in Petroleum Geoscience and three in Geoenergy . Although the energy geoscience industry typically employs statistical workflows that are highly data intensive, it has been relatively slow to adopt modern data-science technologies. This is a result of historical reliance on established methods, the cost and complexity of adopting new technologies, and cultural and organizational challenges. However, with improved computing power and growing interest in data sciences, this is now changing rapidly, with the development and application of data-driven workflows an active area of research in energy geoscience. It is expected that the publication of research contributions in this area will continue to accelerate, with this collection providing a useful summary of the current key emerging themes.

Exploration and development practices have proved that fracturability evaluation is the primary basis for successful fracturing operations. Current studies mainly rely on using traditional physical parameters to evaluate shale fracturability. However, fracture morphology and the extension of natural fractures during fracturing are also crucial for shale fracturability evaluation. In this study, correlations between factors and rock fracture complexity and the degree of natural fracture opening were analysed using 40 sets of related experiments, and a new method is proposed for shale fracturability evaluation that considers Young's modulus, the shear expansion angle, residual strain, the approach angle and the stress variance coefficient. Finally, taking the shale gas field in the Sichuan Basin as an example, a comprehensive evaluation model of shale fracturability was established using the grey correlation method combined with core experimental data. One well was selected in order to compare and analyse the fracturability profile with the stimulated reservoir volume. The results show that peak strain has the most significant influence on shale fracture complexity, followed by the shear expansion angle and Young's modulus. The approach angle and the stress difference coefficient affect the degree of natural fracture opening at the same time. The model is accurate because the fracturability values match the study block's stimulated reservoir volume data. This new method of shale fracturability evaluation provides important technology for predicting the fracturability of shale gas reservoirs and optimizing operation schemes.

The petroleum system elements in the Komombo Basin have not yet been fully assessed. This study aims to evaluate source rocks, reservoirs and seals within the basin by integrating 2D seismic profiles and well logs together with geochemical, core and petrophysical data. Four source rocks are identified within the Cretaceous shale intervals of the B Member of the Six Hills, upper Maghrabi, Quseir and Dakhla formations. The quality of the B Member, upper Maghrabi and Quseir source rocks is good to very good, while the Dakhla Formation demonstrates good to excellent quality. Geochemical characteristics vary across the basin, with higher kerogen quality and thermal maturity observed in the depocentre compared to the flanks. Four reservoirs are recognized in the basin, including the A Member, C Member, sandstones within the D–G members of the Six Hills Formation, and the Sabaya and Maghrabi formations. The A Member reservoir demonstrates a moderate reservoir quality, while the C Member reservoir displays a fair quality. Numerous sandstones with 15–25% porosity values are observed within the D–G members. The Sabaya–Maghrabi reservoir generally exhibits good to very good quality, and is characterized by high porosity and varying permeability. Due to their high organic matter content, the Dakhla and Quseir formations show potential as unconventional reservoirs. Several shale units within the Komombo Basin serve as potential seal rocks. These include the B Member of the Six Hills, Abu Ballas, upper Maghrabi and Taref formations, as well as intra-formational shales within the reservoir rocks. Seismic interpretation indicates that faults are the predominant trapping mechanism in the basin.