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Sedimentary facies modeling is a critical approach for understanding geological phenomena, yet the strong heterogeneity of reservoir systems poses a serious challenge for their refined characterization. In this study, we innovatively propose an interpretable attention-guided generative adversarial network framework with dual-domain learning, which achieves precise sedimentary facies modeling under the constraints of well facies and soft probability data. Specifically, we first effectively extract and preserve prior information of sedimentary facies models from both spatial and frequency domain perspectives. Then, during simulation, to enhance the capability of the network model for finely characterizing complex heterogeneous models, cross-spatial attention mechanisms are designed to effectively capture short-range and long-range dependencies between multi-scale pattern features. Additionally, through systematic feature map visualization analysis, we elucidate the processes of conditional fitting and complex sedimentary facies model reconstruction, intuitively demonstrating the functional mechanisms of each module. Finally, systematic experiments are conducted on multiple datasets to validate the effectiveness of the proposed method. The results demonstrate that the generated sedimentary facies models exhibit high consistency with training datasets in terms of visual realism and statistical indicators. Quantitative comparisons reveal remarkable performance of the method, achieving low Wasserstein distance (0.09), Kernel Inception Distance (0.0017) and Kernel Maximum Mean Discrepancy (0.21). These findings further confirm the high realism of the generated realizations regarding pattern features. This study offers a reliable and practical method for geological reservoir modeling, thereby advancing quantitative, precise geological research with broad application prospects.

This paper discusses the formation and preservation of a fluvial tufa system influenced by Atlantic climate based on stratigraphical, chronological (amino-acid racemization, AAR), sedimentological and stable-isotope analyses. On the southwestern Iberian Peninsula, the tufas and associated deposits of the Almonda River valley occur as isolated terraced bodies and reach 25 m thick. AAR dated most deposits to within the warm Marine Isotope Stage 5 (MIS-5). Two Holocene ages were reset within MIS-5 based on diverse criteria. Widely varied carbonate and minor allochthonous coarse detrital facies occur arranged in four simple vertical associations. The deposit geometry and facies association distribution correspond to a low- to moderate-sloped fluvial valley consisting of several short knickpoints and extensive flat areas between them. The latter are occupied by slow-flowing water facies (carbonate sand, lime mud, phytoclast and oncoid rudstones, and up-growing stem boundstones). Facies that formed in moderate- to high-slope substrates were stromatolite, moss and down-growing stem boundstones. The homogeneous Miocene bedrock lithology and gentle structural deformation propitiated this depositional architecture. Calcite δ 13 C and δ 18 O values suggest that the aquifer water provided the outflowing Almonda water with (1) 18 O-enriched water, compared with present precipitation and groundwater δ 18 O values, and (2) 13 C-depleted CO 2 from bituminous rocks and vegetation cover in the catchment. The proximity to the Atlantic coast favoured the Mesozoic-rock aquifer recharge with 18 O-enriched water precipitation, assuring water availability during the formation of the studied tufas. No evidence of frequent intense erosion phases might indicate stable precipitation regimes, which would have allowed the preservation of loose fine-grained and palustrine deposits.

This paper proposes a novel approach for generating 3-dimensional complex geological facies models based on deep generative models. It can reproduce a wide range of conceptual geological models while possessing the flexibility necessary to honor constraints such as well data. Compared with existing geostatistics-based modeling methods, our approach produces realistic subsurface facies architecture in 3D using a state-of-the-art deep learning method called generative adversarial networks (GANs). GANs couple a generator with a discriminator, and each uses a deep convolutional neural network. The networks are trained in an adversarial manner until the generator can create “fake” images that the discriminator cannot distinguish from “real” images. We extend the original GAN approach to 3D geological modeling at the reservoir scale. The GANs are trained using a library of 3D facies models. Once the GANs have been trained, they can generate a variety of geologically realistic facies models constrained by well data interpretations. This geomodelling approach using GANs has been tested on models of both complex fluvial depositional systems and carbonate reservoirs that exhibit progradational and aggradational trends. The results demonstrate that this deep learning-driven modeling approach can capture more realistic facies architectures and associations than existing geostatistical modeling methods, which often fail to reproduce heterogeneous nonstationary sedimentary facies with apparent depositional trend.

This study presents a comprehensive characterization of the Neogene-Quaternary formations within the offshore Temsah gas field, which is approximately 65 km NNW of Port Said offshore the Nile Delta Basin. By integrating seismic and well log data, we constructed a detailed static reservoir model to address the complex geological structures and evaluate hydrocarbon potential. The Temsah offshore gas field has a complex geological structure characterized by numerous normal faults aligned in the two fault systems one northeast-southwest and another northwest-southeast trending. The predominance of clastic rocks and petroleum system setting in the Nile Delta Basin make this location favourable for hydrocarbon accumulations. Utilizing data from four wells and twenty-nine seismic lines, an in-depth interpretation was conducted to overcome the challenge of precise seismic feature delineation. Static reservoir model incorporates detailed three-dimensional visualizations of sandstones and shale layers, with a particular focus on the Upper Sandstone of Kafr El-Sheikh and Sidi Salem formations. Detailed three-dimensional model illustrated the structural impact on the studied reservoirs. Shale volume, water saturation property distribution grids complemented a constructed 3D model depicting the spatial distribution of rock facies and petrophysical parameters within the Temsah gas field. The integrated approach facilitated the identification and evaluation of three hydrocarbon prospect locations with estimated Gas Initially In Place (GIIP) ranging from approximately 2,856,782 Billion Cubic Feet [BCF] to 5,332,660 [BCF]. These findings underscore the substantial potential for advancing field development in the Temsah gas field.

The term orogenic gold deposits has been widely accepted, but there has been continuing debate on their genesis. Early syn-sedimentary or syn-volcanic models and hydrothermal meteoric-fluid models are now invalid. Magmatic-hydrothermal models fail because of the lack of consistent spatially associated granitic intrusions and inconsistent temporal relationships. The most plausible models involve metamorphic fluids, but the source of these fluids is equivocal. Intra-basin sources within deeper segments of the hosting supracrustal successions, the underlying continental crust, subducted oceanic lithosphere with its overlying sediment wedge, and metasomatized lithosphere are all potential sources. Several features of Precambrian orogenic gold deposits are inconsistent with derivation from a continental metamorphic-fluid source. These include the presence of hypozonal deposits in amphibolite-facies domains, their anomalous multiple sulfur isotopic compositions, and problems of derivation of gold-related elements from devolatilization of dominant basalts in the sequences. The Phanerozoic deposits are largely described as hosted in greenschist-facies domains, consistent with supracrustal devolatilization models. A notable exception is the Jiaodong gold deposits of China, where ca. 120-Ma gold deposits are hosted in Precambrian crust that was metamorphosed over 2000 million years prior to gold mineralization. Other deposits in China are comparable to those in the Massif Central and elsewhere in France, in that they are hosted in amphibolite-facies domains or clearly post-date regional metamorphic events imposed on hosting supracrustal sequences. If all orogenic gold deposits have a common genesis, the only realistic source of fluid and gold is from devolatilization of a subducted oceanic slab with its overlying gold-bearing sulfide-rich sedimentary package, or the associated metasomatized mantle wedge, with CO2 released during decarbonation and S- and ore-related elements released from transformation of pyrite to pyrrhotite at about 500 °C. Although this model satisfies all geological, geochronological, isotopic, and geochemical constraints, and is consistent with limited computer-based modeling of fluid release from subduction zones, the precise mechanisms of fluid flux are model-driven and remain uncertain. From an exploration viewpoint, the model re-emphasizes the ubiquitous occurrence of orogenic gold deposits in subduction-related orogenic belts and importance of continental-scale lithosphere-tapping fault and shear zones to focus large volumes of auriferous fluid. It confirms the importance of the consistent spacing between world-class deposits, broadly equivalent to the depth of the Moho, as derived from empirical observations.

Sedimentary formations that compose most aquifers are difficult to model as a result of the nature of their deposition. Their formation generally involves multiple processes (alluvial, glacial, lacustrine, etc.) that contribute to the complex organization of these deposits. Representative models can be obtained using process‐based or rule‐based methods. However, such methods have several drawbacks: complicated parameterization, large computing time, and challenging, if not impossible, conditioning. To address these problems, we propose a new simple hierarchical surface‐based algorithm, named EROSim. First, a predefined number of stochastic surfaces are simulated in a given order (from older to younger). These surfaces are simulated independently but interact with each other through erosion rules. Each surface is either an erosive or a deposition surface. The deposition surfaces represent the boundaries of depositional events, whereas the erosive surfaces can remove parts of the previously simulated deposits. Finally, these surfaces delimit sedimentary regions that are filled with facies. The approach is quite simple, general, flexible, and can be conditioned to borehole data. The applicability of the method is illustrated using data from fluvio‐glacial sedimentary deposits observed in the Bümberg quarry in Switzerland. Key Points A new surface‐based stochastic facies modeling algorithm is presented It is flexible and relies on few parameters to produce a variety of geological settings The proposed method can efficiently reproduce observed fluvio‐glacial structures

In this paper, we present the construction of a geomechanical conceptual model for Permian–Triassic reservoirs of the Persian Gulf, achieved through a comprehensive comparison between geological facies, wireline data, and geomechanical parameters. Integrating geological and geomechanical data enabled the spatial distribution of key geomechanical parameters and the development of a conceptual model. This model offers valuable insights into the mechanical behavior of the various parts of the reservoir. Our database includes petrographical analysis of 1577 thin sections of 403 m of cores, routine core analysis, wireline logs, and geomechanical data in one well. Also, wireline logs from 6 other wells were used for correlation. Thin section studies showed 12 microfacies that have been deposited in a ramp depositional environment. Geomechanical data including Young modulus (E), Poisson ratio modulus (ϑ), shear modulus (G), bulk modulus (K), Schmidt hammer, and unconfined compressive stress (UCS), compared with geological and petrographical results. Electrofacies were constructed with the use of wireline log data. The incorporation of geomechanical data allowed for the construction of five geomechanical facies. The geomechanical features exhibit a progressive increase from one to five, indicating an inverse relationship with the reservoir quality of the electrofacies. Geomechanical units were defined by grouping similar geomechanical facies. Then, a relationship was established between geomechanical units and sea level changes. Subsequently, these units were correlated with sequence stratigraphic units and matched across the other six wells through the utilization of wireline logs. The spatial distribution of geomechanical units was determined by establishing their correlation with both geological facies and sequence stratigraphic units. Each geomechanical unit corresponds to the same depth interval of a systems tract belonging to a third-order sequence and a complete fourth-order sequence. This enabled us to detect and analyze the variations in the distribution patterns of geomechanical properties across the study area.

This study investigates the factors influencing the scale dependence of dispersivity and the dispersivity upscaling theory in heterogeneous sediments. A series of tracer experiments are first conducted to reveal the evolution of dispersivity across scales. These experiments contain various sedimentary structures, including several nearly‐homogeneous column tests, a heterogeneous column test, a horizontally stratified tank experiment, a randomly filled tank experiment, and a three‐dimensional tank experiment utilizing an analogous simulation to a field‐scale site. The impact of impervious boundaries, sedimentary architectures, and heterogeneity on the dispersivity scaling is assessed by controlling transport distance, setup dimension, facies volume proportions, and facies distribution. Finally, the Lagrangian‐based models, applicable for bounded and unbounded sediments, are employed to examine the relationship between various heterogeneous structures and dispersivity variations. The results indicate that the transport uncertainty introduced by dispersivity scaling is relatively weak in nearly‐homogeneous or stratified media but prominent in complex heterogeneous media. Hydraulic conductivity variance and space correlation structure in sediments contribute greatly to the value and increased rate of dispersivity. The predictive capabilities of transport models can be significantly improved by incorporating detailed facies indicator data and accounting for sediment heterogeneity. Although the impervious boundary enhances longitudinal dispersion by restricting transverse dispersion, the promoting effect decreases with the boundary spacing. The Lagrangian‐based models with detailed facies indicator data effectively capture the dispersivity variation trend with travel distance. The complementary use of the bounded and unbounded models can help better identify the scale‐dependent dispersivities, ultimately leading to more effective contaminant mitigation strategies. Plain Language Summary Dispersivity is a measure that helps us understand the speed and extent to which contaminants spread in the subsurface. It is a challenging factor to consider when simulating the movement of pollutants in the subsurface. In this study, we conducted several experiments to examine how dispersivity changes with the distance that contaminants travel. We used Lagrangian‐based models to analyze the relationships between the different scales and the evolution of dispersivity. Our findings show that the rate at which dispersivity increases with distance is influenced by the arrangement of different soil layers (facies) and the differences in how easily water can pass through them (permeability). The presence of an impermeable boundary affects dispersivity by limiting the spread of contaminants in certain directions. We found that both types of Lagrangian‐based models (bounded and unbounded) can accurately represent the changes in dispersivity seen in our experiments, but the bounded model is slightly more accurate. Key Points Multiple experiments are conducted to study the scale dependence of dispersivity Dispersivity variation in different sediments across scales is evaluated The impact of impervious boundaries on dispersivity scaling is investigated

Evaluating grouLindwater quality and associated hydrochemical properties is critical to manage groundwater resources in arid and semiarid environments. The current study examined groundwater quality and appropriateness for agriculture in the alluvial aquifer of Makkah Al-Mukarramah Province, Saudi Arabia, utilizing several irrigation water quality indices (IWQIs) such as irrigation water quality index (IWQI), total dissolved solids (TDS), sodium adsorption ratio (SAR), potential salinity (PS), magnesium hazard (MH), and residual sodium carbonate (RSC) assisted by multivariate modeling and GIS tools. One hundred fourteen groundwater wells were evaluated utilizing several physicochemical parameters, which indicating that the primary cation and anion concentrations were as follows: Na+ > Ca2+ > Mg2+ > K+, and Cl− > SO42˗ > HCO3˗ > NO3˗ > CO32˗, respectively, reflecting Ca–HCO3, Na–Cl, and mixed Ca–Mg–Cl–SO4 water facies under the stress of evaporation, saltwater intrusion, and reverse ion exchange processes. The IWQI, TDS, SAR, PS, MH, and RSC across two studied regions had mean values of 64.86, 2028.53, 4.98, 26.18, 38.70, and − 14.77, respectively. For example, the computed IWQI model indicated that approximately 31% of samples fell into the no restriction range, implying that salinity tolerance crops should be avoided, while approximately 33% of samples fell into the low to moderate restriction range, and approximately 36% of samples fell into the high to severe restriction range for irrigation, implying that moderate to high salt sensitivity crops should be irrigated in loose soil with no compacted layers. The partial least squares regression model (PLSR) produced a more accurate assessment of six IWQIs based on values of R2 and slope. In Val. datasets, the PLSR model generated strong estimates for six IWQIs with R2 varied from 0.72 to 1.00. There was a good slope value of the linear relationship between measured and predicted for each parameter and the highest slope value (1.00) was shown with RSC. In the PLSR models of six IWQIs, there were no overfitting or underfitting between the measuring, calibrating, and validating datasets. In conclusion, the combination of physicochemical characteristics, WQIs, PLSR, and GIS tools to assess groundwater suitability for irrigation and their regulating variables is beneficial and provides a clear picture of water quality.

This study investigates generative adversarial networks (GANs)’ capacity to model multi-facies distributions of meandering systems. Earlier works showed that GANs outperform geostatistical methods in reproducing complex geometry, like the shapes of fluvial channels. However, the reproduction of geological complexity and geological realism remains an issue when modelling fluvial depositional systems. Meandering systems deposit multiple facies and change facies shape following the migration of rivers. Sand accretes at the inner bank of channels, forming the point bar and erodes the plain at the outer bank to create sediments. Channel fills with mud or sand at the bottom after abandonment due to avulsions or meander cut-offs. Those sedimentary processes yield complex geological patterns. This paper proposes further developing a GAN model, Fluvial GAN, to learn complex multi-facies fluvial patterns across depositional variability. We create a set of meandering facies models by a process-based model, F L U M Y T M , for training a GAN and assessing how well it can learn fluvial facies distributions representing sedimentary processes. Fluvial GAN has three distinct enhancements: (i) a One-Hot Encoder for better handling of multi-facies distribution, (ii) a Hybrid-discriminator for better learning geological patterns, and (iii) an improved loss function to prevent mode collapse. We compare Fluvial GAN performance with two more standard configurations using qualitative and quantitative geological features assessments. Fluvial GAN vastly reduces the occurrence of a typical unrealistic feature, channels forming isolated loops, which we called ‘closed channel’ in this study. We analyse the diversity of Fluvial GAN generations via a dimensionality reduction algorithm, UMAP, that plots the training dataset and Fluvial GAN generations together in a 2D space. Fluvial GAN provides good coverage of the uncertainty space represented by the training dataset.