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Microscopic pore structure forms the foundation for studying shale gas adsorption and transport mechanisms and for establishing geological models. However, most current methods for analyzing microporous structure through physical experiments are time-consuming and labor-intensive. Hence, there is a need to automate pore segmentation and extract pore microstructural information from shale SEM images quickly and accurately. This will significantly enhance the efficiency of digital rock analysis and related computational simulations. This study used scanning electron microscopy (SEM) images of shale from a certain region in China to investigate the relationship between the microscopic structure of shale pores and the macroscopic permeability. Firstly, a semantic image segmentation model called TransUnet, based on deep learning, was used to segment the pore images and extract the micro-pore structure parameters. Then, the relationship between the macroscopic permeability parameters and the micro-pore structure was analyzed using a fractal apparent permeability calculation model. Finally, the permeability of the shale was calculated to improve the efficiency of geological exploration and reduce experimental costs. The experimental results show that this study provides an effective image processing method for the SEM quantification of shale microstructure and extraction of permeability parameters. • The paper highlights the importance of understanding the microscopic pore structure in studying shale gas adsorption and transport mechanisms and establishing geological models. • The study emphasizes that current methods for analyzing microporous structures through physical experiments are time-consuming and labor-intensive, thus necessitating the automation of pore segmentation and extraction of microstructural information from shale images. • The study used scanning electron microscopy images of shale from a specific region in China to investigate the relationship between microscopic pore structure and macroscopic permeability. • The study employed a deep learning-based semantic image segmentation model called TransUnet to segment pore images and extract micro-pore structure parameters. • The study analyzed the relationship between macroscopic permeability parameters and micro-pore structure using a fractal apparent permeability calculation model, ultimately calculating the permeability of the shale to improve geological exploration efficiency and reduce experimental costs. [ABSTRACT FROM AUTHOR]

Geological parameterization entails the representation of a geomodel using a small set of latent variables and a mapping from these variables to grid-block properties such as porosity and permeability. Parameterization is useful for data assimilation (history matching), as it maintains geological realism while reducing the number of variables to be determined. Diffusion models are a new class of generative deep-learning procedures that have been shown to outperform previous methods, such as generative adversarial networks, for image generation tasks. Diffusion models are trained to "denoise", which enables them to generate new geological realizations from input fields characterized by random noise. Latent diffusion models, which are the specific variant considered in this study, provide dimension reduction through use of a low-dimensional latent variable. The model developed in this work includes a variational autoencoder for dimension reduction and a U-net for the denoising process. Our application involves conditional 2D three-facies (channel-levee-mud) systems. The latent diffusion model is shown to provide realizations that are visually consistent with samples from geomodeling software. Quantitative metrics involving spatial and flow-response statistics are evaluated, and general agreement between the diffusion-generated models and reference realizations is observed. Stability tests are performed to assess the smoothness of the parameterization method. The latent diffusion model is then used for ensemble-based data assimilation. Two synthetic "true" models are considered. Significant uncertainty reduction, posterior P 10 –P 90 forecasts that generally bracket observed data, and consistent posterior geomodels, are achieved in both cases. • Latent diffusion model (LDM) developed for low-dimensional geomodel parameterization. • LDM combines generative diffusion model with an autoencoder latent representation. • LDM is applied for the parameterization of 2D, three-facies channelized systems. • LDM-generated models preserve geological features and provide proper flow statistics. • Method is successfully applied for history matching with the ESMDA algorithm. [ABSTRACT FROM AUTHOR]

Full-waveform inversion (FWI) represents an advanced geophysical imaging technique focused on intricately depicting subsurface physical properties by iteratively minimizing the differences between the simulated and observed seismograms. Unfortunately, the conventional FWI utilizing a least-squares loss function suffers from various drawbacks, including the challenge of local minima and the necessity for human intervention in parameter fine-tuning. It is particularly problematic when handling noisy data and inadequate initial models. Recent works have exhibited promising performance in two-dimensional FWI by integrating structural sparse representation to procure adaptive dictionaries. Drawing inspiration from the competitiveness of structural sparse representation, we introduce a paradigm of group sparse residuals that integrates two types of complementary prior information by harnessing both the internal and external subsurface media models. The proposed algorithm is based on an alternate minimization algorithm to guarantee workflow flexibility and efficient optimization capabilities. We experimentally validate our method for two baseline geological models, and a comparison of the results demonstrates that the proposed algorithm faithfully recovers the velocity models and consistently outperforms other traditional or learning-based algorithms. A further benefit from the group sparse coding used in this method is that it reduces the sensitivity to data noise. • The complementary regularization combines with sparse representation in a coupled dictionary. • The group sparse residual incorporates the non-local self-similarity prior from internal and external datasets into the FWI process. • The double complementary regularization into the ADMM framework improves the reconstruction quality for seismic imaging. [ABSTRACT FROM AUTHOR]

In deep geothermal reservoirs, faults and fractures play a major role, serving as regulators of fluid flow and heat transfer while also providing feed zones for production wells. To accurately model the operation of geothermal fields, it is necessary to explicitly consider objects of varying spatial scales, from the reservoir scale itself, to that of faults and fractures, down to the scale of the injection and production wells. Our main objective in developing the ComPASS geothermal flow simulator, was to take into account all of these geometric constraints in a flow and heat transfer numerical model using generic unstructured meshes. In its current state, the code provides a parallel implementation of a spatio-temporal discretization of the non-linear equations driving compositional multi-phase thermal flows in porous fractured media on unstructured meshes. It allows an explicit discretization of faults and fractures as 2D hybrid objects, embedded in a 3D matrix. Similarly, wells are modeled as one dimensional graphs discretized by edges of the 3D mesh which allows arbitrary multi-branch wells. The resulting approach is particularly flexible and robust in terms of modeling. Its practical interest is demonstrated by two case studies in high-energy geothermal contexts. • ComPASS is an open-source, evolutive, massively parallel platform for hydrothermal modeling. • ComPASS core is written in compiled high-performance and a Python API allows simulation setup. • ComPASS uses non-isothermal Coats formulation to solve multi-phase, multi-component Darcy flows. • ComPASS enables simulations of large and complex geothermal reservoirs involving highly non-linear problems. [ABSTRACT FROM AUTHOR]

In the past years, there is a growing interest in the applications of Generative Adversarial Networks (GANs) to generate geological models. Although GANs have proven to be an effective tool to learn and reproduce the complex data patterns present in some geological models, some challenges still remain open. Among others, a well-noticed problem is the need for a large number of samples to ensure high-quality training, which can be prohibitively expensive in some cases. As an attempt to offer a (possibly partial) solution to the aforementioned challenge, in this study, we investigate the feasibility and effectiveness of a zero-centered discriminator regularization technique for improving the performance of a GAN. Additionally, we evaluate an adaptive data augmentation technique to overcome the potential issue of limited training data, for the purpose of generating geologically feasible realizations of hydrocarbon reservoir models. Our findings demonstrate that a combination of the two techniques lead to notable performance improvements of a GAN. Particularly, it is observed that using the adaptive data augmentation technique in a GAN can yield similar results to those obtained by the GAN with a much larger dataset. • Generative Adversarial network to generate geological models. • Application of zero-centered gradient penalty as discriminator regularization. • Application of adaptive discriminator augmentation. • Gradient penalty coefficient should be higher than that previously suggested. • Good performance to generate geological models when available dataset size is limited. [ABSTRACT FROM AUTHOR]

During the process of visualization, format exchange, and spatial analysis, the 3D geological model tends to emphasize its geometric features, thereby diminishing its geological significance to some extent. However, extracting corresponding geological elements directly from the model based solely on the pure geometric features of geologic bodies proves to be difficult and few studies have focused on related problems. This research aims to extract geological elements from existing geological models under the constraints of geological knowledge to enhance the reusability of existing models and the efficacy of their applications in subsequent research. Firstly, each stratum is assigned its geological significance under the constraints of geological knowledge. Then, the study introduces extraction methods for the topographic interface, eroded interface, stratigraphic top and bottom interfaces, and various constraint boundaries. Furthermore, the potential importance of the studies presented in this paper and their application scenarios are analyzed and explored. Finally, the feasibility and effectiveness of the method for extracting geological elements are validated through a case study. This method holds significant scientific importance for efficiently updating and conducting fine application analyses of geological models. Additionally, this research provides valuable insights that enhance the efficiency of model updating, property model construction, and the splicing of block models across extensive areas. • Methods for extracting geological elements from 3D models within the constraints of geological knowledge were proposed. • The methods can effectively extract geological elements from 3D models with no or minimal geological implications. • Extraction results can assist geologists in understanding the geological evolution of the study area. • The methods can enhance the efficiency of model updating and property building. [ABSTRACT FROM AUTHOR]

Fault dating plays an essential role in understanding deformation histories and modeling the tectonic evolution of orogenic belts. However, direct fault dating methods via different isotope geochronological techniques are expensive, and their use is often limited in many cases, making it essential to develop a fast and low-cost fault relative dating method. Therefore, on the basis of knowledge graphs and knowledge reasoning technology, this study proposes an automatic method to relatively date periods of fault activity using the principle of cross-cutting relationships between faults and strata. The method mainly involves (1) generating the knowledge graph based on a digital geological map; (2) using the knowledge reasoning algorithm to interpret the cross-cutting relationships amongst faults and generating the temporal sequence of fault activity; (3) relative dating the faults based on the cross-cutting relationships between faults and strata; and (4) according to the temporal sequence of fault activity, the relationship between faults can be revealed, and relative dating can be optimized. Results for cases in western Nevada and Qixia Hill of Nanjing illustrate the effectiveness of this method for interpreting the period of fault activity. The accuracy rates of the recognition results in the two cases were 90.24% and 80.77%, respectively, which means that the proposed method has the potential to relatively date fault activity across large areas. The algorithm is an effective supplement to the existing direct method of fault dating. The algorithm can efficiently infer the development sequence and the age of fault activity based on geological maps and geological cross-sections, which is of great significance for understanding regional tectonic history, evaluating earthquake disasters, and modeling tectonic evolution processes. • The principle of cross-cutting relationships was used to interpret the sequence and period of geological faults automatically. • Using the knowledge graph to define the spatial models of different geological relationships. • Knowledge reasoning was used to infer temporal and spatial relationships between faults. • The inferred fault development sequence and period information will aid fault recovery and simulation in large-scale areas. [ABSTRACT FROM AUTHOR]

Seismic facies inversion is an important process in the oil and gas industry to estimate subsurface geological facies or rock types based on seismic data. Recently, the Ensemble Smoother with Multi Data Assimilation (ES-MDA) has shown great success in solving complex inverse problems by generating an ensemble of solutions of the model variables for uncertainty quantification. However, there are significant challenges related to the computational cost, Gaussian assumptions, and its application for categorical model variables. Recent developments have proposed to integrate ES-MDA with Variational Autoencoder (VAE) to reparametrize facies into a continuous feature space and reduce computational costs. Despite promising results, it has introduced new limitations concerning network convergence, spatial correlation loss, and manipulation of the latent space. In this paper, a Fully Convolutional Variational Autoencoder (FCVAE) was proposed to outperform the VAE in terms of preserving spatial information, requiring fewer network parameters, presenting higher dimension reduction and yielding better facies estimation. The objective of the application example is to draw sound conclusions based on a 2D synthetic application, where the results of both methods can be compared with reference facies for quality evaluation. • Fully Convolutional VAE to reparametrize facies models. • Latent space with spatial correlation. • Seismic Facies Inversion with ES-MDA and FCVAE. • Pre-salt synthetic dataset with four facies. • Comparison between ES-MDA-FCVAE and ES-MDA-VAE. [ABSTRACT FROM AUTHOR]

Data-driven three-dimensional (3D) mineral prospectivity modeling (MPM) employs diverse 3D exploration indicators to express geological architecture and associated characteristics in ore systems. The integration of 3D geological models with 3D computational simulation data enhances the effectiveness of 3D MPM in representing the geological architecture and its coupled geodynamic processes that govern mineralization. Despite variations in modality (i.e., data source, representation, and information abstraction levels) between geological models and simulation data, the cross-modal gap between these two types of data remains underexplored in 3D MPM. This paper presents a novel 3D MPM approach that robustly fuses multimodal information from geological models and simulation data. Acknowledging the coupled and correlated nature of geological architectures and geodynamic processes, a joint fusion strategy is employed, aligning information from both modalities by enforcing their correlation. A joint fusion neural network is devised to extract maximally correlated features from geological models and simulation data, fusing them in a cross-modality feature space. Specifically, correlation analysis (CCA) regularization is utilized to maximize the correlation between features of the two modalities, guiding the network to learn coordinated and joint fused features associated with mineralization. This results in a more effective 3D mineral prospectivity model that harnesses the strengths from both modalities for mineral exploration targeting. The proposed method is evaluated in a case study of the world-class Jiaojia gold deposit, NE China. Extensive experiments were carried out to compare the proposed method with state-of-the-art methods, methods using unimodal data, and variants without CCA regularization. Results demonstrate the superior performance of the proposed method in terms of prediction accuracy and targeting efficacy, highlighting the importance of CCA regularization in enhancing predictive power in 3D MPM. • Casting 3D mineral prospectivity modeling (MPM) as a deep multimodal fusion problem. • Joint fusion network for integrating features of 3D geological models and simulation data. • Canonical correlation analysis regularization to guide network fusion of the cross-modal gap. • Maximizing the correlation of multimodal information boosts the performance of 3D MPM. [ABSTRACT FROM AUTHOR]

The simultaneous prediction of the subsurface distribution of facies and acoustic impedance (I P) from fullstack seismic data requires solving an inverse problem and is fundamental in natural resources exploration, carbon capture and storage, and environmental risk management. In recent years, deep generative models (DGM), such as variational autoencoders (VAE) and generative adversarial networks (GAN), were proposed to reproduce complex facies patterns honoring prior geological information. Variational Bayesian inference using inverse autoregressive flows (IAF) can be performed to infer the solution to a geophysical inverse problem from the encoded latent space of such pre-trained DGM. Successful applications of such approach on crosshole ground-penetrating radar synthetic data inversion demonstrated that the technique's accuracy is comparable to that of Markov chain Monte Carlo (MCMC) inference methods, while significantly reducing the computational cost. Nonetheless, these application examples did not account for the spatial uncertainty affecting the facies-dependent continuous physical property, from which the geophysical data are calculated. This uncertainty can significantly affect the inversion accuracy and its applicability to real data. In this work, specific VAE and GAN architectures are proposed to simultaneously predict facies and co-located I P , while accounting for their spatial uncertainties. The two types of generative networks are used in Bayesian inversion with IAF for the inversion of seismic data. The results are found to reproduce the statistics of the training images and solve the seismic inversion problem accurately, comparably to MCMC inversion. Furthermore, advantages and limitations of the two DGMs are evaluated by comparing the results obtained. • We propose a VAE and a GAN for multivariate modeling honoring of facies and co-located acoustic impedance. • Resulting realizations honor geological prior and assumed spatial statistics. • We improve uncertainty quantification in variational Bayesian inference using VAE and GAN. • The generative networks are combined with inverse autoregressive flows for fullstack seismic data inversion. • We obtain improvements in subsurface reconstruction and uncertainty quantification. [ABSTRACT FROM AUTHOR]

Topological relationship identification of geological boundaries greatly affects 3D geological modeling efficiency. However, due to the complex spatial distribution and unstructured data architecture of geological boundaries, the current topological identification still mainly depends on the manual operation and interpretation, which is time-consuming and labor-intensive. And geologists' judgments are based on knowledge and experience that are challenging to replicate through conventional algorithmic approaches. To address these challenges, a graph neural network-based geological boundary topological relationships automatic identification method is proposed. Based on graph theory, we conceptualize geological boundaries and their topological relationships as a graph transforming the identification task into an edge prediction problem within this topological graph. We employ the variational graph auto-encoder (VGAE) model to predict unknown edges in the topological graph, enabling automatic identification of geological boundary topological relationships. A geological information transformation method is proposed to extract and encode the geological boundaries' features as inputs for VGAE. The proposed method is verified in an engineering case study. The results reveal that this method can efficiently and accurately analyze the geological boundary topological relationships. Moreover, it can greatly reduce the manual workload, requiring only 30% of the manual effort to obtain the geological boundary topological relationships, while achieving an average prediction accuracy of approximately 95%. • Construct geological boundary topological relationship graphs based on graph theory. • Convert topological analysis into edge prediction in graphs for automation. • Use variational graph auto-encoder to predict unknown edges in topological graphs. • Suggest a method for geological boundary feature extraction and encoding. [ABSTRACT FROM AUTHOR]

For complex geological reservoir modeling, some numerical-simulation-based methods, such as traditional multiple-point statistics (MPS), cannot extract nonstationary patterns of training images (TIs) effectively. CPU-intensive calculations require that variables information only be stored in RAM instead of other storage mediums to avoid huge time consumption if many simulations are performed successively. Generative Adversarial Network (GAN) modeling methods are able to overcome these issues, but when the training datasets are limited, mode collapse easily occurs. Meanwhile, fixed receptive fields might cause the problem that the corresponding reconstruction cannot take local features and global features of the TIs into account simultaneously. Therefore, we propose the Concurrent Multi-Stage U-Net GAN (Con-MSUGAN) based on a single TI. The pyramid structure is used for spatial pattern multi-scale representation in Con-MSUGAN, which can capture the corresponding feature information of TIs under different scales. Meanwhile, concurrent training patterns can make network parameters in adjacent stages correlated with each other to accelerate network convergence speed, thereby realizing stochastic multi-scale reconstructions. Stationary channel and nonstationary delta facies TIs were used to verify the performance of Con-MSUGAN. Results show that simulation of different datasets using this method are more similar in terms of spatial variance, connectivity degree and facies type frequency distribution to the original TIs. Meanwhile, muti-dimensional-scaling (MDS) plots show small discrepancies between simulation results and the corresponding TI. It indicates that Con-MSUGAN can overcome the problem that complex spatial patterns are hard to reproduce. In addition, saved network parameters can be reused. A variety of equiprobability simulation results can be obtained rapidly, thereby realizing stochastic reconstruction of geological reservoir models. • Both global and local features can be captured under multi-scale representation. • The concurrent training pattern accelerates network convergence. • Complex spatial patterns can be reproduced by Con-MSUGAN. [ABSTRACT FROM AUTHOR]

In the field of geo-electromagnetic modeling, the unstructured tetrahedral mesh technique holds great potential for realistically simulating various complex geological structures. However, its practical application is limited by the difficulty in building complex geological models and performing tetrahedral meshing. To solve these problems, we propose a novel workflow for geological modeling and tetrahedral meshing, specifically tailored for electromagnetic modeling. Additionally, we have developed a batch code interface that utilizes MATLAB and COMSOL software to reduce manual operations, efficiently construct geophysical models featuring complex geological interfaces and irregular three-dimensional (3D) structures. We will illustrate the workflow using a magnetotelluric (MT) example in a karst geological context. Firstly, we use Non-Uniform Rational B-Splines (NURBS) modeling, point cloud contour extraction, Boolean operations, and geometric operations to reconstruct complex geological structures and load geophysical exploration devices. Secondly, we devise a specialized mesh sequence to achieve local refinement and generate high-quality unstructured tetrahedral meshes. Finally, we have successfully conducted the first MT forward modeling of such fine geological structures, such as topographies, karst caves and tunnels. This study presents a novel approach to complex geophysical modeling with a straightforward, and open-source workflow, which can significantly enhance modeling efficiency. • Open-source workflow for the geo-electromagnetic modeling of intricate structures. • The 3D geomodelling accurately reconstructs topography, strata, caves and tunnels. • Adaptive algorithm for extracting irregular 3D geological boundaries from point cloud. • 3D magnetotelluric forward modeling of complex karst tunnel model. [ABSTRACT FROM AUTHOR]

We present an open source MATLAB package, IGUG, for 3D inversion of gravity data. The algorithm implemented in this package is based on methodology that was introduced by Bijani et al. (2015). A homogeneous subsurface body is modeled by an ensemble of simple point masses. The model parameters are the Cartesian coordinates of the point masses and their total mass. The set of point masses, assumed to each have the same mass, is associated to the vertices of a weighted complete graph in which the weights are computed by the Euclidean pairwise distances separating vertices. Kruskal's algorithm is used to solve the minimum spanning tree (MST) problem for the graph, yielding the reconstruction of the skeleton of the body described by the model parameters. The algorithm is stabilized using an equidistance function that restricts the spatial distribution of point masses and favors a homogeneous distribution for the subsurface structure. The non-linear global objective function for the model parameters comprises the data misfit term and the equidistance stabilization function. A regularization parameter λ is introduced to balance the two terms of the objective function, and reasonable physically-relevant bound constraints are imposed on the model parameters. A genetic algorithm is used to minimize the bound constrained objective function for a fixed λ , subject to the bound constraints. A new diagnostic approach is presented for determining a suitable choice for λ , requiring a limited number of solutions for a small set of λ. This contrasts the use of the L-curve which was suggested for estimating a suitable λ in Bijani et al. (2015). Simulations for synthetic examples demonstrate the efficiency and effectiveness of the implementation of the algorithm. It is verified that the constraints on the model parameters are not restrictive, even with less realistic bounds acceptable approximations of the body are still obtained. Included in the package is the script GMD.m which is used for generating synthetic data and for putting measurement data in the format required for the inversion implemented within IGUG.m. The script Diagnostic_Results.m is included within IGUG.m for analyzing and visualizing the results, but can also be used as a standalone script given import of prior results. The software can be used to verify the simulations and the analysis of real data that is presented here. The real data set uses gravity data from the Mobrun ore body, north east of Noranda, Quebec, Canada. • A MATLAB software package for inversion of gravity data is provided. • The package can be used for simulated and real data sets. • The estimated point mass distribution of the subsurface body is obtained. • The regularization parameter is efficiently estimated by regression analysis. • The genetic algorithm parameters can be user defined. [ABSTRACT FROM AUTHOR]

Ensemble-based methods have been applied with remarkable success for data assimilation in geosciences. However, they sometimes fail to preserve the geological realism of the model, which is particularly evident in reservoirs with complex facies distributions. This occurs mainly because of the underlying Gaussian assumptions on model parameters that are inherent in these methods. This fact has encouraged an intense research activity to develop Gaussian parameterizations in a latent space that maps into geologically-realistic facies realizations. Despite the large number of publications, the development of robust parameterizations for facies remains an open problem. Deep learning techniques have been delivering impressive results in a number of different areas and the first applications in data assimilation in geoscience have started to appear in the literature. The present paper reports the current results of our investigations on the use of deep neural networks towards the construction of a continuous parameterization of facies, which can be used for data assimilation with ensemble methods. Specifically, we use a convolutional variational autoencoder and the ensemble smoother with multiple data assimilation. We tested the parameterization in three synthetic history-matching problems with channelized facies. We focus on this type of facies because they are among the most challenging to preserve after the assimilation of data. The parameterization showed promising results, outperforming previous methods and generating well-defined channelized facies. However, more research is still required before deploying these methods for operational use. In particular, it is necessary to investigate procedures to improve the reconstruction accuracy in three-dimensional cases and reduce the computational requirements to train the networks. • Novel parameterization for facies history matching combining deep learning and ensemble smoother. • Use of a convolutional variation autoencoder to map discrete facies values into continuous parameters. • Assimilation of static and dynamic (production) data using the method ensemble smoother with multiple data assimilation. • Tested in three history-matching problems, outperforming previous methods and generating well-defined channelized facies. [ABSTRACT FROM AUTHOR]

The study in this paper proposed a new framework for history matching of frequently acquired 4D seismic data. To achieve this goal, the large volumes of seismic data from the many repeated 4D monitors are firstly condensed into a single attribute by directly correlating them to the reservoir production and injection performances. This 'well2seis' cross-correlation is achieved by defining a linear relationship between pressure and saturation-related 4D seismic responses and the cumulative changes of reservoir fluid volumes derived from wells. It is shown that such a cross-disciplinary attribute not only reduces the amount of 4D seismic data for history matching, but also enhances the seismic data reliability since the 4D seismic observations are conditioned by low-uncertainty production data from reservoir engineering domain. In the second step, Morris sensitivity analysis is adapted to fast diagnose the uncertainty reservoir model parameters that are sensitive to the well2seis attributes. To quantitatively assimilate the well2seis observations to calibrate these uncertainty parameters, we then proposed a well2seis objective function that quantifies the mismatch between the observed and model simulated well2seis attributes. Ensemble Smoother with Multiple Data Assimilation (ES-MDA) is performed at the end to iteratively assimilate the well2seis observations by minimizing the well2seis misfit. Application of the proposed workflow to a North Sea field case shows that, when history matching to the observed well2seis attribute that honours the information from seismic and reservoir engineering domains, it can significantly reduce the uncertainty of key reservoir parameters, hence improving the matching quality to both 4D seismic and production observations and enhancing the prediction reliability of the reservoir models. Compared to traditional history matching approaches that attempt to match individual seismic time-lapse attributes and production observations, this approach is observed to significantly boost the history matching efficiency by reducing the number of time-consuming iterations. • We developed and applied a framework for assimilating frequently acquired 4D seismic data to update reservoir models. • The proposed method introduces a new attribute for seismic history matching. • We deploy Morris sensitivity analysis and ES-MDA to analyze and calibrate uncertainty parameters in reservoir models. • The proposed framework shows to boost history matching efficiency on frequently acuquired 4D seismic data. [ABSTRACT FROM AUTHOR]

Abstract Modeling geological boundaries is an important step in geological modeling workflows that aim to quantify the location, volume, and uncertainty of a resource. Several well established explicit and implicit techniques can be used to identify the limits of an ore zone; however, most techniques are unable to account for uncertainty in a straightforward manner, where uncertainty originates from the sparseness of sample data relative to the volume of the resource in question. An implicit technique that benefits from simplicity and has the capabilities to incorporate and account for uncertainty in boundary geometry is signed distance function (SDF) modeling. Sample data that is in the form of line segments from drill holes presents challenges for interpolators such as kriging and radial basis functions. Utilizing linear regression as an interpolator several advantages to the problem are realized. The particular variant of regression that is used is moving least squares (MLS) that permits the exact integration of line data from drill holes and human interpretations without the need for discretization or numerical integration approaches. Integrating line data is critical for including explicit interpretations into implicit boundary models because geologic interpretations commonly involve polygons digitized on two dimensional sections through a field of drill holes. Highlights • A least squares regression approach is proposed for geologic boundary modeling. • Line data like drill holes and geologic interpretations can be integrated exactly. • The technique is amenable to evaluating boundary uncertainty. [ABSTRACT FROM AUTHOR]

Abstract The standard mine modelling practice often involves investing significant effort into the interpretation of the deposit and identification of the geological domains using the chemical assays and geophysics. These domains and their accuracy play a key role in grade estimation using spatial modelling approaches, such as Gaussian processes and kriging. However, the domains developed for grade estimation do not always produce regions with well-defined correlations between the material types that are present in the deposit. The material type is based on the ore characteristics, such as mineralogy, texture and other visible petrological properties. Therefore a new and potentially more flexible methodology for domaining of material types is needed. This study applies t-SNE, SOM and SPADE to the material type data to embed high-dimensional data into low dimensions and thus facilitate clustering. These methods were tested on a banded iron formation hosted iron ore deposit located in the Hammersley region of Western Australia. All three methods produced clusters that were purer mixtures of the material types than the original domains. Due to the geologist input SPADE produced clusters closest to the original domains. However, this may not be the best clustering for material type modelling. Additionally, SPADE produced the best results for the ore, and highlighted how the user input can focus this method on the region of greatest interest. t-SNE and SOM are more automatic, but had results that were further from the original clusters. t-SNE identified clusters that were better spatially grouped than SOM, which was generally the most affected by the high variation in material within the detritals. Highlights • Accurate domaining is key for estimation using spatial modelling approaches. • T-SNE, SOM and SPADE were applied to cluster geological iron ore data. • High-dimensional data was embedded into low dimensions to facilitate grouping. • SPADE had the best results for ore, highlighting how users can focus the results. • t-SNE and SOM are more automatic, but the results were further from the domains. [ABSTRACT FROM AUTHOR]

Abstract In numerical modeling of geological carbon sequestration (GCS), uncertainty quantification (UQ) is usually needed to evaluate the impact of uncertain model parameters on model predictions caused by limited measurements and incomplete knowledge of the parameters. However, UQ for GCS is computationally expensive due to the large ensemble of complex and lengthy model simulations. In this study, we propose an adaptive Kriging method to build a fast-to-evaluate surrogate of the GCS model to alleviate the heavy computational burden. The surrogate model is efficiently generated using a Taylor expansion-based adaptive experimental design algorithm that combines a distance-based exploration criterion and an exploitation criterion to adaptively search for informative training samples. In addition, we analyze the uncertainty brought by substituting the surrogate for the actual simulation model and explore its influence on UQ results. Our method is demonstrated in a synthetic GCS model and its performance is evaluated in comparison with the conventional Monte Carlo sampling. Results indicate that our method can greatly improve the computational efficiency in UQ and provide an effective and reliable UQ solution with the consideration of surrogate uncertainty. Highlights • An adaptive Kriging surrogate method is developed for efficient uncertainty quantification. • The training samples for surrogate construction are sequentially selected using an adaptive experimental design algorithm. • The uncertainty arising from substitution of the surrogate model for the actual model is rigorously quantified. • The proposed method is demonstrated using a geological carbon sequestration model. [ABSTRACT FROM AUTHOR]

Geological modeling currently uses various computer-based applications. Data harmonization at the semantic level using ontologies is essential to make these applications interoperable. Since geo-modeling is part of several multidisciplinary projects, interoperability requires semantic harmonization to exchange information between geological applications and integrate other domain knowledge at a general level. Therefore, domain ontologies that describe geological knowledge must be based on a sound ontological background to ensure this knowledge is integrable. Faults are essential for understanding and solving structural problems but are complex to model because the concept of fault includes a group of geological entities with a distinct ontological nature. A fault can correspond to thin, deformed rock volumes or spatial arrangements resulting from the displacement of geological blocks, but at a broader scale, geologists describe faults as surfaces or components of complex fault arrays. Our work intends to harmonize these views by presenting a domain ontology, GeoFault, resting on the Basic Formal Ontology (BFO) and the GeoCore ontology. GeoCore and GeoFault support the parametric description of geological sites as a preliminary step for quantitative and qualitative analysis. We have proposed GeoFault after systematically revising the literature and several knowledge-acquisition sessions with expert structural geologists. The ontology formalizes a vocabulary for fault "sensu stricto," excluding ductile shear deformations. It covers the regional to outcrop scales, excluding structures at the microscopic, orogenic, and tectonics scales, and it avoids interpretive language associated with geological processes as far as possible. Extending the BFO and GeoCore ontologies allows the fault concept to be related to formal ontological classes in a consistent semantic-rich framework. The ontology artifact is implemented in OWL 2, validated by competency questions with two use cases, and tested using an in-house ontology-driven data entry application. The GeoFault ontology is publicly available and provides a solid framework for clarifying fault knowledge and a foundation for many applications. • Highlight 1: Modeling the descriptive knowledge related to geological faults. • Highlight 2: GeoFault is based on the top-level ontology BFO and on the core ontology GeoCore. • Highlight 3: An ontological analysis of the concept of faults both as solid material and geological structure. • Highlight 4: GeoFault is validated by means of two use cases (one outcrop, one subsurface). • Highlight 5: GeoFault is applied with an in-house ontology-driven application to test is validity. [ABSTRACT FROM AUTHOR]

The 3D modeling method based on parallel geological cross-sections is suitable for modeling large-scale bedrock geological bodies. However, there are usually more branching, pinch-out strata, deformation structures (such as faults and folds), and the correspondence problem of geological boundaries between adjacent cross-sections. These problems have become the most crucial obstacle in the current application of 3D geological modeling based on parallel geological cross-sections. To this end, this paper proposes a topological reasoning automatic processing method for the consistency of parallel geological cross-sections to obtain topologically consistent adjacent cross-sections efficiently. The method involves (1) Stratigraphic inference based on the distance and topological relationships, transforming strata with m:n relationships into three relatively simple cases of 1:1, 1:n, and 1:0; (2) Splitting branching strata based on the Voronoi diagram, transforming strata with 1:n relationships into 1:1 relationships; (3) Dealing pinch-out strata based on the method of filling virtual strata, transforming strata with 1:0 relationships into 1:1 relationships (4) Topologically consistent processing of stratigraphic boundaries based on automatic recognition of no-corresponding arcs and addition of virtual stratum. Based on experiments in the Longtoushan Hill area in Jiangsu Province, China, and the southeast side of Chandler Mountain, Alabama, USA, this method can effectively replace the many manual processes such as determining the location of pinch-out adding auxiliary cross-sections and performing branching strata correspondence in the current application of 3D geological modeling based on arc segments. The geological cross-sections processed based on this method have an exemplary data structure and consistent topological relationships. The 3D geological modeling algorithm based on parallel cross-sections has the advantages of low algorithm complexity, high automation, and stable modeling quality. • An automated method for topology-consistent processing of parallel geological cross-sections was developed. • The strata with corresponding complex relationships can be converted into one-to-one corresponding arc segment sets. • The method lays a sound data foundation for 3D modeling using parallel cross-sections. [ABSTRACT FROM AUTHOR]

Discrete fracture networks (DFN) are widely used to build refined geological models. However, validating whether a refined model can match to reality is a crucial problem, concerning whether the model can be used for analysis. The current validation methods include numerical validation and graphical validation. However, the graphical validation, aiming at estimating the similarity between a simulated trace map and the real trace map by visual observation, is subjective. In this paper, an algorithm for the graphical validation of DFN is set up. Four main indicators, including total gray, gray grade curve, characteristic direction and gray density distribution curve, are presented to assess the similarity between two trace maps. A modified Radon transform and loop cosine similarity are presented based on Radon transform and cosine similarity respectively. Besides, how to use Bézier curve to reduce the edge effect is described. Finally, a case study shows that the new algorithm can effectively distinguish which simulated trace map is more similar to the real trace map. [ABSTRACT FROM AUTHOR]

Modeling complex geological interfaces is a common task in geosciences. Many data sources are available for geological interface modeling, including borehole data and geophysical surveys. Geological knowledge, such as the delineation from geologists, is difficult to quantify but likely adds value to geological interface modeling. To integrate all information, we present a data-knowledge-driven trend surface analysis method to construct stochastic geological interfaces. We design a Metropolis–Hastings sampling framework to sample stochastic trend interfaces and quantify the uncertainty of geological interfaces given all information sources. This method is suitable for both explicit and implicit representations of geological interfaces. We demonstrate our method in three different test cases: modeling stochastic interfaces of Greenland subglacial topography, magmatic intrusion, and buried river valleys in Australia. • This new trend surface analysis method integrates various data types and geological knowledge. • A Markov chain Monte Carlo sampler is used for explicit and implicit geological interfaces. • Stochastic geological interfaces are sampled in three studies: subglacial topography, magmatic intrusion, and palaeovalley modeling. [ABSTRACT FROM AUTHOR]

The assessment of off-road trafficability is an important issue in special military or disaster relief operations. It is affected by geographical and geological environmental factors, and the factor sensitivity changes with space. Previous research used a qualitative manual rule method and geographical factors to assess off-road trafficability in terms of land coverage and terrain factors. However, this caused problems because the factors were not comprehensive, the method was qualitative, and there was a great deal of noise in the assessment results. To solve these problems, we developed a quantitative rule method to comprehensively assess the obstacles caused by geographical and geological factors to off-road trafficability. First, we reviewed remote sensing images of various factors in the study area, searched for traffic rules, and quantified them using the analytic hierarchy process-weighted information content (AHP-WIC) method. Then, the corresponding relationship between rules and off-road trafficability is established by the clustering method, and it is divided into multiple types of motion (generally>2 types). Finally, we used the improved watershed method to filter off-road trafficability map noise. The results showed that the geological environment (including geological hazards, joint & structure, topographic position index, rock structure, etc.) can seriously hinder off-road trafficability but has not been considered in previous research. For complex field environments, the proposed method can adaptively establish the classification relationship between rules and off-road trafficability. Additionally, the improved watershed method can effectively filter the noise generated by the rule method. • This study employs the coupling constraint of geographic and geological factors to assess off-road trafficability. • The study proposes the Analytic Hierarchy Process-Weighted Information Content (AHP-WIC) to quantify the traffic rules generated by geographic and geological environmental factors. • In order to adaptively establish the classification relationship between traffic rules and trafficability, we use the Jenks natural breaks method to classify the comprehensive information content of traffic rules. • To improve the accuracy of the off-road trafficability map, we propose an improved watershed method to filter noise in off-road trafficability maps. [ABSTRACT FROM AUTHOR]

Geoscientific models manage myriad and increasingly complex data structures as trans -disciplinary models are integrated. They often incur significant redundancy with cross-cutting tasks. Reflection, the ability of a program to inspect and modify its structure and behavior at runtime, is known as a powerful tool to improve code reusability, abstraction, and separation of concerns. Reflection is rarely adopted in high-performance Geoscientific models, especially with Fortran, where it was previously deemed implausible. Practical constraints of language and legacy often limit us to feather-weight, native-language solutions. We demonstrate the usefulness of a structural-reflection-emulating, dynamically-linked metaObjects, gd . We show real-world examples including data structure self-assembly, effortless input/output (IO) and upgrade to parallel I/O, recursive actions and batch operations. We share gd and a derived module that reproduces MATLAB-like structure in Fortran and C++. We suggest that both a gd representation and a Fortran-native representation are maintained to access the data, each for separate purposes. Embracing emulated reflection allows generically-written codes that are highly re-usable across projects. [ABSTRACT FROM AUTHOR]

A geological model (geomodel) in three-dimensional (3D) space is a digital representation of the Earth's subsurface, recognized by geologists and stored in resultant geological data (geodata). The increasing demand for data management and interoperable applications of geomodelscan be addressed by developing standard-based exchange formats for the representation of not only a single geological object, but also holistic geomodels. However, current standards such as GeoSciML cannot incorporate all the geomodel-related information. This paper presents Geo3DML for the exchange of 3D geomodels based on the existing Open Geospatial Consortium (OGC) standards. Geo3DML is based on a unified and formal representation of structural models, attribute models and hierarchical structures of interpreted resultant geodata in different dimensional views, including drills, cross-sections/geomaps and 3D models, which is compatible with the conceptual model of GeoSciML. Geo3DML aims to encode all geomodel-related information integrally in one framework, including the semantic and geometric information of geoobjects and their relationships, as well as visual information. At present, Geo3DML and some supporting tools have been released as a data-exchange standard by the China Geological Survey (CGS). [ABSTRACT FROM AUTHOR]

Geological domains contain non-stationary features that cannot be described by a single direction of continuity. Non-stationary estimation frameworks generate more realistic curvilinear interpretations of subsurface geometries. A radial basis function (RBF) based implicit modeling framework using domain decomposition is developed that permits introduction of locally varying orientations and magnitudes of anisotropy for boundary models to better account for the local variability of complex geological deposits. The interpolation framework is paired with a method to automatically infer the locally predominant orientations, which results in a rapid and robust iterative non-stationary boundary modeling technique that can refine locally anisotropic geological shapes automatically from the sample data. The method also permits quantification of the volumetric uncertainty associated with the boundary modeling. The methodology is demonstrated on a porphyry dataset and shows improved local geological features. [ABSTRACT FROM AUTHOR]

A constrained Delaunay discretization method is developed to generate high-quality doubly adaptive meshes of highly discontinuous geological media. Complex features such as three-dimensional discrete fracture networks (DFNs), tunnels, shafts, slopes, boreholes, water curtains, and drainage systems are taken into account in the mesh generation. The constrained Delaunay triangulation method is used to create adaptive triangular elements on planar fractures. Persson's algorithm (Persson, 2005), based on an analogy between triangular elements and spring networks, is enriched to automatically discretize a planar fracture into mesh points with varying density and smooth-quality gradient. The triangulated planar fractures are treated as planar straight-line graphs (PSLGs) to construct piecewise-linear complex (PLC) for constrained Delaunay tetrahedralization. This guarantees the doubly adaptive characteristic of the resulted mesh: the mesh is adaptive not only along fractures but also in space. The quality of elements is compared with the results from an existing method. It is verified that the present method can generate smoother elements and a better distribution of element aspect ratios. Two numerical simulations are implemented to demonstrate that the present method can be applied to various simulations of complex geological media that contain a large number of discontinuities. [ABSTRACT FROM AUTHOR]

Short for Hexahedra from Unique Location in (K)convex Polyhedra – HULK is a simple and efficient algorithm to generate hexahedral meshes from generic STL files describing a geological model to be used in simulation tools based on the finite element, finite volume or finite difference methods. Using binary space partitioning of the input geometry and octree refinement on the grid, a successive increase in accuracy of the mesh is achieved. We present the theoretical basis as well as the implementation procedure with three geological models with varying complexity, providing the basis on which the algorithm is evaluated. HULK generates high accuracy discretizations with cell counts suitable for state-of-the-art subsurface simulators and provides a new method for hexahedral mesh generation in geological settings. [ABSTRACT FROM AUTHOR]

We present Vel-IO 3D, a tool for 3D velocity model creation and time-depth conversion, as part of a workflow for 3D model building. The workflow addresses the management of large subsurface dataset, mainly seismic lines and well logs, and the construction of a 3D velocity model able to describe the variation of the velocity parameters related to strong facies and thickness variability and to high structural complexity. Although it is applicable in many geological contexts (e.g. foreland basins, large intermountain basins), it is particularly suitable in wide flat regions, where subsurface structures have no surface expression. The Vel-IO 3D tool is composed by three scripts, written in Python 2.7.11, that automate i) the 3D instantaneous velocity model building, ii) the velocity model optimization, iii) the time-depth conversion. They determine a 3D geological model that is consistent with the primary geological constraints (e.g. depth of the markers on wells). The proposed workflow and the Vel-IO 3D tool have been tested, during the EU funded Project GeoMol, by the construction of the 3D geological model of a flat region, 5700 km 2 in area, located in the central part of the Po Plain. The final 3D model showed the efficiency of the workflow and Vel-IO 3D tool in the management of large amount of data both in time and depth domain. A 4 layer-cake velocity model has been applied to a several thousand (5000–13,000 m) thick succession, with 15 horizons from Triassic up to Pleistocene, complicated by a Mesozoic extensional tectonics and by buried thrusts related to Southern Alps and Northern Apennines. [ABSTRACT FROM AUTHOR]

A variational Gaussian process (VGP) model for implicit geological modeling is proposed, capable of modeling linear and non-linear trends. A new likelihood capable of honoring geological boundaries is introduced, and its performance is compared with the radial basis function (RBF) model. The model is tested on two case studies in mining, one of which relies only on photogrammetric data. The new approach generates more realistic surfaces on qualitative terms and provides an uncertainty measure. It is available as an open-source Python package. • A variational Gaussian process model for implicit geological modeling. • The model uses only discrete labels, not needing signed distances. • Anisotropy parameters can be learned semi-automatically. • Support for linear and non-linear trends. • A data-driven uncertainty measure is provided. [ABSTRACT FROM AUTHOR]

Markov chain Monte Carlo (McMC) methods are suitable for solving the high-dimensional geostatistical seismic inverse problem. However, traditional McMC is impractical to generate desired lithofacies distribution since the repeated geostatistics and seismic forward simulators are very time-consuming. Recently, various strategies (e.g., the sequential geostatistical resampling (SGR) idea, multi-scale strategy, and annealing process) have been individually designed to alleviate the computational burden. However, the coordination between multiple corresponding key parameters (e.g., blocking window sizes, grid level, and temperatures) adds difficulties to combining multiple strategies with McMC. In this context, we propose a Multi-scale Blocking Moving Window algorithm (MsBMW) to effectively combines blocking McMC updating, a multi-scale strategy, and a new simulated annealing (SA) algorithm. In the iterative process, we monitor the mean acceptance rate (AR) to update window sizes, grid level, and temperatures, keeping the AR within the desired range (e.g., between 25% and 50%) for more efficient sampling. To assess the performance of the MsBMW, we tested it on the Standford VI synthetic reservoir. The standard Metropolis-Hastings (MH) sampling is performed to present the posterior probability density function (pdf) as the reference in this study. Compared with the Blocking Moving Window algorithm (BMW) proposed by Alcolea and Renard (2010), the MsBMW allows better quality results in less time. The results show that the multi-scale approach has the effect of accelerating the convergence of inversion. The adaptive cooling schedule circumvents the problem that the cooling rate is difficult to control in SA. Finally, we applied the proposed methodology to two-dimensional (2-D) and three-dimensional (3-D) real study areas and quickly generated multiple realizations that fit geological patterns, well data, and seismic data for uncertainty evaluation. • The proposed method effectively integrates blocking McMC updating, multi-scale, and adaptive cooling schedule strategies. • The generated models are consistent with the expected geology, well data, and seismic data under Bayesian inversion. • The proposed models' mean acceptance rate (AR) can be kept in the desired range for more efficient sampling. [ABSTRACT FROM AUTHOR]

Deep learning (DL) has stimulated the intelligent processing and interpretation of seismic big data for oil and gas exploration in the past decade. In the applications to seismic stratigraphic modeling, most DL algorithms utilize a combination of 'convolution + pooling' to extract spatial features from seismic volume or images, which, however, may lead to a loss of the fine-grained information endowed in seismic time-series records inevitably. Here, we proposed a novel scheme for intelligent seismic stratigraphic interpretation based on temporal convolution network (TCN). The purpose of using TCN is to implement a trace-by-trace segmentation of lithologies by extracting spatio-temporal correlation of seismic record from a small amount of well log labels or manually interpreted geological profiles, thereby building a 3-D subsurface stratigraphic model. The proposed TCN model for seismic stratigraphic interpretation is trained, validated, and tested using the Netherlands F3 seismic dataset. The results suggest that, with a limited number of either manual interpretation profiles or well logs information, the proposed TCN algorithm can produce a highly accurate and laterally continuous 3-D stratigraphic model. Specifically, the mean pixel accuracy of prediction results of the above two schemes (compared to the real model) has achieved 98.23% and 96.09%, respectively. In addition, comparing TCN to the widely used encoder-decoder convolutional neural networks for seismic strata interpretation shows the outperformance of TCN scheme in terms of training time, model size, and prediction accuracy, while also revealing that the proposed TCN model can well identify thin strata in complex seismic data. • We propose a novel seismic stratigraphic interpretation method based on TCN model. • The proposed method identifies subsurface structures by capturing spatial-temporal correlation of seismic records. • The proposed model improves the performance of deep learning-based seismic stratigraphic interpretation. [ABSTRACT FROM AUTHOR]

Obtaining reliable sedimentary facies models is critical for geology, which primarily involves inferring conditional distributions from sparse wells. Multiple-point simulation (MPS) captures complex geological patterns by simulating multi-point probabilities, but it is sensitive to the stationarity of training images, and the limited training image is difficult to reflect the uncertainty of the prior geological models. Generative Adversarial Networks (GANs) have recently been proposed to spontaneously simulate multiple geological models that behave differently, generating realizations that respect spatial observations (hard data), with impressive results. In this paper, we explore a more flexible approach to conditioning synthetic realizations beyond hard data, allowing stylistic control over the realizations. We construct a conditional generative adversarial network consisting of a deep generator and a multi-scale discriminator as the conditional mapping model, and obtained realizations with improved visual quality. Based on this, style codes, which are numerical codes representing geological properties, are introduced to extend the existing mapping model. The resulting extended generative model can synthesize images that respect the hard data and exhibit the desired geological style. In addition, we collect and construct a dataset, which contains abundant and realistic fluvial patterns to reflect adequate prior geological knowledge. Extensive experiments have been carried out on this dataset, the results show that the generated images reflect absolute responses to style codes while maintaining the matching rate of 94.78% to the hard data, which can provide new ideas for conditional geological modeling. • Migrates geological modeling to image conditional generation. • A flexible and transparent conditional modeling approach for conditioning geological realizations. • A geological dataset containing realistic and rich river morphology. • Provides an alternative for geological modeling. [ABSTRACT FROM AUTHOR]

Urban underground spaces (UUS), especially those containing natural resources that have not yet been utilized, have been recognized as important for future sustainable development in large cities. One of the key steps in city planning is to estimate the quality of urban underground space resources, since they are major determinants of suitable land use. Yet geological constraints are rarely taken into consideration in urban planning, nor are the uncertainties in the quality of the available assessments. Based on Fuzzy Set theory and the analytic hierarchy process, a 3D stepwise process for the quality assessment of geotechnical properties of natural resources in UUS is presented. The process includes an index system for construction factors; area partitioning; the extraction of geological attributes; the creation of a relative membership grade matrix; the evaluation of subject and destination layers; and indeterminacy analysis. A 3D geological model of the study area was introduced into the process that extracted geological attributes as constraints. This 3D geological model was coupled with borehole data for Foshan City, Guangdong province, South China, and the indeterminacies caused by the cell size and the geological strata constraints were analyzed. The results of the case study show that (1) a relatively correct result can be obtained if the cell size is near to the average sampling distance of the boreholes; (2) the constraints of the 3D geological model have a major role in establishing the UUS quality level and distribution, especially at the boundaries of the geological bodies; and (3) the assessment result is impacted by an interaction between the cell resolution and the geological model used. [ABSTRACT FROM AUTHOR]

This paper introduces a software system (GeoCube) for three dimensional (3D) extraction and integration of exploration criteria from spatial data. The software system contains four key modules: (1) Import and Export, supporting many formats from commercial 3D geological modeling software and offering various export options; (2) pre-process, containing basic statistics and fractal/multi-fractal methods (concentration–volume (C–V) fractal method) for extraction of exploration criteria from spatial data (i.e., separation of geological, geochemical and geophysical anomalies from background values in 3D space); (3) assessment, supporting five data-driven integration methods (viz., information entropy, logistic regression, ordinary weights of evidence, weighted weights of evidence, boost weights of evidence) for integration of exploration criteria; and (4) post-process, for classifying integration outcomes into several levels based on mineralization potentiality. The Nanihu Mo (W) camp (5.0 km×4.0 km×2.7 km) of the Luanchuan region was used as a case study. The results show that GeoCube can enhance the use of 3D geological modeling to store, retrieve, process, display, analyze and integrate exploration criteria. Furthermore, it was found that the ordinary weights of evidence, boost weights of evidence and logistic regression methods showed superior performance as integration tools for exploration targeting in this case study. [ABSTRACT FROM AUTHOR]

In this paper, we propose a majorized Newton-CG augmented Lagrangian-based finite element method for 3D elastic frictionless contact problems. In this scheme, we discretize the restoration problem via the finite element method and reformulate it to a constrained optimization problem. Then we apply the majorized Newton-CG augmented Lagrangian method to solve the optimization problem, which is very suitable for the ill-conditioned case. Numerical results demonstrate that the proposed method is a very efficient algorithm for various large-scale 3D restorations of geological models, especially for the restoration of geological models with complicated faults. [ABSTRACT FROM AUTHOR]

The decision-making process in oil fields includes a step of risk analysis associated with the uncertainties present in the variables of the problem. Such uncertainties lead to hundreds, even thousands, of possible scenarios that are supposed to be analyzed so an effective production strategy can be selected. Given this high number of scenarios, a technique to reduce this set to a smaller, feasible subset of representative scenarios is imperative. The selected scenarios must be representative of the original set and also free of optimistic and pessimistic bias. This paper is devoted to propose an assisted methodology to identify representative models in oil fields. To do so, first a mathematical function was developed to model the representativeness of a subset of models with respect to the full set that characterizes the problem. Then, an optimization tool was implemented to identify the representative models of any problem, considering not only the cross-plots of the main output variables, but also the risk curves and the probability distribution of the attribute-levels of the problem. The proposed technique was applied to two benchmark cases and the results, evaluated by experts in the field, indicate that the obtained solutions are richer than those identified by previously adopted manual approaches. The program bytecode is available under request. [ABSTRACT FROM AUTHOR]

Geological reports aid in understanding exploration by providing valuable information on rock formation ,evolution and the geological environment in which deposits formed. Querying and extracting the critical information from these enormous historical geological report data helps in understanding the exploration risks in different geological settings. However, large amounts of unstructured text data occur in geological reports; therefore, it is challenging to obtain valuable information from them without performing information extraction and processing. This study proposed an automated method for extracting information from geological reports through triple extraction, then automatically constructs a geological knowledge graph from the extracted entities and relations. Simultaneously, due to the lack of samples, this study used multiple geological reports to construct a corpus of jointly extracted geological entities and relations. The proposed model reached an F1-score of 90.05% in the experimental results on the constructed corpus. Finally, a knowledge graph was constructed based on the extracted results to demonstrate the application value of the proposed method. The results showed that the structured information helps better represent the content of the source report and matches well with the geological domain knowledge. The proposed method can quickly and robustly convert textual data into a structured form that is convenient for reasoning and querying geological entities and relations. • Proposed a joint extraction model of triples for geological texts. • The extraction integrity of geological text triples was improved and the overlapping problems was solved. • A geological report knowledge graph was construced to discover new geological knowledge. [ABSTRACT FROM AUTHOR]

3D geological underground models are often presented by vector data, such as triangulated networks representing boundaries of geological bodies and geological structures. Since models are to be used for numerical simulations based on the finite difference method, they have to be converted into a representation discretizing the full volume of the model into hexahedral cells. Often the simulations require a high grid resolution and are done using parallel computing. The storage of such a high-resolution raster model would require a large amount of storage space and it is difficult to create such a model using the standard geomodelling packages. Since the raster representation is only required for the calculation, but not for the geometry description, we present an algorithm and concept for rasterizing geological models on the fly for the use in finite difference codes that are parallelized by domain decomposition. As a proof of concept we implemented a rasterizer library and integrated it into seismic simulation software that is run as parallel code on a UNIX cluster using the Message Passing Interface. We can thus run the simulation with realistic and complicated surface-based geological models that are created using 3D geomodelling software, instead of using a simplified representation of the geological subsurface using mathematical functions or geometric primitives. We tested this set-up using an example model that we provide along with the implemented library. [ABSTRACT FROM AUTHOR]

We present a workflow for building 3D geological models from oblique photogrammetry of outcrops. The workflow is used to build a 3D model of Kilen, a structurally complex and remotely located pseudo-nunatak in eastern North Greenland. The area was visited during two brief field seasons during which 1300 oblique photographs were taken by a hand held digital camera from a helicopter. The photos were triangulated and georeferenced and visible geological features as bedding and faults were mapped as 3D polylines. The polylines were used to calculate strike and dip of bedding and faults, generating a large number of structural information. These were imported into a 3D modeling software along with the 3D polylines, an unpublished digitized field map, a Digital Elevation Model, an orthophoto and georeferenced field observations. The 3D modeling software allows us to produce 2D cross-sections and 3D surfaces so that structural hypotheses could be tested through restoration and extrapolation of the data. This workflow proved effective in improving the structural knowledge of a remote area in the Arctic, showing that it is possible to produce quality 3D data from oblique photogrammetry and that those data can be used for 3D modeling. [ABSTRACT FROM AUTHOR]

The very complex near-surface geology in Denmark is a big challenge when constructing 3D geological models. Borehole data alone are normally insufficient for proper 3D modelling because data are too widespread. Therefore, Airborne ElectroMagnetic (AEM) techniques are often used to obtain supplementary information on the spatial distribution and composition of the geology. A large-scale AEM survey and high-resolution seismic data along with both new and existing borehole data and seismic data from hydrocarbon exploration were available for the construction of a detailed 3D geological model in our study area. The data are unevenly distributed, and only part of the study area was covered by the AEM survey. Cross-cutting tunnel valleys, erosional unconformities, delta units and a large glaciotectonic complex are among the geological features identified in the area. The geological complexity varies significantly across the model area. A broad geological overview and understanding of the area was obtained by joint cognitive interpretation of the geophysical and the geological data. To address the geological complexity and the very high level of detail gained from the AEM data, the model was constructed as a voxel model with lithofacies attributes supplemented by a number of bounding surfaces. In areas where the geology is not too detailed and complex, the model was constructed manually, whereas automated methods were used to populate voxels in areas with a high complexity. The automated methods comprised clay fraction modelling, which was used where AEM data are available, and stochastic modelling, which was used outside the area covered by AEM data. Our study shows that it is advantageous to combine several modelling methods in areas with varying geological complexity and data density. The choice of modelling methods should depend on the character and coverage of available data and on variations in geology throughout the model area. [ABSTRACT FROM AUTHOR]

In this paper we demonstrate how recent geomodelling techniques can be combined and used to build a 3D geological model on a real case study: the former coal mine of Merlebach (France), that is targeted to be exploited for low-temperature geothermal energy production. From geological maps, cross-sections, borehole and mine exploitation data, we build a 3D model in which are identified the rocks and infrastructures having significantly different permeabilities. First, a structural model of the main geological interfaces in our area of interest (2 horizons and 13 faults) is built with classical geomodelling techniques. Then, we propose to model by surfaces the 71 irregularly stacked, very close and very thin, sub-vertical coal beds. To ease their construction, we use an implicit method which represents 3D surfaces as isovalues of a scalar field defined in a 3D tetrahedral grid of the area. The corresponding triangulated surfaces are remeshed with a recently proposed method based on Voronoi diagrams so that the exploited parts of the coal beds, now filled by sand, can be computed. The 3D surface-based geological model, in which infrastructures can be inserted as piecewise lines, can be volumetrically meshed. It is available for download as supplemental material, as well as a volumetric grid. [ABSTRACT FROM AUTHOR]

Fault modeling is a very important step in making an accurate and reliable 3D geological model. Typical existing methods demand enough fault data to be able to construct complex fault models, however, it is well known that the available fault data are generally sparse and undersampled. In this paper, we propose a workflow of fault modeling, which can integrate multi-source data to construct fault models. For the faults that are not modeled with these data, especially small-scale or approximately parallel with the sections, we propose the fault deduction method to infer the hanging wall and footwall lines after displacement calculation. Moreover, using the fault cutting algorithm can supplement the available fault points on the location where faults cut each other. Increasing fault points in poor sample areas can not only efficiently construct fault models, but also reduce manual intervention. By using a fault-based interpolation and remeshing the horizons, an accurate 3D geological model can be constructed. The method can naturally simulate geological structures no matter whether the available geological data are sufficient or not. A concrete example of using the method in Tangshan, China, shows that the method can be applied to broad and complex geological areas. [ABSTRACT FROM AUTHOR]

Due to an expected increase in geological voxel model data-flow and user demands, the development of improved quality control for such models is crucial. This study explores the potential of a new type of quality control that improves the detection of errors by just using gaze behavior of 12 geological experts. Gaze is used as input for an attention model that results in ‘attended areas’ on sliced representations of part of a geological voxel model. We compared attended areas to errors as manually marked by the experts. We found a clear match between manually marked errors and attended areas as determined using gaze. We also found that a large proportion of this match is reached within a small amount of total viewing time. [ABSTRACT FROM AUTHOR]

Airborne magnetic data are commonly processed and interpreted by geologists to produce preliminary geological maps of unexplored areas. Machine learning can partly fulfill this task rapidly and objectively as convolutional neural networks applied to image segmentation showed promising results in comparable applications. As these algorithms require a large and high-quality dataset to be trained, we developed an innovative geostatistical data augmentation workflow that uses a 3D conceptual lithological and magnetic susceptibility model as input. The workflow uses soft-constrained Multi-Point Statistics to create equiprobable synthetic 3D geological models and Sequential Gaussian Simulation to populate these models with a geologically meaningful magnetic susceptibility spatial distribution. Then, geophysical forward modeling is computed to get the airborne magnetic responses of the synthetic models, which are associated with their counterpart synthetic geological maps. We applied this workflow on a 3D model of the Malartic Mine area to obtain a large synthetic airborne magnetic and counterpart geological map dataset. Then, a Gated Shape Convolutional Network is trained on this dataset to perform a preliminary geological mapping. A semi-supervised approach using clustering on the feature maps from the trained network is also implemented. Testing the trained network on synthetic data and remote areas of similar geological context shows that the methodology is suitable for producing preliminary geological maps using airborne magnetic data. Notably, the clustering module shows a precise and adaptive segmentation of the magnetic anomalies into pertinent preliminary geological maps. The quality of the results empirically validates our data augmentation method. Our method allows producing geological maps by training a convolutional neural network using an area where a detailed geological and petrophysical 3D model exists and then permits applying the pre-trained model in new areas of the same geological context, where only airborne magnetic data is available. • Multi-Point Statistics, Sequential Gaussian Simulation and Forward modeling can be used to perform data-augmentation using a 3D Magnetic Susceptibility and Geological Model as input. • Convolutional Neural Networks perform to generate preliminary geological mapping using only airborne magnetic data. • Apply clustering on the feature maps of the CNN provides pertinent preliminary geological mapping. • Gated-Convolutional Layers provide pertinent representations of an airborne magnetic data. [ABSTRACT FROM AUTHOR]

Multiple-point statistics (MPS) is a powerful method to generate realistic geological models. Given a training image as a prior model, the program iteratively reproduces spatial patterns in the simulation grid. However, running speed becomes a limitation to practical applications. MPS has to handle complicated and high-dimensional structures at the cost of simulation time. With the objective to accelerate geostatistical modeling with categorical variables, we propose a nearest neighbor simulation (NNSIM) method. Several k-nearest neighbor (kNN) classifiers are incorporated into MPS framework. First, we identify representative patterns with a prototype selection method. Different from existing MPS programs, our method selects training patterns according to their influences on simulation quality. A pattern subset of small size has a positive effect on searching time. Second, a teacher-student architecture is suggested to improve the pattern subset. In order to address missing data, our program augments the subset with key patterns during simulation. A cosine distance metric is applied to compare the original dataset and pattern subset. Third, our program organizes patterns with a ball tree. Pattern groups with low similarity are dynamically removed to fulfill fast search. We examine the proposed NNSIM by a benchmark channel simulation, a 2D flume model, and a 3D sandstone modeling. Many quantitative approaches are employed to evaluate geometrical and physical properties. The experimental results indicate that our NNSIM significantly improves the computational efficiency while exhibits comparable simulation quality to traditional MPS programs. • K nearest neighbor classifiers are incorporated into the multiple-point statistics. • The nearest neighbor simulation method significantly speeds up geological modeling. • A group of realistic 3D sandstone models are generated from a 2D slice. [ABSTRACT FROM AUTHOR]

Generative Adversarial Networks (GAN) have shown great potential in not only producing acceptable realizations of geologically complex models but also successfully reparametrizing them. Training GANs is quite challenging. One such challenge is mode collapse. When generating realizations of spatial property, mode collapse causes reduction in variability, compared to the input training dataset, and thus, the realizations become spatially biased at specific locations. To address this issue, we developed a new GAN architecture where a regularization term is introduced to maintain the variability and reduce mode collapse. This is achieved by using a probability map to evaluate variability and spatial bias of generated realizations and modifying the GAN loss function to minimize this bias. We applied the new architecture to a binary channelized permeability distribution and compared the results with those generated by Deep Convolutional GAN (DCGAN) and Wasserstein GAN with gradient penalty (WGAN-GP). Our results show that the proposed architecture significantly enhances variability and reduces the spatial bias induced by mode collapse, outperforming both DCGAN and WGAN-GP in the application of generating subsurface property distributions. • Maintain variability of geological realizations of Generative adversarial networks. • Address mode collapse and spatial bias in geological models. • Introducing probability maps into the architecture of GANs. • Comparison of variability in generated GAN results. [ABSTRACT FROM AUTHOR]

The support vector classifier (SVC) is one of the most powerful machine learning algorithms. This algorithm has been accepted as an effective method in three-dimensional geological modeling. Although the model selection has a great impact on the performance of SVC algorithm, most of mining studies have neglected it and used the grid search method. Therefore, in this study, a new approach is proposed for improving the selection of SVC models. This approach uses particle swarm optimization (PSO) to determine the important parameters of SCV such as penalty and kernel parameters. The proposed approach was applied in the modeling process of the Iju porphyry copper deposit to delineate alteration and mineralization zones. The optimal penalty and kernel parameters were found to be 27.2 and 2−4.75 for alteration zone, and 22.72 and 2−6.23 for mineralization zone, respectively. With 97.4% and 97.01% rates of accuracy for mineralization and alteration zones, the PSO results showed reasonable performance in classification. The proposed approach had better accuracy than grid search method. Therefore, because of its better performance, the geological models were developed using the PSO method to be used as a basis for future resource evaluation. • The particle swarm optimization (PSO) algorithm and grid search method were compared in parameter optimization. • Implicit geological modeling by a support vector classifier whose parameters were optimized using PSO. • Implicit geological model of alteration and mineralization domains are separately prepared. [ABSTRACT FROM AUTHOR]

Specifying point sources and sinks of water near boundaries is presented as a flexible approach to prescribe inflows and outflows along arbitrary directions in Cartesian grid lake models. Implementing the approach involves a straightforward modification of the governing equations, to include a first order source term in the continuity and momentum equations. The approach is implemented in a Cartesian grid model and applied to several test cases. First, the flow along a straight flat bottom channel with its axis forming different angles with the grid directions is simulated and the results are compared against well-known analytical solutions. Point-sources are then used to simulate unconfined inflows into a reservoir (a small river entering a reservoir in a jet-like manner), which occur at an angle with the grid directions. The model results are assessed in terms of a mixing ratio between lake and river water, evaluated at a cross section downstream of the inflow boundary. Those results are particularly sensitive to changes in the inflow angle. It is argued that differences in mixing rates near the inflow sections could affect the fate of river-borne substances in model simulations. [ABSTRACT FROM AUTHOR]