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The carbon capture and storage (CCS) process has become one of the main technologies used for mitigating greenhouse gas emissions. The success of CCS projects relies on accurate subsurface reservoir petrophysical characterization, enabling efficient storage and captured CO 2 containment. In digital rock physics, X-ray microtomography ( μ -CT) is applied to characterize reservoir rocks, allowing a more assertive analysis of physical properties such as porosity and permeability, enabling better simulations of porous media flow. Estimating petrophysical properties through numeric simulations usually requires high-resolution images, which are expensive and time-inefficient to obtain with μ -CT. To address this, we propose using two deep learning models: a super-resolution model to enhance the quality of low-resolution images and a surrogate model that acts as a substitute for numerical simulations to estimate the petrophysical property of interest. A correction process inspired by generative adversarial network (GAN) adversarial training is applied. In this approach, the super-resolution model acts as a generator, creating high-resolution images, and the surrogate network acts as a discriminator. By adjusting the generator, images that correct the errors in the surrogate’s estimations are produced. The proposed method was applied to the DeePore dataset. The results shows the proposed approach improved permeability estimation overall.

In addition to the ongoing development, pre-salt carbonate reservoir characterization remains a challenge, primarily due to inherent geological particularities. These challenges stimulate the use of well-established technologies, such as artificial intelligence algorithms, for image classification tasks. Therefore, this work intends to present an application of deep learning techniques to identify lithological patterns in Brazilian pre-salt carbonate rocks using microtomographic images. Four convolutional neural network models were proposed. The first model includes three convolutional layers, followed by a fully connected layer. This model is used as a base model for the following proposals. In the next two models, we replace the max pooling layer with a spatial pyramid pooling and a global average pooling layer. The last model uses a combination of spatial pyramid pooling followed by global average pooling in place of the final pooling layer. All models are compared using original images, when possible, as well as resized images. The dataset consists of 6,000 images from three different classes. The model performances were evaluated by each image individually, as well as by the most frequently predicted class for each sample. According to accuracy, Model 2 trained on resized images achieved the best results, reaching an average of 75.54% for the first evaluation approach and an average of 81.33% for the second. We developed a workflow to automate and accelerate the lithology classification of Brazilian pre-salt carbonate samples by categorizing microtomographic images using deep learning algorithms in a non-destructive way.

The recent surge in artificial intelligence (AI) advancements has been driven by the availability of open datasets for model development and evaluation. However, in the field of earth sciences, particularly in digital rock physics applications, open data remains scarce. To bridge this gap, we introduce a dataset comprising 16 rock samples from the Brazilian pre-salt region, available in both low resolution (48 μ m - 64 μ m) and high resolution (6 μ m - 8 μ m). The dataset also includes their respective segmented images into pore and matrix. Furthermore, porosity and permeability values obtained from laboratory measurements are provided for all samples. This dataset serves as a valuable resource for developing and benchmarking AI-based superresolution/segmentation models. Additionally, it can be utilized to develop models for predicting porosity and permeability directly from μ -CT images.

Innovative solutions have been designed to meet the global demand for energy and environmental sustainability, such as enhanced hydrocarbon recovery and geo-sequestration of CO 2 . These processes involve the movement of immiscible fluids through permeable rocks, which is affected by the interfacial properties of rocks at the pore scale. Overcoming major challenges in these processes relies on a deeper understanding about the fundamental factors that control the rock wettability. In particular, the efficiency of oil recovery strategies depends largely on the 3D wetting pattern of reservoir rocks, which is in turn affected by the adsorption and deposition of ‘contaminant’ molecules on the pores’ surface. Here, we combined high-resolution neutron tomography (NT) and synchrotron X-ray tomography (XRT) to probe the previously unobserved 3D distribution of molecular and mineralogical heterogeneity of oil reservoir rocks at the pore scale. Retrieving the distribution of neutron attenuation coefficients by Monte Carlo simulations, 3D molecular chemical mappings with micrometer dimensions could be provided. This approach allows us to identify co-localization of mineral phases with chemically distinct hydrogen-containing molecules, providing a solid foundation for the understanding of the interfacial phenomena involved in multiphase fluid flow in permeable media.

Young–Laplace equation-based algorithms for simulating capillary-driven flow have been used in porous media research for more than two decades. However, the lack of a uniform mathematical description hinders a wider application of these algorithms, as well as impede their comparison. After conducting a detailed review of the most important publications in the area, we propose a unified algorithm. This resulting framework is capable of handling four distinct physical situations: drainage and imbibition with either compressible or incompressible displacement fluid. Additionally, there is no restriction regarding the geometry or the initial fluid distribution used. The proposed algorithm can simulate variable or mixed wettability, even for imbibition, which has not already been described in the literature. Finally, at the end of the manuscript, we provide an efficient open-source C +  + code for the proposed unified algorithm and also many examples of its use.

In fluid mechanics, multicomponent fluid systems are generally treated either as homogeneous solutions or as completely immiscible parts of a multiphasic system. In immiscible systems, the main task in numerical simulations is to find the location of the interface evolving over time, driven by normal and tangential surface forces. The lattice-Boltzmann method (LBM), on the other hand, is based on a mesoscopic description of the multicomponent fluid systems, and appears to be a promising framework that can lead to realistic predictions of segregation in non-ideal mixtures of partially miscible fluids. In fact, the driving forces in segregation are of a molecular nature: there is competition between the intermolecular forces and the random thermal motion of the molecules. Since these microscopic mechanisms are not accessible from a macroscopic standpoint, the LBM can provide a bridge linking the microscopic and macroscopic domains. To this end, the first purpose of this article is to present the kinetic equations in their continuum forms for the description of the mixing and segregation processes in mixtures. This paper is limited to isothermal segregation; non-isothermal segregation was discussed by Philippi et al. (Phil. Trans. R. Soc., vol. 369, 2011, pp. 2292–2300). Discretization of the kinetic equations leads to evolution equations, written in LBM variables, directly amenable for numerical simulations. Here the dynamics of the kinetic model equations is demonstrated with numerical simulations of a spinodal decomposition problem with dissolution. Finally, some simplified versions of the kinetic equations suitable for immiscible flows are discussed.

The thermodynamic consistency of kinetic models for non-ideal mixtures in non-isothermal conditions is investigated. A kinetic model is proposed that is suitable for deriving high-order lattice Boltzmann equations by an appropriate discretization of the velocity space, satisfying the Galilean invariance condition and free of spurious terms in the first moment equations.

In this work we present a method to classify a set of rock textures based on a Spectral Analysis and the extraction of the texture Features of the resulted images. Up to 520 features were tested using 4 different filters and all 31 different combinations were verified. The classification process relies on a Naive Bayes classifier. We performed two kinds of optimizations: statistical optimization with covariance-based Principal Component Analysis (PCA) and a genetic optimization, for 10,000 randomly defined samples, achieving a final maximum classification success of 91% against the original 70% success ratio (without any optimization nor filters used). After the optimization 9 types of features emerged as most relevant.

In addition to the ongoing development, pre-salt carbonate reservoir characterization remains a challenge, primarily due to inherent geological particularities. These challenges stimulate the use of well-established technologies, such as artificial intelligence algorithms, for image classification tasks. Therefore, this work intends to present an application of deep learning techniques to identify patterns in Brazilian pre-salt carbonate rock microtomographic images, thus making possible lithological classification. Four convolutional neural network models were proposed. The first model includes three convolutional layers followed by fully connected layers and is used as a base model for the following proposals. In the next two models, we replace the max pooling layer with a spatial pyramid pooling and a global average pooling layer. The last model uses a combination of spatial pyramid pooling followed by global average pooling in place of the last pooling layer. All models are compared using original images, when possible, as well as resized images. The dataset consists of 6,000 images from three different classes. The model performances were evaluated by each image individually, as well as by the most frequently predicted class for each sample. According to accuracy, Model 2 trained on resized images achieved the best results, reaching an average of 75.54% for the first evaluation approach and an average of 81.33% for the second. We developed a workflow to automate and accelerate the lithology classification of Brazilian pre-salt carbonate samples by categorizing microtomographic images using deep learning algorithms in a non-destructive way.

The particles model, the collision model, the polynomial development used for the equilibrium distribution, the time discretization and the velocity discretization are factors that let the lattice Boltzmann framework (LBM) far away from its conceptual support: the continuous Boltzmann equation (BE). Most collision models are based on the BGK, single parameter, relaxation-term leading to constant Prandtl numbers. The polynomial expansion used for the equilibrium distribution introduces an upper-bound in the local macroscopic speed. Most widely used time discretization procedures give an explicit numerical scheme with second-order time step errors. In thermal problems, quadrature did not succeed in giving discrete velocity sets able to generate multi-speed regular lattices. All these problems, greatly, difficult the numerical simulation of LBM based algorithms. In present work, the systematic derivation of lattice-Boltzmann models from the continuous Boltzmann equation is discussed. The collision term in the linearized Boltzmann equation is modeled by expanding the distribution function in Hermite tensors. Thermohydrodynamic macroscopic equations are correctly retrieved with a second-order model. Velocity discretization is the most critical step in establishing regular-lattices framework. In the quadrature process, it is shown that the integrating variable has an important role in defining the equilibrium distribution and the lattice-Boltzmann model, leading, alternatively, to temperature dependent velocities (TDV) and to temperature dependent weights (TDW) lattice-Boltzmann models.