Rock Images Research Articles

A Computer Aided System for Reservoir Rock Classification

This study is mainly aimed to know the reservoir rocks among core specimens that are obtained during the stage of drilling of exploration oil and gas wells by the processing of the image digitally using a Computer-Aided System. This computerized method has been conducted using two programming languages; Python and Matlab. The Python program is used to segment rock images and classify the extracted features, whereas the MATLAB language has been used to extract intensity features from segmented images. The database used in this project has been collected from two sources, one of them from the Sarier formation (the biggest Libyan oil field) while the second source was from the internet. The images used in this study are sedimentary rocks images, which include sandstone, limestone, and dolomite. As a result, this study has reached that the system, which was used here is capable of classifying rocks and can be used to classify oil rocks or any other type of rocks. Also, it can be used near the well due to certain conditions such as the absence of experts due to the lack of enough database, the study has only introduced the preliminary results as proof of the concept.

Similarity ratings for basic-level categories from the Nosofsky et al. (2018) database of rock images

Similarity ratings for basic-level categories from the Nosofsky et al. (2018) database of rock images

A spatio-temporal forecasting method of fracture distribution using dynamically exposed rock images in tunnel: Methodology and application

Forecasting the geometric characteristics of rock fractures in unexcavated tunnel sections is crucial for construction arrangements. This paper presents a method for predicting fracture distribution near the tunnel face from a spatiotemporal perspective. We Innovatively analogize the tunnel excavation mileage as the time series data. Using fractal geometry and geostatistics, we extract geometric features of peripheral rock fractures and establish a spatiotemporal dataset for dynamic prediction. The SCINet model, a spatial-aware recursive neural network, is employed for deterministic-stochastic dynamic prediction. Numerical simulations are conducted to validate the predictive accuracy of the model for various distributions, including trace length, dip angle, and density. Notably, the Mean Absolute Percentage Error (MAPE) for trace length prediction is remarkably low at 5.49 %, and the Kullback-Leibler (KL) divergence is as low as 4.81 %. The method is further applied to an underground oil storage cavern, China, revealing structural surface information. By incorporating continuously exposed fracture data, prediction accuracy is progressively improved, demonstrating the method's potential as a predictive tool for enhancing tunnel construction safety and efficiency.

Rock art in the stone age of Gobustan: an archaeologist’s view

Gobustan is home to a large group of archaeological sites and many thousands of rock images dating from the Upper Palaeolithic to the Middle Ages. Due to its extraordinary scientific and artistic significance, the Gobustan complex is included in the UNESCO World Cultural Heritage List. This paper focuses on rock images dating back to the Stone Age. An attempt is made to systematize petroglyphs, taking into account their types, artistic styles, techniques of their creation, and themes of the images. The author considers relative dating and correlation of the distinguished styles with Gobustan sites of various stages of the Stone Age. Based on the analysis, style 1 of the region’s petroglyphs is dated from the first half of the Upper Palaeolithic (no later than 25–24 kya). Style 2 is synchronized with Mesolithic sites, and styles 3a and 3b are dated, with a certain degree of conditionality, from the Neolithic and Chalcolithic.

Digital rock modeling of deformed multi-scale media in deep hydrocarbon reservoirs based on in-situ stress-loading CT imaging and U-Net deep learning

Deep and ultra-deep hydrocarbon reservoirs are regarded as an attractive target for onshore oil and gas exploration and development in China. The multi-scale fractures are widely distributed in this type of reservoir. The fluid-solid coupling effect is strong, which can greatly affect the fluid flow, resulting in a low hydrocarbon recovery with the average value less than 15%. To explore the underlying flow dynamics during efficient development of deep and ultra-deep hydrocarbon reservoirs, it is of great importance to characterize the multi-scale fracture-pore structure evolution of rock affected by strong geo-stress field variation. To tackle the above issues, an actual rock drilled from a typical ultra-deep reservoir in Tarim Basin are selected to conduct in-situ stress-loading computed tomography (CT) scanning experiments, and the CT gray images of rock microstructure under different dynamic loading conditions are obtained. A fully convolutional neural network (U-Net) deep learning segmentation algorithm is introduced to accurately distinguish the rock skeleton, pore space and fractures in deep rock. The three-dimensional (3-D) digital rock model of deformed multi-scale fracture-porous media under different dynamic loading conditions is ultimately established to investigate the evolution of fracture morphology and deep rock microstructure as the in-situ stress is gradually loaded. It indicates that, the U-Net deep learning semantic segmentation algorithm can accurately segment CT images of deep rocks, and the established 3D digital rock model of fractured porous media can accurately represent the pore-throat distribution, fracture morphology, and pore-scale topological connectivity. At the initial stage of stress loading, there are few micro-fractures inside the rock, and the fracture connectivity is poor. Due to effect of rock compaction, the average pore throat radius increases while pore throat ratio, coordination number and tortuosity greatly decrease. As the effective stress is gradually loaded, the micro-fractures begin to propagate, leading to stronger heterogeneity of micro-fractures’ distribution and better topological connectivity, and both the coordination number and tortuosity gradually increase. After the deep rock is fractured, micro-fractures run through a fracture network and all the above topological parameters increase greatly.

Reconstruction of reservoir rock using attention-based convolutional recurrent neural network

The digital reconstruction of reservoir rock or porous media is important as it enables us to visualize and explore their real internal structures. The reservoir rocks (such as sandstone and carbonate) contain both spatial and temporal characteristics, which pose a big challenge in their characterization through routine core analysis or x-ray microcomputer tomography. While x-ray micro-computed tomography gives us three-dimensional images of the porous media, it is often impossible to quantify the variability of the pore, grains, structure, and orientation experimentally. Recently, machine learning has successfully demonstrated the reconstruction ability of reservoir rock images or any porous media. These reservoir rock images are crucial for the digital characterization of the reservoir. We propose a novel algorithm consisting of the convolutional neural network, an attention mechanism, and a recurrent neural network for the reconstruction of reservoir rock or porous media images. The attention-based convolutional recurrent neural network (ACRNN) can reconstruct a representative sample of reservoir rocks. The reconstructed image quality was checked by comparing them with the original Parker sandstone, Leopard sandstone, carbonate shale, Berea sandstone, and sandy medium images. We evaluated the reconstruction by measuring pore and throat properties, two-point probability function, and structural similarity index. Results show that ACRNN can reconstruct reservoir rock or porous media of different scales with approximately the same geometrical, statistical, and topological parameters of the reservoir rock images. This deep learning method is computationally efficient, fast, and reliable for synthetic image realizations. The model was trained and validated on real images, and the reconstructed images showed excellent concordance with the real images having almost the same pore and grain structures. The deep learning-based digital rock reconstruction of reservoir rock or porous media images can aid in rapid image generation to better understand reservoir rock or subsurface formation.

Research on coal-rock boundary identification based on the morphological sobel algorithm

Due to the harsh underground environment during coal mining, the quality of images collected by cameras is not sufficient, and the acquired images are greatly affected by noise, affecting visual observation; to a certain extent, subsequent intelligent mining is limited. A morphological Sobel coal-rock boundary recognition algorithm is proposed according to the different gray levels of coal-rock images to solve the problem of coal image quality. First, the details of the coal and rock images are smoothly preprocessed to improve the contrast between the feature boundaries and surrounding pixels, and the gray-level adaptive threshold is applied after processing. Morphological corrosion theory is used to process the morphological structure in an image, and the corresponding boundary in the image is extracted for recognition. Compared with the boundary points identified by each algorithm, the area error of coal and rock identification is calculated by using the boundary point fitting curve. The morphological Sobel algorithm is used to calculate the identification area error of coal and rock at different angles according to the camera range. The experimental results show that the boundaries identified by the morphological Sobel algorithm have the best degree of overlap with the boundaries of the original image. The identification error area is only about 10% of the Sobel operator and Canny operator algorithm. Monitoring coal and rock specimens can enable the effective identification of coal and rock boundaries from various angles.

Deep learning for pore-scale two-phase flow: Modelling drainage in realistic porous media

In order to predict phase distributions within complex pore structures during two-phase capillary-dominated drainage, we select subsamples from computerized tomography (CT) images of rocks and simulated porous media, and develop a pore morphology-based simulator (PMS) to create a diverse dataset of phase distributions. With pixel size, interfacial tension, contact angle, and pressure as input parameters, convolutional neural network (CNN), recurrent neural network (RNN) and vision transformer (ViT) are transformed, trained and evaluated to select the optimal model for predicting phase distribution. It is found that commonly used CNN and RNN have deficiencies in capturing phase connectivity. Subsequently, we develop a higher-dimensional vision transformer (HD-ViT) that drains pores solely based on their size, regardless of their spatial location, with phase connectivity enforced as a post-processing step. This approach enables inference for images of varying sizes and resolutions with inlet-outlet setup at any coordinate directions. We demonstrate that HD-ViT maintains its effectiveness, accuracy and speed advantage on larger sandstone and carbonate images, compared with the microfluidic-based displacement experiment. In the end, we train and validate a 3D version of the model.

YOLOv8-Coal: a coal-rock image recognition method based on improved YOLOv8.

To address issues such as misdetection and omission due to low light, image defocus, and worker occlusion in coal-rock image recognition, a new method called YOLOv8-Coal, based on YOLOv8, is introduced to enhance recognition accuracy and processing speed. The Deformable Convolution Network version 3 enhances object feature extraction by adjusting sampling positions with offsets and aligning them closely with the object's shape. The Polarized Self-Attention module in the feature fusion network emphasizes crucial features and suppresses unnecessary information to minimize irrelevant factors. Additionally, the lightweight C2fGhost module combines the strengths of GhostNet and the C2f module, further decreasing model parameters and computational load. The empirical findings indicate that YOLOv8-Coal has achieved substantial enhancements in all metrics on the coal rock image dataset. More precisely, the values for AP50, AP50:95, and AR50:95 were improved to 77.7%, 62.8%, and 75.0% respectively. In addition, optimal localization recall precision (oLRP) were decreased to 45.6%. In addition, the model parameters were decreased to 2.59M and the FLOPs were reduced to 6.9G. Finally, the size of the model weight file is a mere 5.2 MB. The enhanced algorithm's advantage is further demonstrated when compared to other commonly used algorithms.

Hyperspectral Rock Classification Method Based on Spatial-Spectral Multidimensional Feature Fusion

The issues of the same material with different spectra and the same spectra for different materials pose challenges in hyperspectral rock classification. This paper proposes a multidimensional feature network based on 2-D convolutional neural networks (2-D CNNs) and recurrent neural networks (RNNs) for achieving deep combined extraction and fusion of spatial information, such as the rock shape and texture, with spectral information. Experiments are conducted on a hyperspectral rock image dataset obtained by scanning 81 common igneous and metamorphic rock samples using the HySpex hyperspectral sensor imaging system to validate the effectiveness of the proposed network model. The results show that the model achieved an overall classification accuracy of 97.925% and an average classification accuracy of 97.956% on this dataset, surpassing the performances of existing models in the field of rock classification.

IMAGE-BASED YOLOV4 ARCHITECTURE FOR DETECTING MINERAL IN SEDIMENTARY ROCKS THIN SECTION

Thin section analysis of sedimentary rocks is the basis for identifying minerals and textures. In general, quantitative analysis of thin sections of rock often requires many hours of work when done manually. In today's era, mineralogical interpretation and percentage calculations must be carried out automatically using more practical applications. The research method begins with the identification of 44 thin section samples in parallel plane polarized (PPL) and crossed polarized (XPL) conditions with thin section analysis then mineralogy detection is carried out using a computational approach, namely the use of image-based Deep Learning YoloV4 architecture with 2D RGB image objects from the thin section of sedimentary rock. The results of this study show the best values of Average Precision in Quartz, Feldspar, and lithic are 39.21% in the XPL model, 26.53% in the XPL model, and 15.75% in combined mode, according to the training and testing of YoloV4 Models for the identification of rock minerals in thin sections. Based on the complexity of the mineral types, the granularity of the detection, and the specific geological objectives, establishing a meaningful benchmark or baseline for comparison is always challenging. Additionally, consider discussing the trade-offs between precision and recall, as a higher precision may be more critical in some geological applications. It is expected that the application of this research can produce practical, fast and accurate interpretation of the determination of minerals in sedimentary rocks from all thin-section images of rocks and thus provide a complete understanding of geological views automatically.

Reconstruction of large-scale anisotropic 3D digital rocks from 2D shale images using generative adversarial network

Digital rock technology has played a significant role in unconventional oil and gas exploration and development. Current techniques face challenges such as high cost or low efficiency when reconstructing 3D pore structures at the nanoscale, which limit the study of rock physical properties in reservoirs. To address these issues, this work proposes an efficient generative adversarial network workflow for generating large-scale anisotropic 3D digital rocks from 2D images. Instead of relying on 3D data, the model is trained using only 2D images. During training, rock images taken from various angles can be utilized to construct anisotropic 3D digital rocks. Moreover, a novel method for generating large-scale 3D data is devised, where the input random noise is divided with overlap to generate small-scale digital rocks and concatenated into a larger-scale 3D digital rock. The similarity between the 3D reconstructed data and the training images was assessed separately, as well as the pore structure of the 3D reconstructed data, and the feasibility of the proposed method was verified using the simulation of absolute permeability. The digital rock modeling process presented in this study enables rapid and accurate 2D-to-3D large-scale digital rock modeling, providing an effective representation of the anisotropy present in shale oil reservoirs.

Two-phase flow simulation algorithm for numerical estimation of relative phase permeability curves of porous materials

Abstract The paper presents an algorithm for three-dimensional modelling of two-phase flows on the scale of pore size order for numerical evaluation of relative phase permeability curves of porous materials. Such an evaluation is performed based on the results of numerical simulation of primary drainage with subsequent waterflooding. In this case, models of porous materials based on three-dimensional tomographic images of rocks are used. The simulation of the flow considers the Stokes equation and the Cahn–Hilliard equation for modelling phase transfer, which allows us to determine phases using the concentration function. The combination of the phase field method and finite difference method makes it possible to correctly take into account the contact angle and stably calculate surface tension forces in domains with complex topology.

Absolute permeability estimation from microtomography rock images through deep learning super-resolution and adversarial fine tuning

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 CO2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extrm{CO}_2$$\\end{document} containment. In digital rock physics, X-ray microtomography (μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu $$\\end{document}-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\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu $$\\end{document}-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.

Super-resolution of digital rock images with hybrid attention multi-branch neural network

High-resolution digital rock images are vital in studying microstructure and fluid flow characterization of oil and gas reservoirs. Computed tomography (CT) is currently the primary tool to acquire digital rock images because of its non-destructiveness and three-dimensional imaging capability. However, the restriction of imaging capability of CT sets a compromise between image resolution and field of view (FOV). The Hybrid Attention Multi-branch Super-Resolution Network (HAMSR) proposed in this study aims to remedy this deficiency. HAMSR employs a limited discrete distribution to capture all possible high-order information and generate reliable high-resolution images. The network is built to prioritize the recovery of pores, cracks, and textures in digital rock images by introducing channel and spatial attention mechanisms. The structure together with modules of HAMSR have been designed and optimized to yield superior performance with fewer parameters and less training time. HAMSR and several contemporary advanced models are trained on the DeepRock-SR 2D dataset which contains lots of sandstone and carbonate images. It is evident that HAMSR-generated images have minimal noise, clearer edges, and more micropores. Quantitative calculations show that compared to traditional bicubic interpolation, HAMSR acquires a 2.669 dB and 1.376 dB increase in peak signal-to-noise ratio (PSNR) respectively on the test sets (27%–38% reduction in relative error). Furthermore, the high-resolution (HR) images reconstructed by HAMSR are generally consistent with real HR images concerning statistical characteristics such as porosity, pore size distribution, shape factor distribution, and two-point correlation function. This work provides a new super-resolution model to generate high-quality digital rock images for further analysis.

An Optimization Method for Lightweight Rock Classification Models: Transferred Rich Fine-Grained Knowledge.

Rock image classification represents a challenging fine-grained image classification task characterized by subtle differences among closely related rock categories. Current contrastive learning methods prevalently utilized in fine-grained image classification restrict the model's capacity to discern critical features contrastively from image pairs, and are typically too large for deployment on mobile devices used for in situ rock identification. In this work, we introduce an innovative and compact model generation framework anchored by the design of a Feature Positioning Comparison Network (FPCN). The FPCN facilitates interaction between feature vectors from localized regions within image pairs, capturing both shared and distinctive features. Further, it accommodates the variable scales of objects depicted in images, which correspond to differing quantities of inherent object information, directing the network's attention to additional contextual details based on object size variability. Leveraging knowledge distillation, the architecture is streamlined, with a focus on nuanced information at activation boundaries to master the precise fine-grained decision boundaries, thereby enhancing the small model's accuracy. Empirical evidence demonstrates that our proposed method based on FPCN improves the classification accuracy mobile lightweight models by nearly 2% while maintaining the same time and space consumption.

RockDNet: Deep Learning Approach for Lithology Classification

Analyzing rock and underground layers is known as drill core lithology. The extracted core sample helps not only in exploring the core properties but also reveals the lithology of the entire surrounding area. Automating rock identification from drill cuttings is a key element for efficient reservoir characterization, replacing the current subjective and time-consuming manual process. The recent advancements in computer hardware and deep learning technology have enabled the automatic classification of various applications, and lithology is not an exception. This work aims to design an automated method for rock image classification using deep learning technologies. A novel CNN (Convolution Neural Network) is proposed for lithology classification in addition to thorough comparison with benchmark CNN models. The proposed CNN model has the advantageous of having very low complexity while maintaining high accuracy. Experimental results on rock mages taken from the “digitalrocksportal” database demonstrate the ability of the proposed method to classify three classes, carbonate, sandstone and shale rocks, with high accuracy, and comparisons with related work demonstrated the efficiency of the proposed model, with more than 98% saving in parameters.

Machine Learning Assisted Prediction of Porosity and Related Properties Using Digital Rock Images.

Accurately estimating reservoir rock properties is paramount for modeling the storage and flow of fluids (hydrocarbon, carbon dioxide, and groundwater) in porous media. However, existing laboratory techniques to measure rock properties are usually time-consuming, expensive, and computationally intensive. This work proposes an efficient workflow that uses the machine learning algorithm, based on the convolutional neural network (CNN) framework, to predict rock properties from microcomputed tomography (micro-CT) X-ray images. The workflow involves data preprocessing, label extraction, training, and prediction using the segmented images of the rock to predict porosity, throat area, and pore surface area, which are essential for pore-scale modeling. The model was trained and validated on the Bentheimer sandstone, which was then used to predict properties of other sandstones (Castlegate and Leopard) with different pore structures and flow properties. The model yielded a good prediction for the throat and pore surface area but a significant error for porosity. Subsequently, a new complex model was trained and validated using diverse images from Bentheimer and an additional rock Castlegate, which was then used to predict the properties of Leopard sandstone. The new model improved the prediction of each property, resulting in mean absolute percentage error (MAPE) values of 2.19%, 3.04%, and 6.08% for porosity, pore surface area, and throat area with the binary images, respectively. In addition, we present a novel data-driven method using a simple regression model to predict the absolute permeability of a digital rock sample using the pore network parameters as predictors. The extreme gradient boost (XGBoost), which performed the best among several machine algorithms, was trained and validated using digital rock images from Bentheimer and Castlegate sandstone. The generated model was then used to predict the absolute permeability of the Leopard sandstone with an R 2 of 0.813, which was a significant improvement over the model generated solely by using either the Bentheimer or the Castlegate sandstone images. Furthermore, our analysis showed that the tortuosity had the most significant effect on the absolute permeability prediction of the rock sample. This study showed that we can reliably predict the morphological properties of porous media using computationally efficient models generated from digital rock images, which can be used to build a regression model to predict the crucial petrophysical properties needed to model the flow of fluids in porous media.

A Pore-Scale Phase Field Model for CO2-Fluid-Basalt Interactions.

Basalt is a suitable target area for CO2 storage. Clarifying the evolution of the pore structure during CO2-fluid-basalt interactions is a crucial element in the preparation of a CO2 geologic storage operation. In this study, a pore-scale phase field model is proposed to simulate the CO2-fluid-basalt interaction process of Snake River Plain basalt. Our model is suitable for complex multimineral natural rocks, and the initial mineral distribution before the reaction can be completely based on a real 3D rock image without the need to simplify the mineral geometry. The simulation results demonstrate that after 120 days of dissolution-precipitation reaction, the volume of secondary minerals (3.75%) exceeds that of dissolved minerals (2.01%), leading to a reduction in porosity by 1.74%. The pore structure of the basalt changes significantly, and the connectivity is obviously reduced. The effects of temperature and pressure on the reaction rate were examined to guide site selection for CO2 sequestration. The results show that increasing temperature and pressure can accelerate the reaction rate, but the impact of pressure on the reaction rate is negligible compared to the significant influence of temperature. Therefore, an area with a high geothermal gradient is conducive to geological sequestration.

Generation of pore-space images using improved pyramid Wasserstein generative adversarial networks

High-resolution three-dimensional X-ray microscopy can be used to image the pore space of materials. Machine learning algorithms can generate a statistical ensemble of representative images of arbitrary sizes for rock characterization, modeling, and analysis. However, current methods struggle to capture features at different spatial scales observed in many complex rocks which have a wide range of pore size. We use the Improved Pyramid Wasserstein Generative Adversarial Network (IPWGAN) to automatically reproduce multi-scale features in segmented three-dimensional images of porous materials, enabling the reliable generation of large-scale representations of complex porous media. A Laplacian pyramid generator is introduced, which creates pore-space features across a hierarchy of spatial scales. Feature statistics mixing regularization enhances the discriminator’s ability to distinguish between real and generated images by mixing their feature statistics, thereby indirectly enhancing the generator’s ability to capture and reproduce multi-scale pore-space features, leading to increased diversity and realism in the generated images. The method has been tested on five sandstone and carbonate samples. The generated images, which can be of any size – including cm-scale ten-billion-cell images – demonstrate the power of the approach. These images have two-point correlation functions, porosity, permeability, Euler characteristic, curvature, and specific surface area closer to those of the training datasets than existing machine learning techniques. The generated images accurately capture geometric and flow properties, demonstrating a considerable improvement over previously published studies using generative adversarial networks. For instance, the mean relative error in the calculated absolute permeability between the Berea sandstone images generated by IPWGAN and the corresponding real rock images can be reduced by 79%. The work allows representative models of a wide range of porous media to be generated, offering potential benefits in carbon dioxide sequestration, underground hydrogen storage, and enhanced oil recovery.