Classification Of Rocks Research Articles

RDNN for Classification and Prediction of Rock or Mine in Underwater Acoustics

Mines in the waters are just explosives that detonate upon contact with an object. The underwater submarine must foresee if it will encounter a mine or a rock. Lacking the development of the Ranging Sound Navigation approach, which utilizes particular variables to identify whether a surface or a barrier is made of a mine or rock, finding mines or rocks would have been extremely difficult. In our study, we demonstrate a technique for predicting underwater rocks and mines using SONAR waves. At 60 different angles, SONAR pings are employed to record the various frequencies of submerged objects. To identify whether the object in the ocean is a mine or just a rock, the submarine uses SONAR signals, which transmit sound and receive switchbacks. The mine and rock categories are predicted using the prediction models. To create these prediction models, Supervised Machine Learning Classification methods were employed.

Data-Driven Algorithms for Image-Based Rock Classification and Formation Evaluation in Formations With Rapid Spatial Variation in Rock Fabric

Supervised learning algorithms can be employed for the automation of time-intensive tasks, such as image-based rock classification. However, labeled data are not always available. Alternatively, unsupervised learning algorithms, which do not require labeled data, can be employed. Using either of these methods depends on the evaluated formations and the available training/input data sets. Therefore, further investigation is needed to compare the performance of both approaches. The objectives of this paper are (a) to train two supervised learning models for image-based rock classification employing image-based features from computerized tomography (CT) scan images and core photos, (b) to conduct image-based rock classification using the trained model, (c) to compare the results obtained using supervised learning models against an unsupervised learning-based workflow for rock classification, and (d) to derive class-based petrophysical models for improved estimation of petrophysical properties First, we removed non-formation visual elements from the core image data, such as induced fractures, the core barrel, and the seal peel tag on core photos. Then, we computed image-based features such as grayscale, color, and textural features from core image data and conducted feature selection. Then, we employed the extracted features for model training. Finally, we used the trained model to conduct rock classification and compared the obtained rock classes against the results obtained from an unsupervised image-based rock classification workflow. This workflow uses image-based rock fabric features coupled with a physics-based cost function for the optimization of rock classes. We applied the workflow to one well intersecting three formations with rapid spatial variation in rock fabric. We used 60% of the data to train a random forest and a support vector machines classifier using a 5-fold cross-validation approach. The remaining 40% of the data was used to test the accuracy of the supervised models. We established a base case of unsupervised learning rock classification and four different cases of supervised learning rock classification. The highest accuracy obtained for supervised rock classification was 97.4%. The accuracy obtained in the unsupervised learning rock classification approach was 82.7% when compared against expert-derived lithofacies. Class-based permeability estimates decreased the mean relative error by 34% and 35% when compared with formation-based permeability estimates, for the supervised and unsupervised approaches, respectively. The highest accuracies for the supervised and unsupervised models were obtained when integrating features from CT-scan images and core photos, highlighting the importance of feature selection for machine-learning workflows. A comparison of the two approaches for rock classification showed higher accuracy obtained from the supervised learning approach. However, the unsupervised method provided reasonable accuracy as well as a more general and faster approach for rock classification and enhanced formation evaluation.

„ხობი 2 ჰესის“ ჰიდროტექნიკური კომპლექსის სათავე ნაგებობის და ჰესის შენობის სამშენებლო მოედნის ამგები გრუნტების ფიზიკურ-მექანიკური თვისებები

Modern methods of rock evaluation, such as Rock Quality Index (RQD), Rock Mass Rating (RMR) and Rock Mass Classification System (Q), are used within the field of geotechnical surveys. RQD was determined by D.U. Deere in 1963, as a simple classification system of rock mass stability. While using RQD index five classes of rocks (A-E) are determined. Q value can be determined in different ways: during mapping in underground excavations, on the surface or alternatively – on basis of core description. The most accurate values are obtained during underground geological mapping. Dividing the underground excavations into several parts might be required during mapping, so that variation of Q value is moderate in each section, meaning that such variation should not exceed the rock class variation index according to the reinforcement scheme. During excavation works single blasting often represents natural section for individual mapping. A variation may take place in sections of several meters, but for showing this variation histograms can be used during mapping.

Electrical rock typing using Gaussian mixture model to determine cementation factor

Many studies have worked on the estimation of fluid saturation as an important petrophysical property in hydrocarbon reservoirs. Based on Archie's law, proper determination of cementation factor (m) can lead to accurate values of water saturation. Given that the m is mainly affected by electrical properties of rock, electrical quality index (EQI) can be used to estimate m through a novel rock typing technique. Despite the efficient applicability of EQI for the classification of rocks, with similar electrical behaviors, into distinct electrical rock types (ERTs), manual implementation of this method is time-consuming and gets excessively more difficult for larger datasets. In this work, a fast automated version of EQI methodology was presented. As a fuzzy clustering algorithm, Gaussian mixture model (GMM) was implemented on a large quantity of carbonate and sandstone samples to cluster them into distinct ERTs based on EQI values. To this end, 100 data points were randomly selected for testing purposes, and the remaining data points were used as training subsets for carbonate and sandstone samples. An innovative hybrid EQI-GMM approach was developed to determine the optimum number of clusters. Furthermore, results of two commonly-used criteria, namely Schwarz's Bayesian Criterion (BIC) and Akaike Information Criterion (AIC), showed that they fail to specify ERTs properly. The predicted values for m by the hybrid EQI-GMM approach were more accurate (RMSE is 0.0167 and 0.0056 for carbonate and sandstone samples, respectively) than outputs of the traditional Archie’s law (RMSE is 1.6697 and 0.1850 for carbonate and sandstone samples, respectively).

변성암 분류 과정에서 정보 제공 정도에 따른 지구과학 교사들의 시선 특성과 암석 판별의 정확성 분석

Objectives The purposes of this study is to find out whether there is a difference in earth science teachers’ gaze characteristics and accuracy of rock identification according to the level of information provided in the metamorphic rock classification process.
 Methods To this end, 6 samples of metamorphic rocks (phyllite, slate, gneiss, hornfels, marble, and schist) were selected, and by using them, tasks related to metamorphic rock classification with different levels of information were developed. Afterwards, the developed task was applied to 21 earth science teachers, and quantitative data on gaze movement in the process of classifying metamorphic rock was collected and analyzed using a eye tracker (Tobii Pro Glasses 2 model).
 Results In the process of classifying metamorphic rocks, earth science teachers tended to focus too much on specific areas or unique features of the rock surface rather than the overall characteristics of the rock, and tended to classify rocks based on the limited information collected in this way. Second, although there are some differences depending on the type of rock, the more specific the information provided, the more the teachers’ total duration of fixation and number of fixation tended to decrease. Third, as the information provided was more specific, the accuracy of rock identification increased, and the types of rocks mentioned tended to decrease. Fourth, according to the level of information provided by earth science teachers, the most accurate pattern for classifying rocks was ‘wrong answer-wrong answer-correct answer’.
 Conclusions Through the result of this study, it was confirmed that it is necessary to provide a systematic manual on matters to be paid attention to when observing the characteristics of metamorphic rocks, and to reduce the cognitive loads of learners through effective rock arrangement enhance understanding of metamorphic rocks classification.

Automated real-time formation evaluation from cuttings and drilling data analysis: State of the art

Traditional formation evaluation via laboratory testing and wireline logging of horizontal wells and deep formations face challenges due to several reasons and lead to uncertain results. Real-time cuttings and drilling data analysis of horizontal wells is an actively developing alternative approach to formation evaluation that can overcome several challenges faced by laboratory testing and wireline logging in providing improved estimates of formation parameters relevant to reservoir and completion quality. This study presents a state-of-the-art review of the latest methods and technologies in drill cuttings analysis to enable real-time characterization of the entire suite of formation properties, including chemical composition, densities and porosity, permeability, lithology, geomechanical properties, and characterization of fracture patterns. Specifically, the methods/techniques that enable characterizing drill cuttings in real-time and critically reviewed in this study include Raman spectroscopy for chemical composition, nuclear magnetic resonance for densities and porosity, liquid pressure pulse for permeability, deep learning for rock classification, 7 different methods for geomechanical properties, and mud loss signatures for characterization of fracture patterns. Benchmark comparison of drill cuttings analysis with the measurements from the core samples at similar depths is also reviewed. Key learnings are provided in 4 areas: to address the uncertainties in estimates of specific parameters affected by physical deformations due to drill bits, minimum cutting size for reliable nuclear magnetic resonance data, sweet spot identification, and power and network considerations for real-time analysis, respectively. Cited as: Singh, H., Li, C., Cheng, P., Wang, X., Hao, G., Liu, Q. Automated real-time formation evaluation from cuttings and drilling data analysis: State of the art. Advances in Geo-Energy Research, 2023, 8(1): 19-36. https://doi.org/10.46690/ager.2023.04.03

Improved Surrounding Rock Classification Method for the Middle Rock Pillar of a Small Clear-Distance Tunnel

In tunnel design and construction, proper and accurate classification of rock surrounding tunnels is needed to ensure tunnel construction safety, guarantee construction quality, and reduce project costs. With rapid urbanization, numerous small clear-distance tunnels have been constructed in dense urban road networks. Compared with ordinarily separated tunnels, the construction scale and difficulty of small clear-distance tunnels are greater, and the requirements for the classification of rock surrounding tunnels are accordingly higher. A small clear-distance tunnel in an urban super large and complex underground interchange hub of the Xiamen Haicang Evacuation Channel was selected as the background, and the classification method of the middle rock pillar in a small clear-distance tunnel is presented based on the general classification standard of surrounding rocks. Based on the geometric, physical, and mechanical factors of the middle rock pillar, six indices affecting the stability and quality of the middle rock pillar were selected, and the classification index system of the middle rock pillar was established from the two dimensions of the basic and auxiliary indices. The basic and auxiliary indices were scored using the scoring method, and the different grades of the middle rock pillar were divided according to different scores. The middle rock pillar classification standard was applied to the quality assessment of the middle rock pillar, which provided a basis for the on-site assessment of the quality of the middle rock pillar and proved the accuracy and superiority of the improved classification standard. The newly established classification standard can provide a reference for selecting the correct construction method and supporting structure type for small clear-distance tunnels.

Cementation factor in clayey rock samples: investigating the role of clay content and determination using electrical rock classification

Accurate determination of cementation factor (m) has a significant role in the petrophysical characterization of underground reservoirs. For clean sandstones, the application of this parameter in Archie's Equation provides a basic framework for estimating water saturation. However, the presence of clay minerals in the rock texture could affect the evaluation process. There are different correlations from which the cementation factor can be estimated. In addition, applying rock classification techniques revealed an excellent prospect for better estimating this parameter. However, in most of the mentioned approaches, the effect of clay minerals is missed. In this study, a newly developed modified form of electrical rock typing technique (i.e. classification of rock samples according to their electrical properties) incorporating the effect of clay minerals for improving the prediction of cementation factor in clay-bearing rocks is proposed. In doing so, a new form of electrical quality index is defined based on which the modified formation resistivity factor versus porosity data for 204 clayey rock samples are classified into nine distinct rock types. Comparative studies demonstrated the priority of the suggested methodology rather than classic hydraulic flow unit-based methods such as flow zone indicator and discrete rock type. The latter showed a high degree of scattering in the classification stage. For each rock type, developed based on the proposed scheme, the equations were proposed in which the modified form of formation resistivity factor and cementation factor versus porosity were correlated. Linear relationships with reasonable values of coefficient of determination were then demonstrated for the developed equations. In addition, the effect of clay minerals on the rock's electrical quality and electrical properties (i.e., rock conductivity and cementation factor) is discussed. The results of this study offer a better framework to incorporate the effect of clay constituents in the prediction of cementation factor, as compared to classical rock typing approaches.

Unsupervised ore/waste classification on open-cut mine faces using close-range hyperspectral data

The remote mapping of minerals and discrimination of ore and waste on surfaces are important tasks for geological applications such as those in mining. Such tasks have become possible using ground-based, close-range hyperspectral sensors which can remotely measure the reflectance properties of the environment with high spatial and spectral resolution. However, autonomous mapping of mineral spectra measured on an open-cut mine face remains a challenging problem due to the subtleness of differences in spectral absorption features between mineral and rock classes as well as variability in the illumination of the scene. An additional layer of difficulty arises when there is no annotated data available to train a supervised learning algorithm. A pipeline for unsupervised mapping of spectra on a mine face is proposed which draws from several recent advances in the hyperspectral machine learning literature. The proposed pipeline brings together unsupervised and self-supervised algorithms in a unified system to map minerals on a mine face without the need for human-annotated training data. The pipeline is evaluated with a hyperspectral image dataset of an open-cut mine face comprising mineral ore martite and non-mineralised shale. The combined system is shown to produce a superior map to its constituent algorithms, and the consistency of its mapping capability is demonstrated using data acquired at two different times of day.

Geotechnical zoning in Hai Duong province for construction planning

Information on geology and engineering plays an important role in construction activities, forecasting adverse problems, and choosing the foundation solutions. The management and use of this information on the basis of zoning geological conditions will contribute to ensure the sustainability in economic exploitation of the territory in general and in construction development in particular. This paper presents the results of analysis, evaluation, and systematization of geological, tectonic, geomorphological, hydrogeological, and engineering geological data in Hai Duong province from the point of view of engineering geology. In particular, the geological conditions for construction are evaluated on the basis of classification of soil and rock of F.P Xavarenski and standard TCVN 9362-2012. Accordingly, soil and rock in the study area are divided into 5 groups: hard rock (I), semi-hard rock (II), cohesiveless soil (III), cohesive soil (IV) and soft soil (V) and then they are classified according to construction properties. Applying the partitioning method of I.V. Popov, the geotechnical conditions of the study area are divided into 3 zones: I, II, III and 3 sub-zones (III.a, III.b, III.c). On that basis, engineering geology problems in each zone and sub-zone are analyzed and evaluated as the basis for planning, serving for digitization and integration relating to digitized maps.

Interpretation of well logs and core data via Bayesian inversion

Estimating in situ petrophysical and compositional properties of rocks (e.g., porosity, mineralogy, and fluid saturation) from well logs and core measurements is critical for the evaluation of subsurface fluid resources. Traditional multimineral analysis of well logs is susceptible to abnormal borehole and geometric conditions, such as shoulder-bed effects and tool/borehole-related biases. Moreover, traditional multimineral analysis rarely includes uncertainty quantification, much less assessments of the impact of measurement noise, borehole conditions, and/or inaccurate rock-physics models (RPMs) on estimated properties. Probabilistic formation evaluation with credible-interval estimations can improve the outcome of mineral and fluid evaluations from well logs by explicitly incorporating the effects of measurement biases and a priori compositional and petrophysical constraints in the estimation. A probabilistic workflow is developed to estimate petrophysical/compositional properties from well logs and available core data. The workflow consists of two sequential steps: borehole measurements (e.g., density, resistivity, and gamma ray) are first converted into a layer-by-layer earth model, which consists of piecewise constant property values, with associated uncertainty estimations. The estimated earth model is tool/borehole independent, allowing for a common baseline to effectively compare properties among neighboring wells. Next, the estimated earth-model physical properties are used to derive petrophysical properties (e.g., volumetric concentration of fluid and solid rock constituents) via petrophysical joint inversion. In each step, Bayesian inversion is implemented with Markov chain Monte Carlo sampling in contrast to deterministic algorithms used by traditional multimineral methods. The developed method has the following advantages over traditional approaches: (1) it propagates uncertainty from well logs to petrophysical properties by assimilating measurement noise, tool/borehole-related biases, and RPM errors and (2) it enables the implementation of a priori knowledge per rock class to constrain the petrophysical estimation, effectively integrating field-specific core/regional geologic data. Examples of thinly laminated and organic shale formations successfully verify the reliability, efficiency, and accuracy of our estimation method.

Rapid detection and identification of rocks and minerals by laser‐induced breakdown spectroscopy

AbstractIn geological exploration, it is necessary to analyze the geological history according to the rock types. But in places for people to reach, sample collection, and transportation is a costly task. At present, the remote intelligent detection of rock types needs to be further developed. In this study, laser‐induced breakdown spectroscopy (LIBS), a sensitive optical technique that can rapidly analyze various elements, is applied to real‐time detection and analysis of rock types. Representative rock samples and minerals are selected for spectral analysis and machine learning. The characteristic spectral lines of Ca, Al, Mg, Ti, Si, Na, Fe, K, and Li were observed in the spectra. By comparing the spectra of different samples, the differences among them were discussed. First, principal component analysis (PCA) is used for dimensionality reduction. With the help of PCA, the data are distributed in two‐dimensional and three‐dimensional space and different kinds of rocks and minerals are classified successfully. Then, combined with error back propagation training artificial neural network, the rock and mineral identification model was established, and the recognition rate can reach 100%. The results show that LIBS is a powerful tool for remote intelligent real‐time rock detection and classification, and has great application prospects in the exploration of extraterrestrial objects including planets, satellites and asteroids in the future.

Rock image classification using deep residual neural network with transfer learning

Rock image classification is a significant part of geological research. Compared with traditional image classification methods, rock image classification methods based on deep learning models have the great advantage in terms of automatic image features extraction. However, the rock classification accuracies of existing deep learning models are unsatisfied due to the weak feature extraction ability of the network model. In this study, a deep residual neural network (ResNet) model with the transfer learning method is proposed to establish the corresponding rock automatic classification model for seven kinds of rock images. ResNet34 introduces the residual structure to make it have an excellent effect in the field of image classification, which extracts high-quality rock image features and avoids information loss. The transfer learning method abstracts the deep features from the shallow features, and better express the rock texture features for classification in the case of fewer rock images. To improve the generalization of the model, a total of 3,82,536 rock images were generated for training via image slicing and data augmentation. The network parameters trained on the Texture Library dataset which contains 47 types of texture images and reflect the characteristics of rocks are used for transfer learning. This pre-trained weight is loaded when training the ResNet34 model with the rock dataset. Then the model parameters are fine-tuned to transfer the model to the rock classification problem. The experimental results show that the accuracy of the model without transfer learning reached 88.1%, while the model using transfer learning achieved an accuracy of 99.1%. Aiming at geological engineering field investigation, this paper studies the embedded deployment application of the rock classification network. The proposed rock classification network model is transplanted to an embedded platform. By designing a rock classification system, the off-line rock classification is realized, which provides a new solution for the rock classification problem in the geological survey. The deep residual neural network and transfer learning method used in this paper can automatically classify rock features without manually extracting. These methods reduce the influence of subjective factors and make the rock classification process more automatic and intelligent.

Application of a Combined FEM/DEM Approach for Teaching a Deep Rock Mass Mechanics Course

Deep rock mass mechanics is a professional course which is offered to undergraduate and postgraduate students in some mining universities. This course mainly includes the following topics: the geological structure of deep rock mass, the mechanical properties of deep rocks, the strength theory of deep rock masses, stability analysis and control of deep surrounding rock classification of engineering rock masses, and the application of deep rock mechanics in underground mining engineering The purpose of this course is to present students with a basic theoretical knowledge of deep rock mass engineering. Analyzing the limitations of traditional deep rock mass mechanics teaching methods, here, we propose integrating a combined FEM/DEM (Finite Element Method/Discrete Element Method) approach into the teaching of a course on deep rock mass mechanics. The mechanical behaviors and failure instability process of rock at laboratory and engineering scales were analyzed using ELFEN software (a finite/discrete element code). The results show that a combined FEM/DEM approach as a deep rock mass mechanics teaching method is completely feasible and reasonable; this approach has the advantages of strong intuition, high reliability, time and labor savings, and low cost, which can offset the shortcomings of traditional teaching methods. Moreover, the proposed approach can stimulate students’ interests in a mining course on deep rock mass mechanics, deepen students’ understanding of the course curriculum, and cultivate students’ innovative abilities and subjective initiatives.

A real-time intelligent classification model using machine learning for tunnel surrounding rock and its application

ABSTRACT Real-time and accurate prediction of surrounding rock grade is crucial for tunnel dynamic construction and design. However, the internationally accepted semi-quantitative methods (e.g. rock mass rating (RMR), Q, and basic quality (BQ)) cannot provide fast and accurate classification in construction. This study proposed an intelligent surrounding rock classification method and a tunnel information management system, which can predict the surrounding rock grade in real-time and accurately. A database is collected with 286 cases in China, including seven geological parameters and surrounding rock grades. Based on different training parameters, 12 classification models are established using VGGNet, ResNet, and support vector machine (SVM) algorithms. The accuracy of the SVM classifier is 93.02%, which performs better than the VGGNet and ResNet classifiers. Moreover, precision, recall, F-measure, receiver operating characteristic (ROC), and 20-case verification show that the SVM classification model has greater robustness in learning and generalising for small and imbalanced samples. Additionally, a tunnel information management system is developed with cloud technology, which can accurately predict the surrounding rock grade within 10 s. Overall, the achievements of this study can provide valuable references for real-time rock mass classification in traffic tunnels and underground powerhouses.

The rock mass and the development of joints during the tunnel excavation

The term joint has been historically used imprecisely when carrying out geomechanical characterization of rock mass. The current classification systems consider the different fracture planes simply as they are observed visually, assuming independence of dimensions and other fixed patterns with depth. That can lead to over-design underground works. Joint patterns in a rock mass depend on the change of the stress field acting on the rock from its original confinement state. The stress field on the rock mass is not constant and varies with changing distance in any underground excavation. In tunnel analyses, empirical classifications consider the parameters related to the joints in the rock mass as constant variables. Those classifications, constitute more than 75% of the valuation without considering the effect of the development and frequencies of joints according to the change of the stress field. In this work, it is considered the necessity to broaden the definition of joints as non-moving fractures for which the development of the fracture plane depends on the change of the stress field in the rock mass. Regarding the discontinuity term, it is important to differentiate the joints from the stratification and foliation planes, which are terms associated with the classification of rocks according to its origin, particularly for sedimentary and metamorphic, respectively. These are made up of continuous planes that control the mechanical behavior according to the stresses acting in the rock mass. Considering a tunnel excavated in an igneous rock mass, the decompression due to the opening effect differs from a stratified or foliated massif. The first one depends on the development of joints, and the second, mainly on the phenomenon of bending of layers within the rock mass, according to the relative dimensions of layer thickness to tunnel diameter. In this article, several practical examples are presented which support the hypothesis that joints develop as a function of the change in the stress field. Also, by means of examples applying numerical methods, it is shown that the deformations of a rock mass with constant parameters are bigger than those when a zoned rock mass is considered, including changes in the geomechanical characteristics related to the distance in the tunnel excavation. The results have an impact on the thickness of supporting elements, yielding lower construction costs.

Engineering and geological conditions of dam construction

The article presents studies of the engineering-geological conditions of dam construction, consisting of the structure of river valleys and slopes, conditions of rock occurrence, geotechnical and filtration properties of rocks, and geological conditions of the dam construction site. A classification of valley types with schemes, classification of rocks about the location of dams, and types of tectonic structure of valleys about the construction of dams are presented. Conclusions are drawn about the most favorable and unfavorable conditions for the construction of dams.

Petrographic Characteristic and Geochemical features of Basement Rocks in Ikogosi, Southwestern Nigeria.

This study investigates and reports petrographic characteristics, geochemical features and the processes of formation of rocks in Ikogosi, southwestern Nigeria. Eighteen (18) rock samples (six quartzite, six migmatite gneiss and six schists) were collected and subjected to optical study. Ten (10) representative samples (six quartzite, three schist and one migmatite gneiss) were evaluated for major elements composition using Energy Dispersive X-Ray Fluorescence (ED-XRF). Petrographic evaluation revealed quartz, feldspar, muscovite, biotite, hornblende, pyroxene and opaque are dominant mineral constituents in quartzite and schist while migmatite gneiss contains the same set of minerals but without opaque constituents. Quartz forms a dominant constituent in quartzite and migmatite gneiss but as subordinate mineral in the schist. The feldspars are mainly plagioclase with albite twinning, associated microcline has characteristic crosshatched twinning. Chemical investigation reveals the rock units are generally siliceous. Three oxides (SiO2, Al2O3 and Fe2O3) constitute 85–90% of the bulk chemical composition of the rocks. SiO2 constituent as applied in rock classification indicates the quartzite is unequivocally felsic, migmatite is intermediate while the schist is mafic to ultramafic. K2O versus SiO2 variation plot revealed that the quartzite and schist are tholeiitic while the migmatite gneiss is shoshonitic. TiO2 versus SiO2 variation diagram depicts igneous antecedent.

АНАЛІЗ МАГНІТНОЇ АНІЗОТРОПІЇ ЗРАЗКІВ ПОРІД ПОЛІГОНУ КРИВОРІЗЬКОЇ НАД ГЛИБОКОЇ СВЕРДЛОВИНИ

Understanding the reasons and principles of anisotropy of geological rocks opens the way to assess geodynamic processes in regions, enables to track changes of geological factors that form them, provides information about the structure and directions of the dynamics of geological rocks and etc. The aim of this study is the investigation of the nature of magnetic anisotropy of a collection of 26 samples that were taken from the well "Suputnyk-2" (490–2720 m) of the Kryvorizka extremely deep well. The samples consist of the following rocks: biotite schists, gneisses, ferruginous quartzites, muscovite quartzites, microcline-plagioclase granites. The authors used equipment of the KLY-2 series to measure the magnetic susceptibility of samples having the shape of cuberhombic decahedron. The authors have performed primary processing of measurement results and calculation of anisotropy parameters in the ANIZO10 program. The following parameters were determined as a result of the analysis: value of magnetic susceptibility (χ); statistical value of the standard deviation; normal values of magnetic susceptibility along three axes; coefficients characterizing magnetic anisotropy (H1 – H6); magnetic inclination angle (D) and magnetic inclination angle (I). The authors performed an initial classification of rocks based on the practical data processing scheme. Analyzing the calculations based on the ellipsoid of magnetic susceptibility, four rock classes were obtained: the shape of a sphere (12 %), a flattened shape (46 %), triaxial (23 %) and a complex ellipsoid shape (19 %). This indicates the complex geological conditions where rocks from the well "Suputnyk-2" (490–2720 m) were formed. It is confirmed that the samples belong to the VІІ–X tectonofacies according to the authors' previous research. The authors have investigated the coefficient of differential magnetic anisotropy, such as acoustic anisotropy. According to the results of the calculations, it was established that samples with low magnetic anisotropy are only 3.8 % of the total number of samples. Highly anisotropic samples of the collection comprise 53.8 % of the total number. It indicates the variety of processes that affected the rocks during their formation. According to the previous research of the authors, the coefficient of acoustic anisotropy of the well samples also varies from 2.8 to 24.9 %. The symmetry of the acoustic tensor in the majority of rock textures is rhombic. The final step in comparing the research results of the parameters of acoustic and magnetic anisotropy involved a tectonofacial analysis of the obtained data. It was determined that both magnetic and acoustic anisotropy coefficients, calculated from their respective tensors, reflect changes in tectonofacial conditions during the formation of the studied rocks.

Towards Multiscale Digital Rocks: Application of a Sub-Resolution Production Model to a multiscale Sandstone

Many digital rock methodologies use a direct simulation approach, where only resolved pores are accounted for. This approach limits the types of rocks that can be analyzed, excluding some types of carbonates, unconventionals, and complex sandstones from the digital rock analysis. This is due to the challenge for single scale imaging to capture the full range of relevant pore sizes present in multiscale rocks. In this paper, a physical model is presented, within the context of an established direct simulation approach, to predict the production of hydrocarbons including the contribution of sub-resolution pores. The direct simulation component of the model employs a multiphase lattice Boltzmann method to simulate multiphase fluid flow displacement in resolved pores. In the production model, the amount of hydrocarbons present in the sub-resolution pores is identified and a physical description of the production behavior is provided. This allows a relative permeability curve to be predicted for rocks where mobile hydrocarbons are present in pores smaller than the image resolution. This simplified model for the oil movement in the unresolved pore space is based on a physical interpretation of different regions marked by simulation resolution limits in a USBM wettability test curve. The proposed methodology is applied to high-resolution microCT images of a sandstone that contains pores at multiple scales, some resolved and some not resolved. To allow for benchmarking, experimental routine and special core analysis data was also obtained. Good agreement to experimental results is observed, specifically in absolute and relative permeability. The presented multiscale model has the potential to extend the classes of reservoir rocks eligible for digital rock analysis and paves the way for further advancements in the modelling of multiscale rocks, particularly unconventionals and carbonates.