Rock classification based on resistivity patterns in electrical borehole wall images (original) (raw)
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Multi-class supervised classification of electrical borehole wall images using texture features
Computers & Geosciences, 2011
Electrical borehole wall images represent micro-resistivity measurements at the borehole wall. The lithology reconstruction is often based on visual interpretation done by geologists. This analysis is very time-consuming and subjective. Different geologists may interpret the data differently. In this work, linear discriminant analysis (LDA) in combination with texture features is used for an automated lithology reconstruction of ODP (Ocean Drilling Program) borehole 1203A drilled during Leg 197. Six rock groups are identified by their textural properties in resistivity data obtained by a Formation MircoScanner (FMS). Although discriminant analysis can be used for multi-class classification, non-optimal decision criteria for certain groups could emerge. For this reason, we use a combination of 2-class (binary) classifiers to increase the overall classification accuracy. The generalization ability of the combined classifiers is evaluated and optimized on a testing dataset where a classification rate of more than 80% for each of the six rock groups is achieved. The combined, trained classifiers are then applied on the whole dataset obtaining a statistical reconstruction of the logged formation. Compared to a single multi-class classifier the combined binary classifiers show better classification results for certain rock groups and more stable results in larger intervals of equal rock type.
Texture Analysis for Automated Classification of Geologic Structures
2006
Texture present in aeromagnetic anomaly images offers an abundance of useful geological information for discriminating between rock types, but current analysis of such images still relies on tedious, human interpretation. This study is believed to be the first effort to quantitatively assess the performance of texture-based digital image analysis for this geophysical exploration application. We computed several texture measures and determined the best subset using automated feature selection techniques. Pattern classification experiments measured the ability of various texture measures to automatically predict rock types. The classification accuracy was significantly better than a priori probability and prior weights-of-evidence results. The accuracy rates and choice of texture measures that minimize the error rate are reported
Earth Sciences Research Journal, 2018
In this research algorithms of support vector machine (SVM) and a logic function were applied to identify automatically sections of carbonate rocks in wells located in the former Barco Concession, Catatumbo Basin - Colombia. During training stages the SVMs use neutron, photoelectric factor and gamma ray logs as input; also mean and variance of resistivity acquired for image tool and fractal dimension of resistive images. The first SVM employs in the training stage intervals manually interpreted of fossiliferous limestone, performed by a specialized geologist integrating information of core-logs correlation of a pilot well; afterwards, in classification stages, this SVM automatically recognizes intervals with fossiliferous limestone only using logs data of any well of the field. The second SVM was also trained with nuclear logs, resistivity and fractal dimension, but in this case, with information of intervals composed of calcareous shales interbedded with limestone, recognizing auto...
Automated Segmentation of Resistivity Image Logs Using Wavelet Transform
Mathematical Geosciences, 2009
We describe a wavelet-transform-based method for automated segmentation of resistivity image logs that takes into account the apparent dip in the data and addresses the problem of discriminating lithofacies boundaries from noise and intrafacies variations. Our method can be applied to borehole measurements in general, but might have an advantage when applied to resistivity image logs as it addresses explicitly the large variability in facies segments recorded with a high-resolution multiple-sensor tool. We have developed an algorithm based on this method that might outperform other existing segmentation methods in the cases of low to moderate dip. We made a detailed comparison of the segmentation from our method with the one done by a geologist to delineate different lithofacies blocks in a well drilled in a deepwater depositional environment. Our results show considerable success rates in reproducing the geologically defined lithofacies boundaries, and the generality of our procedure suggests it could also be applied to other depositional environments.
PROBABILISTIC BASED ROCK TEXTURE CLASSIFICATION
The classification of natural images is an essential task in computer vision and pattern recognition applications. Rock images are the typical example of natural images, and their analysis is of major importance in rock industries and bedrock investigations. Rocks are mainly classified into three types. They are Igneous, Metamorphic and Sedimentary. They are further classified into Andesite, Basalt, Amphibolite, Granite, Breccia, Coal and etc… In this project classification is done in three subdivisions. First the given rock image is classified into major class. Next it is classified into subclass. Finally the group of coal images is segmented and classified using Tamura features, Probabilistic Latent Semantic Analysis (PLSA) and Sum of Square Difference classifier. Rock image classification is based on specific visual descriptors extracted from the images. Using these descriptors images are divided into classes according to their visual similarity. This project deals with the rock image classification using two approaches. Firstly the textural features of the rock images are calculated by applying Tamura features extraction method. The Tamura features are Coarseness, Contrast, Directionality, Line likeness, Roughness and Regularity, Smoothness and Angular second moments. In next step calculated Tamura features are applied to Probabilistic Latent Semantic Analysis (PLSA) to generate a topic model. This topic model is applied to SSD classifier to classify the rock image into one of the major class. Similarly the rock textures are classified into subclass, and the group of coal images is segmented and classified. This method is compared with Gray Level Co-occurrence Matrix (GLCM) method and Color Co-occurrence Matrix method. This method gives a better accuracy when compared to those methods. This technique can readily be applied to automatically classify the rocks in such fields of rock industries and bedrock investigations.
Classification of Rock Images using Textural Analysis
Citation/Export MLA Mr. Sachin K. Latad, Prof.(Dr) S. M. Deshmukh, “Classification of Rock Images using Textural Analysis”, March 15 Volume 3 Issue 3 , International Journal on Recent and Innovation Trends in Computing and Communication (IJRITCC), ISSN: 2321-8169, PP: 1323 - 1325, DOI: 10.17762/ijritcc2321-8169.150393 APA Mr. Sachin K. Latad, Prof.(Dr) S. M. Deshmukh, March 15 Volume 3 Issue 3, “Classification of Rock Images using Textural Analysis”, International Journal on Recent and Innovation Trends in Computing and Communication (IJRITCC), ISSN: 2321-8169, PP: 1323 - 1325, DOI: 10.17762/ijritcc2321-8169.150393
The use of texture measures in improving mine classification performance
Oceans 2003. Celebrating the Past ... Teaming Toward the Future (IEEE Cat. No.03CH37492), 2003
Research over the last 9 years has resulted in an effective mine classification approach that involves the use of image-segmentation based screening methods followed by multilayer perceptron networks for mine classification. The present approach centers around a baseline 23 Feature set related to highlight, shadow, and highlight/shadow contrast statistic based segmentations, and the use of associated statistical and shape related factors. In the work described here we investigate the improvement of baseline performance by incorporating image texture related features such as Cooccurrence Matrix related factors.
Identification of Lithology from Well Log Data Using Machine Learning
EAI endorsed transactions on internet of things, 2024
INTRODUCTION: Reservoir characterisation and geomechanical modelling benefit significantly from diverse machine learning techniques, addressing complexities inherent in subsurface information. Accurate lithology identification is pivotal, furnishing crucial insights into subsurface geological formations. Lithology is pivotal in appraising hydrocarbon accumulation potential and optimising drilling strategies. OBJECTIVES: This study employs multiple machine learning models to discern lithology from the well-log data of the Volve Field. METHODS: The well log data of the Volve field comprises of 10,220 data points with diverse features influencing the target variable, lithology. The dataset encompasses four primary lithologies-sandstone, limestone, marl, and claystoneconstituting a complex subsurface stratum. Lithology identification is framed as a classification problem, and four distinct ML algorithms are deployed to train and assess the models, partitioning the dataset into a 7:3 ratio for training and testing, respectively. RESULTS: The resulting confusion matrix indicates a close alignment between predicted and true labels. While all algorithms exhibit favourable performance, the decision tree algorithm demonstrates the highest efficacy, yielding an exceptional overall accuracy of 0.98. CONCLUSION: Notably, this model's training spans diverse wells within the same basin, showcasing its capability to predict lithology within intricate strata. Additionally, its robustness positions it as a potential tool for identifying other properties of rock formations.
Variogram-Based Descriptors for Comparison and Classification of Rock Texture Images
Mathematical Geosciences, 2019
Rock characterization is typically performed by geologists in mining companies and involves the analysis of several meters of drill-hole samples to describe distinctive geological properties. In this procedure, rock texture is not typically taken into account despite its importance given its close relation with metallurgical responses and, therefore, all mineral processes. To support the work of geology experts, this research seeks to obtain rock texture information, discriminating it from digital images through image processing and machine learning techniques. For this purpose, a geologist-labeled digital photograph database was used with different rock texture classes (including geological textures and structures) from drill-hole samples. To characterize rock texture, three texture descriptors based on variographic information are proposed, which summarize data contained in the image pixels, focusing on local structural patterns that numerically describe its texture properties. Then, based on a methodology of image texture comparison, which could be extended to classify different types of rock texture classes, a quantification of the system's performance was obtained. The results showed a high discrimination among common texture classes using compact variogram-based features that outperformed previous methods applied on the same rock texture database.
Texture-Based Classification Approach to Simulate Absolute Permeability in Reservoir Rock Sample
2018
Carbonate reservoirs represent around half hydrocarbon reserves in the world. However, characterizing rock properties in these reservoirs is highly challenging because of rock heterogeneities revealed at several length scales. In the last two decades, a new approach known as Digital Rock Physics (DRP) revealed high potential to better understand rock properties behaviour at pore scale. This approach uses 3D X-ray Micro tomography images to characterize pore network and also simulate rock properties from these images. Even though, DRP is able to predict realistic rock properties results in sandstone reservoirs it is still suffering from a lack of clear workflow in carbonate rocks. The main challenge is the integration of properties simulated at different scales in order to obtain the effective rock property of core plugs. In this paper, we propose to characterize absolute permeability in a carbonate core plug sample using texture analysis. We propose to segment 3D micro-CT image in t...