Integrated Multi-Physics Workflow for Automatic Rock Classification and Formation
            Evaluation Using Multi-Scale Image Analysis and Conventional Well Logs

González, Ángel; Kanyan, Lawrence; Heidari, Zoya; Lopez, Olivier

doi:10.30632/t60als-2019_a

Cited by 7 publications

(3 citation statements)

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“…For example, De Lima utilized deep learning and transfer learning in a number of works [11][12][13] to classify images of rocks based on rock thin section and borehole image logs and CT images. Gonzalez et al [14] introduced an automatic rock classification method using a process flow that merges core CT images, optical core photographs, conventional well logs, and routine core analysis (RCA) data. In the context of this process flow, the relevant rock-fabric features are employed to identify the rock classes by the utilization of a clustering algorithm after being extracted from all core CT images and core photographs.…”

Section: Related Workmentioning

confidence: 99%

RockDNet: Deep Learning Approach for Lithology Classification

Abdullah,

Mohammed,

Awad

2024

Applied Sciences

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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.

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Section: Related Workmentioning

confidence: 99%

RockDNet: Deep Learning Approach for Lithology Classification

Abdullah,

Mohammed,

Awad

2024

Applied Sciences

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“…The advent of machine learning techniques, exemplified by fuzzy clustering and neural networks, represents a significant evolution [20][21][22][23][24]. These methods focus on stratification using individual well-logging curves, potentially leading to imprecision, especially in instances of ambiguous curve delineation [25].…”

Section: Introductionmentioning

confidence: 99%

DRAG: A Novel Method for Automatic Geological Boundary Recognition in Shale Strata Using Multi-Well Log Curves

Zhou,

Zhu,

Zhu

et al. 2023

Processes

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Ascertaining the positions of geological boundaries serves as a cornerstone in the characterization of shale reservoirs. Existing methods heavily rely on labor-intensive manual well-to-well correlation, while automated techniques often suffer from limited efficiency and consistency due to their reliance on single well log data. To overcome these limitations, an innovative approach, termed DRAG, is introduced, which uses deep belief forest (DBF), principal component analysis (PCA), and an enhanced generative adversarial network (GAN) for automatic layering recognition in logging curves. The approach employed in this study involves the use of PCA for dimensionality reduction across multiple well log datasets, coupled with a sophisticated GAN to generate representative samples. The DBF algorithm is then applied for stratification, incorporating a confidence screening mechanism to improve computational efficiency. In order to improve both accuracy and stability, a coordinate system is introduced that adjusts for stratification variations among neighboring wells around the target well. Experimental comparisons demonstrate the superior performance of the proposed algorithm in reducing stratification fluctuations and improving precision.

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“…Gonzalez et al [17] considered a workflow for an automatic rock classification that combines conventional well logs, whole core CT images, optical core photographs, and routine core analysis (RCA) data. In this workflow, rock-fabric-related features are first extracted from whole core CT images and core photographs and then used to determine the rock classes by means of a clustering algorithm.…”

Section: Introductionmentioning

confidence: 99%

Lithology classification of whole core CT scans using convolutional neural networks

Chawshin

Berg

Varagnolo³

et al. 2021

SN Appl. Sci.

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X-ray computerized tomography (CT) images as digital representations of whole cores can provide valuable information on the composition and internal structure of cores extracted from wells. Incorporation of millimeter-scale core CT data into lithology classification workflows can result in high-resolution lithology description. In this study, we use 2D core CT scan image slices to train a convolutional neural network (CNN) whose purpose is to automatically predict the lithology of a well on the Norwegian continental shelf. The images are preprocessed prior to training, i.e., undesired artefacts are automatically flagged and removed from further analysis. The training data include expert-derived lithofacies classes obtained by manual core description. The trained classifier is used to predict lithofacies on a set of test images that are unseen by the classifier. The prediction results reveal that distinct classes are predicted with high recall (up to 92%). However, there are misclassification rates associated with similarities in gray-scale values and transport properties. To postprocess the acquired results, we identified and merged similar lithofacies classes through ad hoc analysis considering the degree of confusion from the prediction confusion matrix and aided by porosity–permeability cross-plot relationships. Based on this analysis, the lithofacies classes are merged into four rock classes. Another CNN classifier trained on the resulting rock classes generalize well, with higher pixel-wise precision when detecting thin layers and bed boundaries compared to the manual core description. Thus, the classifier provides additional and complementing information to the already existing rock type description. Article Highlights A workflow for automatic lithofacies classification using whole core 2D image slices and CNN is introduced. The proposed classifier shows lithology-dependent accuracies. The prediction confusion matrix is exploited as a tool to identify lithofacies classes with similar transport properties and to automatically generate lithofacies hierarchies.

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Integrated Multi-Physics Workflow for Automatic Rock Classification and Formation Evaluation Using Multi-Scale Image Analysis and Conventional Well Logs

Cited by 7 publications

References 0 publications

RockDNet: Deep Learning Approach for Lithology Classification

RockDNet: Deep Learning Approach for Lithology Classification

DRAG: A Novel Method for Automatic Geological Boundary Recognition in Shale Strata Using Multi-Well Log Curves

Lithology classification of whole core CT scans using convolutional neural networks

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