A Novel Approach for Automated Lithology Extraction from Core Photographs

Thomas, A.; Rider, M.; Curtis, A.; MacArthur, Alasdair

doi:10.3997/2214-4609.201400891

Cited by 2 publications

(3 citation statements)

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“…Subsequent classification was carried out on samples from the same well. Thomas et al (2011) applied the trained classifier to the whole dataset in order to classify lithotypes and the results were checked manually by an expert. Additional samples were added or removed from the nearest neighborhood model if the results were not reasonable.…”

Section: Introductionmentioning

confidence: 99%

Deep convolutions for in-depth automated rock typing

Baraboshkin

Ismailova

Orlov

et al. 2020

Computers & Geosciences

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The description of rocks is one of the most time-consuming tasks in the everyday work of a geologist, especially when very accurate description is required. We here present a method that reduces the time needed for accurate description of rocks, enabling the geologist to work more efficiently. We describe the application of methods based on color distribution analysis and feature extraction. Then we focus on a new approach, used by us, which is based on convolutional neural networks. We used several well-known neural network architectures (AlexNet, VGG, GoogLeNet, ResNet) and made a comparison of their performance. The precision of the algorithms is up to 95% on the validation set with GoogLeNet architecture. The best of the proposed algorithms can describe 50 m of full-size core in one minute.

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Section: Introductionmentioning

confidence: 99%

Deep convolutions for in-depth automated rock typing

Baraboshkin

Ismailova

Orlov

et al. 2020

Computers & Geosciences

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“…The core can be analyzed in a laboratory to determine the formation tops and lithology, as well as other information about the rock's physical and chemical properties. 12,13 Cuttings analysis for the rock cuttings that are brought to the surface during drilling can be performed. The cuttings can be analyzed to determine the formation tops and lithology, as well as other information about the rock's physical and chemical properties.…”

Section: Introductionmentioning

confidence: 99%

“…There are several methods that can be used to define formation tops and lithology including well logging, core sampling, cuttings analysis, seismic surveys, and mud logging. − The logging tool takes measurements of the rock’s various physical and chemical properties, such as gamma ray (GR), density, porosity, and resistivity, which can be used to infer the formation tops and lithology. − Core sampling involves physically removing a cylindrical sample of rock, called a “core”, from the formation. The core can be analyzed in a laboratory to determine the formation tops and lithology, as well as other information about the rock’s physical and chemical properties. , Cuttings analysis for the rock cuttings that are brought to the surface during drilling can be performed. The cuttings can be analyzed to determine the formation tops and lithology, as well as other information about the rock’s physical and chemical properties. , Seismic surveys involve the use of sound waves to create images of the subsurface.…”

Section: Introductionmentioning

confidence: 99%

Applications of Different Classification Machine Learning Techniques to Predict Formation Tops and Lithology While Drilling

Ibrahim,

Ahmed,

Elkatatny

2023

ACS Omega

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Accurate prediction of formation tops and lithology plays a critical role in optimizing drilling processes, cost reduction, and risk mitigation in hydrocarbon operations. Although several techniques like well logging, core sampling, cuttings analysis, seismic surveys, and mud logging are available for identifying formation tops, they have limitations such as high costs, lower accuracy, manpower-intensive processes, and time or depth lags that impede real-time estimation. Consequently, this study aims to leverage machine learning models based on easily accessible drilling parameters to predict formation tops and lithologies, overcoming the limitations associated with traditional methods. Data from two wells (A and B) in the Middle East, encompassing drilling mechanical parameters such as rate of penetration (ROP), drill string rotation (DSR), pumping rate (Q), standpipe pressure (SPP), weight on bit (WOB), and torque, were collected for real-field analysis. Machine learning models including Gaussian naive Bayes (GNB), logistic regression (LR), and linear discriminant analysis (LDA) were trained and tested on the data set from well A, while the data set from well B was utilized for model validation as unseen data. The formations of wells A and B consist of four lithologies, namely, sandstone, anhydrite, carbonate/shale, and carbonates, necessitating the development of multiclass classification models. The drilling parameters, specifically the WOB and ROP, exhibited a strong influence on lithology identification. Among the models, GNB demonstrated exceptional performance in predicting formation lithology from the drilling parameters, achieving accuracy and nearly perfect precision, recall, and F1 score for the different classes. LDA and LR models accurately predicted sandstone and carbonate lithologies, although some misclassifications occurred in approximately 5% of points for anhydrite and around 20% in carbonate/shale formations. During validation, the models demonstrated accuracies of around 0.96, 0.95, and 0.92 for the GNB, LR, and LDA, respectively. The study highlights the efficacy of the developed machine learning models in accurately predicting the formation lithology and tops in real time. This is achieved by utilizing readily available drilling parameters, making the approach highly accurate and cost effective by leveraging existing real-time drilling data.

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A Novel Approach for Automated Lithology Extraction from Core Photographs

Cited by 2 publications

References 0 publications

Deep convolutions for in-depth automated rock typing

Deep convolutions for in-depth automated rock typing

Applications of Different Classification Machine Learning Techniques to Predict Formation Tops and Lithology While Drilling

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