Automatic detection of formations using images of oil well drilling cuttings

Khojasteh, Parham; Ahmadyfard, Alireza; Tokhmechi, Behzad; Mirmahdavi, S. A.

doi:10.1016/j.petrol.2014.10.023

Cited by 7 publications

(8 citation statements)

References 18 publications

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“…For example, Linek et al (2007) proposed a lithofacies classification model by training the relationship between the textures from borehole images and preinterpreted lithology from rock-core analysis. In addition, Khojasteh et al (2015) proposed an automatic key bed classification based on drill cutting images. Although the applied images in the previous studies can improve the resolution information of lithologies (e.g., sandstones vs. shales), the methods require preinterpreted lithofacies due to the application of a supervised learning algorithm to construct the interpretation model.…”

Section: Introductionmentioning

confidence: 99%

Interpreting the Subsurface Lithofacies at High Lithological Resolution by Integrating Information From Well‐Log Data and Rock‐Core Digital Images

et al. 2020

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Spectral facies interpretation and classification methods have been proposed to improve the sophistication of interpretation of the subsurface heterogeneity. In the spectral facies interpretations, the intensity values of the RGB spectrum and the local entropy from rock‐core digital images are used, and the results are compared to conventional electrofacies and expert petrophysical interpretations. During the classification, a practically applicable model that identifies the more detailed types of lithofacies is constructed by using a multilayer neural network model, with the interpreted spectral facies and well‐log data from the corresponding depths used as response and explanatory variables, respectively. Core digital images and five types of well‐log data from the Satyr 5 well in Western Australia are applied for the actual implementation. Through comparative interpretations, three spectral facies are identified as separable lithofacies (i.e., shale, shaly‐sandstone, and sandstone lithofacies), which is supported by detailed HyLogger mineralogy along the tested cores. On the other hand, two electrofacies (i.e., shale‐dominant and sand‐dominant facies) are identified by a conventional method. In the classification based on the spectral facies, the trained multilayer neural network model showed high prediction accuracy for all the lithofacies. Based on these observations, it is confirmed that more precise lithofacies interpretation and classification can be conducted with the developed methods. The developed methods have the potential to improve subsurface characterization when high lithological resolution is essential.

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Section: Introductionmentioning

confidence: 99%

Interpreting the Subsurface Lithofacies at High Lithological Resolution by Integrating Information From Well‐Log Data and Rock‐Core Digital Images

et al. 2020

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“…Recognition and classification of geological image have not been an object of active research in recent years although there have been some studies in this field. Khojasteh et al [ 10 ] applies color and texture analysis for classification of keybeds in Gachsaran, and the upper Asmari formations and classification is done by using the SVM. Tools for classification were in that research co-occurrence matrix and fuzzy c-mean clustering (FCM).…”

Section: Introductionmentioning

confidence: 99%

A two-stage classification method for borehole-wall images with support vector machine

et al. 2018

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Analyzing geological drilling hole images acquired by Axial View Panoramic Borehole Televiewer (APBT) is a key step to explore the geological structure in a geological exploration. Conventionally, the borehole images are examined by technicians, which is inefficient and subjective. In this paper, three dominant types of borehole-wall images on coal-rock mass structure, namely, border images, fracture images and intact rock mass images are mainly studied. The traditional image classification methods based on unified feature extraction algorithm and single classifier is not effect for the borehole images. Therefore, this paper proposes a novel two-stage classification approach to improve the classification performance of borehole images. In the first-stage classification, the border images are identified from three kinds of images based on texture features and gray-scale histograms features. For the remaining two types of images, in the second-stage classification, Gabor filter is first applied to segment the region of interest (ROI) (such as microfracture, absciss layer and horizontal cracks, etc.) and the central interference region. Then, using the same feature vector after eliminating the central interference region, fracture images are separated from intact rock mass images. We test our two-stage classification system with real borehole images. The results of experimental show that the two-stage classification method can effectively classify three major borehole-wall images with the correction rate of 95.55% in the first stage and 95% in the second stage.

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“…Recently there has been renewed interest in the data available from drill cuttings and the application of advanced approaches. Experimental procedures and modelling techniques are developed for extracting reservoir properties from small sizes of drill cutting samples (Sliwinski et al, 2009;Khojasteh et al, 2015;Clarkson and Haghshenas, 2016;Haghshenas et al, 2016).…”

Section: Iv65 Connections With Previous Results From the Study Areamentioning

confidence: 99%

“…The simplest and the most obvious method to analyse drill cuttings because of their small sample size is microscopy and polarization microscopy to petrographic analysis (Richards, 1930). However, the method of digital image analysis enables to get much more information about the small sized samples than using of the conventional optical analysis (Sliwinski et al, 2009;Khojasteh et al, 2015). The recognition of the reservoir properties from drill cutting samples can be fulfilled by different analytical methods such as X-Ray Diffraction (XRD), X-Ray Fluorescence (XRF), Scanning Electron Microscopy (SEM) and Pyrolysis.…”

Section: Iv65 Connections With Previous Results From the Study Areamentioning

confidence: 99%

“…As a result of these methods one can get mineralogical (mineral composition, texture/microstructure), geochemical (major and trace element concentration, TOC), petrographic (lithology, rock structure) and petrophysical (porosity, pore size) information about the drilled formation using exclusively the drill cuttings. This information enables the lithological identification (Bradbury et al, 2007;Khojasteh et al, 2015) log data, and also shows some limitations (Hartel, 2010).…”

Section: Iv65 Connections With Previous Results From the Study Areamentioning

confidence: 99%

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Complex characterisation of Mecsekalja Zone using single quartz grains of drill cuttings from Sztl-1 well

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Automatic detection of formations using images of oil well drilling cuttings

Cited by 7 publications

References 18 publications

Interpreting the Subsurface Lithofacies at High Lithological Resolution by Integrating Information From Well‐Log Data and Rock‐Core Digital Images

Interpreting the Subsurface Lithofacies at High Lithological Resolution by Integrating Information From Well‐Log Data and Rock‐Core Digital Images

A two-stage classification method for borehole-wall images with support vector machine

Complex characterisation of Mecsekalja Zone using single quartz grains of drill cuttings from Sztl-1 well

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