Automatic discrimination of sedimentary facies and lithologies in reef-bank reservoirs using borehole image logs

Chai, Hua; Li, Ning; Xiao, Chengwen; Xing-li, Liu; Li, Duoli; Wang, Caizhi; Wu, Dacheng

doi:10.1007/s11770-009-0011-4

Cited by 31 publications

(9 citation statements)

References 6 publications

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“…One of the methods took the RGB or gray-scale image of the rock slice as input and obtained multiple numerical feature values through image processing, which was used as input to train the multilayer perceptron [22] network to achieve rapid recognition of rock textures [23,24]. Chai et al [25] used a watershed algorithm to extract features from the input borehole images and perform feature selection and finally used it to discriminate the lithology. Although the results achieved by these methods are considerable, the rock features are still processed manually [26], the efficiency of feature extraction is not high and the reliability of the extracted features is not objective.…”

Section: Introductionmentioning

confidence: 99%

Deep learning based lithology classification of drill core images

Wang

et al. 2022

PLoS ONE

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Drill core lithology is an important indicator reflecting the geological conditions of the drilling area. Traditional lithology identification usually relies on manual visual inspection, which is time-consuming and professionally demanding. In recent years, the rapid development of convolutional neural networks has provided an innovative way for the automatic prediction of drill core images. In this work, a core dataset containing a total of 10 common lithology categories in underground engineering was constructed. ResNeSt-50 we adopted uses a strategy of combining channel-wise attention and multi-path network to achieve cross-channel feature correlations, which significantly improves the model accuracy without high model complexity. Transfer learning was used to initialize the model parameters, to extract the feature of core images more efficiently. The model achieved superior performance on testing images compared with other discussed CNN models, the average value of its Precision, Recall, F1−score for each category of lithology is 99.62%, 99.62%, and 99.59%, respectively, and the prediction accuracy is 99.60%. The test results show that the proposed method is optimal and effective for automatic lithology classification of borehole cores.

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Section: Introductionmentioning

confidence: 99%

Deep learning based lithology classification of drill core images

Wang

et al. 2022

PLoS ONE

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“…Therefore, many scholars have improved the automatic process of borehole images, such as image color space transformation and Hough transformation for automatic interpretation of structural planes [28]. Chai Hua et al attempted to use borehole image automatic recognition technology to realize the division of lithology and sedimentary facies in reef-shoal reservoirs [29]. Assous et al realized the automatic detection of the plane features of borehole images through gradient boundary detection and sinusoidal retrieval methods [30].…”

Section: Introductionmentioning

confidence: 99%

A Practical Method for the Automatic Recognition of Rock Structures in Panoramic Borehole Image during Deep-Hole Drilling Engineering

Zou

Wang

Zhang³

et al. 2021

Applied Sciences

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Digital panoramic borehole imaging technology has been widely used in the practice of drilling engineering. Based on many high-definition panoramic borehole images obtained by the borehole imaging system, this paper puts forward an automatic recognition method based on clustering and characteristic functions to perform intelligent analysis and automatic interpretation researches, and successfully applied to the analysis of the borehole images obtained at the Wudongde Hydropower Station in the south-west of China. The results show that the automatic recognition method can fully and quickly automatically identify most of the important structural planes and their position, dip, dip angle and gap width and other characteristic parameter information in the entire borehole image. The recognition rate of the main structural plane is about 90%. The accuracy rate is about 85%, the total time cost is about 3 h, and the accuracy deviation is less than 4% among the 12 boreholes with a depth of about 50 m. The application of automatic recognition technology to the panoramic borehole image can greatly improve work efficiency, reduce the time cost, and avoid the interference caused by humans, making it possible to automatically recognize the structural plane parameters of the full-hole image.

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“…Ge et al (2019) [14] claimed that this method produced a better recognition than it of Wang et al (2017) [15] and made high computational efficiency. As for the discontinuity location part, Chai et al (2009) [16] employed discriminant function analysis (DFA) to recognize discontinuities from resistivity images. This process was established on a classification of GLCM features.…”

Section: Introductionmentioning

confidence: 99%

An Automatic Method for Discontinuity Recognition in Coal-Measure Strata Borehole Images

Yang

et al. 2021

IEEE Access

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An automatic recognition of discontinuities in borehole images is a desirable way to overcome the inefficiency and inconsistency inherent in the conventional method of manual annotation. The rough borehole walls and prominent noise failed the application of existing recognition methods to borehole images taken in the coal-measure strata. This paper presents a novel approach to automatically convert coal-measure strata borehole images into identified discontinuity maps. The developed procedure formed an integrated feature representation for the borehole image through combining the color information of image and the textural features generated from multi-channel filtering. Image regions containing discontinuities are then separated from other regions by implementing fuzzy c-means clustering on the acquired feature representation. The identification of discontinuities is finally accomplished by searching for four predefined patterns (named topographic model) on the intensity transection of image regions. The proposed method is proven to be superior in the respects of noise suppression, discontinuity positioning, and recognition completeness.INDEX TERMS Automatic recognition, Borehole image, Coal-measure strata, Rock discontinuity.

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Automatic discrimination of sedimentary facies and lithologies in reef-bank reservoirs using borehole image logs

Cited by 31 publications

References 6 publications

Deep learning based lithology classification of drill core images

Deep learning based lithology classification of drill core images

A Practical Method for the Automatic Recognition of Rock Structures in Panoramic Borehole Image during Deep-Hole Drilling Engineering

An Automatic Method for Discontinuity Recognition in Coal-Measure Strata Borehole Images

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