A Novel Texture Extraction Method for the Sedimentary Structures’ Classification of Petroleum Imaging Logging

Gao, Haoqi; Wang, Huafeng; Feng, Zhou; Fu, Mingxia; Ma, Chennan; Pan, Haixia; Xu, Binshen; Li, Ning

doi:10.1007/978-981-10-3005-5_14

Cited by 15 publications

(1 citation statement)

References 9 publications

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“…It can accurately describe the sedimentary characteristics of reservoirs, and can clearly reflect shale, clastic, calcite crystal, porosity, fracture, suture line, bedding, and biological disturbance. It is also of interest for lithology and sedimentary facies research [5][6][7][8][9][10][11][12][13] . The structural characteristics of fractures, dissolution cavities, faults and folds can also be identified by resistivity imaging logging, and it is an effective tool for reservoir identification, well-side structure, and in-situ stress analysis [14,15] .…”

Section: Introductionmentioning

confidence: 99%

Valuable data extraction for resistivity imaging logging interpretation

Ren

Gong

Zhou

et al. 2020

Tinshhua Sci. Technol.

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Imaging logging has become a popular means of well logging because it can visually represent the lithologic and structural characteristics of strata. The manual interpretation of imaging logging is affected by the limitations of the naked eye and experiential factors. As a result, manual interpretation accuracy is low. Therefore, it is highly useful to develop effective automatic imaging logging interpretation by machine learning. Resistivity imaging logging is the most widely used technology for imaging logging. In this paper, we propose an automatic extraction procedure for the geological features in resistivity imaging logging images. This procedure is based on machine learning and achieves good results in practical applications. Acknowledging that the existence of valueless data significantly affects the recognition effect, we propose three strategies for the identification of valueless data based on binary classification. We compare the effect of the three strategies both on an experimental dataset and in a production environment, and find that the merging method is the best performing of the three strategies. It effectively identifies the valueless data in the well logging images, thus significantly improving the automatic recognition effect of geological features in resistivity logging images.

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