Automatic Well Log Analysis Across Priobskoe Field Using Machine Learning Methods (Russian)

Belozerov, Boris; Bukhanov, N.; Egorov, Dmitry V.; Закиров, А. И.; Osmonalieva, Oksana Taalaevna; Golitsyna, Maria; Reshytko, Alexander; Semenikhin, Artyom; Shindin, Evgeny; Lipets, Vladimir

doi:10.2118/191604-18rptc-ru

Cited by 12 publications

(1 citation statement)

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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

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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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