{"title":"A new method based on multiresolution graph-based clustering for lithofacies analysis of well logging","authors":"","doi":"10.1007/s10596-024-10277-y","DOIUrl":null,"url":null,"abstract":"<h3>Abstract</h3> <p>The lithofacies analysis of logging data is an essential step in reservoir evaluation. Multiresolution graph-based clustering (MRGC) is a commonly used methodology that provides information on the best number of clusters and cluster fitting results for geological understanding. However, the cluster fusion approach of MRGC often leads to an overemphasis of the boundary constraints among clusters. MRGC neglects the global cluster distribution relationship, which limits its practical application effectiveness. This paper proposes a new methodology, named kernel multiresolution graph-based clustering (KMRGC), to improve the merging part of clustering in MRGC, and it can give more weight to the spatial relationship characteristics among clusters. The clustering performance of K-means, Gaussian Mixture Model(GMM), fuzzy c-means(FCM), Density-Based Spatial Clustering of Applications with Noise(DBSCN), spectral clustering, MRGC and KMRGC algorithm was evaluated on a publicly available training set and noisy dataset, and the best results in terms of the adjusted Rand coefficients and normalized mutual information(NMI) coefficients on most of the datasets were obtained using KMRGC algorithm. Finally, KMRGC was used for logging data lithofacies clustering in cased wells, and the clustering effect of KMRGC algorithm was much better than that of the K-means, GMM, FCM, DBSCN, spectral clustering and MRGC algorithms, and the accuracy and stability were better.</p>","PeriodicalId":10662,"journal":{"name":"Computational Geosciences","volume":"162 1","pages":""},"PeriodicalIF":2.1000,"publicationDate":"2024-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Geosciences","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1007/s10596-024-10277-y","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
The lithofacies analysis of logging data is an essential step in reservoir evaluation. Multiresolution graph-based clustering (MRGC) is a commonly used methodology that provides information on the best number of clusters and cluster fitting results for geological understanding. However, the cluster fusion approach of MRGC often leads to an overemphasis of the boundary constraints among clusters. MRGC neglects the global cluster distribution relationship, which limits its practical application effectiveness. This paper proposes a new methodology, named kernel multiresolution graph-based clustering (KMRGC), to improve the merging part of clustering in MRGC, and it can give more weight to the spatial relationship characteristics among clusters. The clustering performance of K-means, Gaussian Mixture Model(GMM), fuzzy c-means(FCM), Density-Based Spatial Clustering of Applications with Noise(DBSCN), spectral clustering, MRGC and KMRGC algorithm was evaluated on a publicly available training set and noisy dataset, and the best results in terms of the adjusted Rand coefficients and normalized mutual information(NMI) coefficients on most of the datasets were obtained using KMRGC algorithm. Finally, KMRGC was used for logging data lithofacies clustering in cased wells, and the clustering effect of KMRGC algorithm was much better than that of the K-means, GMM, FCM, DBSCN, spectral clustering and MRGC algorithms, and the accuracy and stability were better.
期刊介绍：
Computational Geosciences publishes high quality papers on mathematical modeling, simulation, numerical analysis, and other computational aspects of the geosciences. In particular the journal is focused on advanced numerical methods for the simulation of subsurface flow and transport, and associated aspects such as discretization, gridding, upscaling, optimization, data assimilation, uncertainty assessment, and high performance parallel and grid computing.
Papers treating similar topics but with applications to other fields in the geosciences, such as geomechanics, geophysics, oceanography, or meteorology, will also be considered.
The journal provides a platform for interaction and multidisciplinary collaboration among diverse scientific groups, from both academia and industry, which share an interest in developing mathematical models and efficient algorithms for solving them, such as mathematicians, engineers, chemists, physicists, and geoscientists.
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