Electrofacies in gas shale from well log data via cluster analysis: A case study of the Perth Basin, Western Australia

Abstract

Identifying reservoir electrofacies has an important role in determining hydrocarbon bearing intervals. In this study, electrofacies of the Kockatea Formation in the Perth Basin were determined via cluster analysis. In this method, distance data were initially calculated and then connected spatially by using a linkage function. The dendrogram function was used to extract the cluster tree for formations over the study area. Input logs were sonic log (DT), gamma ray log (GR), resistivity log (IND), and spontaneous potential (SP). A total of 30 reservoir electrofacies were identified within this formation. Integrated geochemical and petrophysics data showed that zones with electrofacies 3, 4, 9, and 10 have potential for shale gas production. In addition, the results showed that cluster analysis is a precise, rapid, and cost-effective method for zoning reservoirs and determining electrofacies in hydrocarbon reservoirs.

Effects of the introduction of pre-treated wastewater in a shallow lake reed stand

“…This is a widely known multivariate classification method in which each case starts in a separate cluster and joins up to the other clusters as the linkage distance grows, until only one cluster remains [41]. This method has been successfully applied in hydrology [42][43][44][45], hydrogeology [46], geology [47][48][49][50][51], chemistry [52] and anthropology [53] to find similar and homogeneous groups of observations. The validity of the groupings was verified using linear discriminant analysis (LDA), which separates the observations with linear planes resulting in a percentage of correctly classified cases [54,55].…”

Evaluation and optimization of multi-lateral wells using MODFLOW unstructured grids

“…26; Email: mlux@mol.hu János Szanyi, Tivadar M. Tóth: University of Szeged, H-6722, Szeged, Egyetem u. [2][3][4][5][6] necessarily in the same plane [1]. This suggests that the variations of multi-lateral well patterns are only limited by technology and imagination.…”

“…The conceptual model is the same as described above. Since we equiangularly distributed the radial branches, branch number (2,4,6,8,10) We evaluated fishbone wells along the same principle as radially distributed multi-lateral wells but branch number and branch angle had to be evaluated separately.…”

“…With the continuous advancement in technology more and more sophisticated well patterns and geometries are possible to be implemented in practice [2]. Multi-lateral wells can also offer an alternative to hydraulic fracturing in certain areas of unconventional hydrocarbon production such as production from coal-beds and ductile shale layers where due to the ductile behaviour of rocks artificial fractures will close around the proppant and so well productivity will be reduced [3][4][5].…”

Machine Learning Methods for Identifying Composition of Uranium Deposits in Kazakhstan

“…[12], [13]. In geophysical tasks, the application of unsupervised learning methods is rather limited; some examples of use are self-organising maps [14], Vector quantization (VQ) [15], clusterization [16], Fuzzy classification [17], and Kernel Density Estimation (KDE) [18]. In such cases, the resulting clusters were compared with the rocks encountered in the deposit during the exploration phase.…”