We have identified the lithologies from well logs and available core data through the cluster and neural network analysis in the Krishna-Godavari (KG) basin. The unsupervised hierarchical cluster analysis (HCA) has been used to find out the dissimilarity behaviour of pairwise well log data associated with each lithological unit as a measure of proximity. Whereas, supervised neural network analysis has been used for the identification of lithologies of un-cored portion of well using the wireline logs and associated lithologies identified from cores in the same or nearby well. Initially, the persistence behaviour of the wireline logs is confirmed by rescaled range (R/S) analysis. These log data follow a local trend of lithological variation and hence can be used for lithology identification. Subsequently, we have used HCA and multi-layer feed forward (MLF) neural network to envisage the lithological sequence of varying thickness (thick beds of the order of 6 m; thin beds up to 0.3048 m) through the analysis of gamma-ray, bulk density (RHOB), neutron porosity (Φ), sonic transit time (Δt) and photoelectric factor downhole logs. The results using the log data from the Expedition 02 of Indian National Gas Hydrates Program (NGHP-Exp.-02) demonstrate that these non-traditional approaches are suitable for analysing formation lithologies where core data associated with discriminating finer beds are available. The HCA and MLF network-predicted lithologies, made in this study, match realistically with the core derived lithologies, demonstrating their efficacy. Thus, the approach is quite useful for providing quick and accurate information on subsurface lithologies.

我们已经通过克里希纳-戈达瓦里（KG）盆地的聚类和神经网络分析，从测井和可用的岩心数据中识别了岩性。无监督分层聚类分析（HCA）已用于找出与每个岩性单元相关的成对测井数据的相似性行为，以此作为一种接近性度量。鉴于监督神经网络分析已用于通过使用测井曲线和从同一井或附近井的岩心中识别出的相关岩性来识别井中非岩心部分的岩性。最初，通过重新调整范围（R / S）来确认有线日志的持久性行为分析。这些测井数据遵循岩性变化的局部趋势，因此可用于岩性识别。随后，我们使用了HCA和多层前馈（MLF）神经网络，通过对伽玛射线的分析，设想了厚度各不相同的岩性序列（厚层为6 m；薄层为0.3048 m），堆密度（RHOB），中子孔隙率（Φ），声波传播时间（Δt）和光电系数井下测井曲线。使用来自印度国家天然气水合物计划Expedition 02（NGHP-Exp.-02）的日志数据得出的结果证明了这些非传统方法适用于分析与识别精细层相关的核心数据的地层岩性。该HCA和MLF网络预测岩性，在这项研究中提出，拥有核心衍生岩性现实相符，证明其有效性。因此，该方法对于提供有关地下岩性的快速而准确的信息非常有用。