An integrated approach for the identification of lithofacies and clay mineralogy through Neuro-Fuzzy, cross plot, and statistical analyses, from well log data

Abstract

Today, researchers face multiple challenges identifying clay mineral types and lithofacies from well log data. This research paper hopes to offer new insight into this particular challenge. Formation evaluation characteristics play a significant role in the exploration and production of future and current oil and gas fields. The proposed methodology in this study uses an integrated approach that includes: (1) numerical equations, (2) Neuro-Fuzzy neural networks, (3) cross plots, and (4) statistical analyses. This proposed integrated approach is capable of dramatically improving the accuracy of the results. Well logging data provide valuable information for identifying lithofacies, clay mineralogy types, as well as other important hydrocarbon reservoir characteristics. Talhar Shale in the Southern Lower Indus Basin, Pakistan, is composed of interbedded shale, sand, and shaly-sand, intervals that have been identified via the lithological interpretation process of well logs. Talhar Shale contains montmorillonite type clay with minor amounts of illite, glauconite, and various micas that can be easily identified by natural gamma ray absorption profiles, as well as through ratio logs, bulk density log, and photoelectric absorption index log. These interpretations can be further confirmed via cross plots and other statistical analyses. This approach consists of a comprehensive study of well logging data and thus can lend itself to be a helpful component in characterizing the hydrocarbon structures of the Talhar Shale.