Logging identification method of depositional facies in Sinian Dengying Formation of the Sichuan Basin

The sedimentary facies/microfacies, which can be correlated with well logs, determine reservoir quality and hydrocarbon productivity in carbonate rocks. The identification and evaluation of sedimentary facies/microfacies using well logs are very important in order to effectively guide the exploration and development of oil and gas. Previous carbonate facies/microfacies identification methods based on conventional well log data often exist multiple solutions. This paper presents a new method of facies/ microfacies identification based on core-conventional logs-electrical image log-geological model, and the method is applied in the fourth member of the Dengying Formation (Deng 4) in the Gaoshiti-Moxi area of the Sichuan Basin. Firstly, core data are used to calibrate different types of facies/microfacies, with the aim to systematically clarify the conventional and electrical image log responses for each type of facies/microfacies. Secondly, through the pair wise correlation analysis of conventional logs, GR, RT and CNL, are selected as sensitive curves to establish the microfacies discrimination criteria separately. Thirdly, five well logging response models and identification charts of facies/microfacies are established based on electrical image log. The sedimentary microfacies of 60 exploratory wells was analyzed individually through this method, and the microfacies maps of 4 layers of the Deng 4 Member were compiled, and the plane distribution of microfacies in the Gaoshiti-Moxi area of the Sichuan Basin was depicted. The comparative analysis of oil testing or production results of wells reveals three most favorable types of microfacies and they include algal psammitic shoal, algal agglutinate mound, and algal stromatolite mound, which provide a reliable technical support to the exploration, development and well deployment in the study area. (c) 2021 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/ 4.0/).