Identification of lithofacies and prediction of mineral composition in shales – A case study of the Shahejie Formation in the Bozhong Sag

Abstract

The identification of shale lithofacies is the basic work of shale gas exploration and development. Accurate quantitative characterization of different mineral components in fine-grained mixed shale is of great significance for the identification and classification of lithofacies types, the enrichment conditions of shale oil and gas (hydrocarbon generation, reservoir, occurrence and preservation), and the evaluation of shale oil potential (reservoir, oil bearing, fracturability and oil mobility). When organic-rich argillaceous laminae are moderately mixed with brittle laminae, the laminated shale is not only the favorable interval of shale oil and gas enrichment for hydrocarbon generation (organic-rich argillaceous laminae) and storage (brittle laminae), but also the excellent “sweet spots” for continental shale oil and gas optimization. Different shale lithofacies have distinct rock texture, fabric and composition, leading to different brittleness and rock physical properties. For the identification of shale lithofacies containing various components, the overlaps of different wire-line logging responses and the vague boundaries between various logging data cause the large deviation in the logging prediction of lithofacies by traditional methods. In this study, shale samples from the lower part of the third member of Shahejie Formation in Bozhong Sag of the Bohai Bay Basin are selected to carry out the reservoir characterization and then the data mining of logging information by a Back Propagation (BP) neural network coupled with Atomic Search Optimization (ASO) algorithm. The BP algorithm based on the identified shale lithofacies (expected output) and logging data index (input) is used to train the neural network. The complex and unrecognized nonlinear relationship between shale lithofacies and logging data is mapped onto the high-dimensional identifiable nonlinear quantitative relationship to establish the prediction model of the relative content of clay minerals, silicate minerals and carbonate minerals. This study reveals the main lithologic characteristics of lacustrine shale lithofacies from Shahejie Formation in Bozhong Sag, and the main controlling factors for shale lithofacies prediction based on logging data. Our results show that the main mineral composition of shale lithofacies associations can be effectively predicted through the whole rock X-ray diffraction data, wire-line log data and neural network analysis, which provides the basis for lithofacies identification shale interval in well locations lacking core and test data. • Based on the optimized algorithm, a neural network prediction model of mineral composition is established to mine the hidden information in conventional logging data to predict lithofacies, and the accuracy is improved compared with the conventional neural network model. • The prediction of lithofacies mineral composition data for a wider range of non-coring wells by neural network provides more geological basis for the selection and prediction of favorable lithofacies zone - dessert area in subsequent exploration.