Logging identification for diagenetic facies of tight sandstone reservoirs: A case study in the Lower Jurassic Ahe Formation, Kuqa Depression of Tarim Basin

The Ahe Formation is one of the significant hydrocarbon units in the Kuqa Depression of Tarim Basin, which is characterized by poor reservoir quality and strong heterogeneities due to the diagenetic effects. Thus, ‘diagenetic facies’ are introduced to explore high-quality reservoir layers. According to the reservoir spaces, main diagenetic minerals and diagenesis, four types of diagenetic facies are categorized: tightly compacted facies, dissolution facies, carbonated cemented facies and microfracture facies. Petrophysical characteristics of different diagenetic facies are summarized, which shows that the dissolution facies and microfracture facies have higher porosities, permeabilities and bigger pore radii. Logging characteristics indicate that logging values of four diagenetic facies are overlapped seriously, making it impossible to classify them by original logging crossplot. Principal component analysis (PCA), linear discriminant analysis (LDA) and back propagation neural networks (BPNN) are used to build diagenetic facies identification models, and the former two belong to the linear statistical algorithms, while the latter is a nonlinear algorithm. By comparing the identification accuracy of these three algorithms, principal components (PCs) crossplot cannot recognize diagenetic facies effectively. LDA can achieve automatic identification by establishing discriminant functions, but its identification accuracy (71.7%) is too low to satisfy the exploration requirements. However, the trained BPNN yield 80% accuracy, which is more advantageous than PCA and LDA. The blind test shows that the predicted results by trained BPNN have a very high coincidence with the thin section analysis and the logging interpretation results. Therefore, the BPNN as provided in this paper can be effectively used for diagenetic facies identification based on conventional well logs.