Characterization and simulation of conglomerate reservoirs using core data of Triassic Baikouquan Formation, Mahu Depression

The 1 Gt oilfield discovery solidified the Mahu oilfield as the world's largest conglomerate oil region, underscoring the exploration potential of these reservoirs. However, optimizing and selecting the target interval for hydraulic fracturing remains challenging due to the significant heterogeneity of the structure and composition of conglomerate reservoirs. This study addresses key gaps in understanding conglomerate reservoir characteristics and their impact on hydrocarbon production, focusing on the Baikouquan (T₁b) Formation (Fm) on the Mahu Depression's northern slope. It introduces a new classification to better manage these complexities. In contrast to other classification methods, the proposed approach incorporates key factors influencing hydraulic fracture (HF) propagation, including grain size, cementation, supporting forms, and gravel composition, the latter of which is introduced for the first time. Based on core and test results, the conglomerate reservoirs are categorized into two main groups-fan delta front and fan delta plain conglomerates-and further divided into eight lithofacies types. Fan delta front conglomerates are subdivided into four types: A-1 (tuff, metamorphic, and magmatic rocks-dominated gravel-supported cobble-to-boulder lithofacies), A-2 (tuff and magmatic rocks-dominated matrix-supported pebble-to-cobble lithofacies), A-3 (tuff-dominated matrix-supported granule-to-pebble lithofacies), and A-4 (tuff-dominated gravel-supported granule-to-pebble lithofacies). Fan delta plain conglomerates are further divided into four types: B-1 (tuff and magmatic rocks-dominated gravel-supported granule-to-pebble lithofacies), B-2 (tuff and sedimentary rocks-dominated gravel-supported pebble-to-cobble lithofacies), B-3 (tuff-dominated gravel-supported cobble-to-boulder lithofacies), and B-4 (tuff, magmatic, and sedimentary rocks-dominated matrix-supported pebble-to-cobble lithofacies). The novelty of this classification method lies in its integration of both geological and engineering perspectives, particularly in optimizing hydraulic fracturing strategies. The study evaluates lithofacies from geological factors such as bedding, composition, and poroperm characteristics, as well as engineering considerations like fracturing potential and flow capacity. The results reveal that certain lithofacies types correlate strongly with higher fracturing success, providing insights that can guide more efficient hydraulic fracturing practices. By addressing the challenge of heterogeneity of the structure and composition in conglomerate reservoirs, this study offers a comprehensive framework for selecting optimal target intervals for hydraulic fracturing, which can significantly enhance hydrocarbon exploration and production strategies. This approach is expected to be valuable for similar complex conglomerate reservoirs worldwide.