Lithofacies Types and Physical Characteristics of Organic-Rich Shale in the Wufeng–Longmaxi Formation, Xichang Basin, China

Abstract

The shale of the Upper Ordovician Wufeng Formation and the Lower Silurian Longmaxi Formation in the Xichang Basin is the main replacement horizon for the shale gas exploration being conducted in the Sichuan Province, except for the Sichuan Basin. The fine identification and classification of the types of shale facies are important for shale gas exploration and development evaluation. However, the lack of systematic experimental studies on rock physical characteristics and micro-pore structures leads to a lack of physical evidence for the comprehensive prediction of shale sweet spots. Therefore, the present study used different means, such as core observation, total organic carbon content (TOC), helium porosity measurement, X-ray diffraction analysis, and mechanical properties analysis, in combination with the analysis of the whole rock mineral composition and characteristics of shale, for the identification and classification of the lithofacies of the shale layer, the systematic petrology and hardness measurement of the shale samples with different lithofacies, and discussion of the dynamic and static elastic properties of the shale samples and the control factors. It was revealed that nine types of lithofacies existed in the Wufeng Formation the Long11 sub-member in the Xichang Basin, among which moderate organic carbon content–siliceous shale facies, moderate organic carbon content–mixed shale facies, and high-organic carbon content–siliceous shale facies were the best lithofacies with the optimum reservoir conditions, providing sufficient space for shale gas accumulation. The siliceous shale facies mainly developed organic pores and fractures, and the pore texture was excellent overall. The mixed shale facies mainly developed intergranular pores and mold pores, with a preference toward pore texture. The argillaceous shale facies mainly developed dissolution pores and interlayer fractures, and the pore texture was relatively poor. The geochemical characteristics of the organic-rich shale samples with TOC > 3.5% revealed that the sample was composed of microcrystalline quartz grains as the rock support framework, while the intergranular pores were located between the rigid quartz grains, which exhibited hard pores in the analysis of their mechanical properties. In the relatively organic-poor shale samples with TOC < 3.5%, the quartz source was mainly terrigenous clastic quartz, and the sample was composed of plastic clay minerals as the rock support skeleton, while the intergranular pores were located between argillaceous particles, which exhibited soft pores in the analysis of their mechanical properties. The difference in the rock fabric of the shale samples resulted in an ″initial increase followed by a decrease″ trend of velocity with quartz content, with the organic-rich shale samples exhibiting low velocity–porosity and velocity–organic matter content change rate, and the two kinds of rocks were easier to distinguish in the correlation diagram of the combined elastic parameters such as the P-wave impedanc...

On the Pore Geometry and Structure Rock Typing

“…Samples in each class seem indistinguishable due to similar geological depositions and diagenetic alterations . Many methods have addressed rock typing, for example: Based on mechanical properties, mineralogy, and organic geochemistry The use of permeability, porosity, and irreducible water saturation data either empirically ,− or with a hydraulic flow unit (HFU) approach; ,, Involvement of capillary pressure data and J -function − and combined with radius; Consideration of thin section descriptions and interpretations such as rock fabrics, depositional facies, and rock textures; , Geostatistics and machine learning implementation such as clustering, , ANN, self-organizing map, , and fuzzy logic; Grouping based on the dimensionless form of absolute permeability, relative permeability, porosity, and phase viscosity, the so-called true effective mobility TEM function; , The use of resistivity data and porosity to yield in electrical flow unit; Further development of analytical models, such as the pore geometry and structure (PGS) method. − …”