Qun Zhao

High graphitization is responsible for low-resistivity shale development with poor reservoir quality. This paper provides an explanation of organic matter graphitization and determines the impact of high graphite content on low-resistivity shale reservoir quality at the Wufeng-Longmaxi Formation in the Southern Sichuan Basin. Fine veins are frequently developed at shale samples with Ro > 3.5%, graphitized organic matter > 25%, and resistivity < 5 Ω•m, which are dominated by three mineral assemblages: brunsvigite, barite-hyalophane-barium feldspar-potassium feldspar-anhydrite, and calcite-ankerite. These filling minerals are characterized by an Eu positive anomaly and high Ba, Fe, and Mn contents, suggesting that low-resistivity shale was modified by magmatic-related low-temperature hydrothermal fluid. Temperature measurements of brine inclusions and a semi-empirical geothermometer of chlorite show that low-temperature hydrothermal fluid experienced the chlorite stage (150–180 °C), the low-sulfidation stage (120–150 °C), and the low-temperature calcitization stage. Paleozoic fault systems and late Permian hydrothermal activities associated with the Emeishan mantle plume control the graphitization of low-resistivity shale. The water formation and seawater infiltrated into the deep crust along the Paleozoic basement faults under gravity, developing alkaline hot brine through mantle plume heating and then causing a water-rock reaction with basement rocks. They migrated upward along deep and large Paleozoic faults through convective thermal circulation in the Tiangongtang area, the Shuanglong-Luochang area, and the Xuyong area. Cation exchange and redox reactions occurred during the interaction between high-temperature hydrothermal fluid and cool wall rocks. The migration of alkaline hot brine via the Wufeng-Longmaxi shale introduced a subsequent water-rock reaction, resulting in the development of hydrothermal mineral assemblages that intricately filled fractures. It increased formation temperature and enhanced thermal maturity and graphitization of organic matter at the Wufeng-Longmaxi low-resistivity shale, resulting in a wide distribution of low-resistivity shale at the Changning Block.

At the Changning block and at the Luzhou block, the genetic mechanism of low-resistivity shale and its impact on reservoir quality are currently a hot topic on a world-wide scale. Shale with resistivity lower than 20 Ω·m is widely developed at the Wufeng-Longmaxi Formation in the Southern Sichuan Basin, bringing a considerable challenge for reservoir prediction using the electromagnetic method. This paper discusses the genetic mechanisms and reservoir qualities of three low-resistivity shale reservoir types in the Southern Sichuan Basin (the Changning block and Luzhou block). Three primary elements controlling low-resistivity shale distribution in the Southern Sichuan Basin have been deduced: widely distributed gravity flow deposits, poor structural preservation conditions and shale graphitization caused by Emeishan basalt. Specifically, (1) the shale reservoir with a resistivity <12 Ω·m was uniformly distributed with gravity flow deposits in the Southern Sichuan Basin. High clay mineral contents (especially illite) in gravity flow deposits increased cation exchange capacity and irreducible water saturation at shale reservoir, decreasing electrical resistivity. (2) The resistivity of the shale reservoir close to a complex fault-fracture zone was generally lower than 20 Ω·m, indicating that poor structural preservation conditions played an important role in the wide distribution of low-resistivity shale. The resistivity of the shale reservoir near NE-trending faults at the Changning block was significantly lower than that in other areas. (3) Emeishan basalt caused extensive shale graphitization at the west of the Changning block, which was limited at the Luzhou block. The shale resistivity at the Luzhou block was not affected by graphitization. Among three types of low-resistivity shale, type III was characterized by high quartz content, high TOC, high porosity, high gas content and low graphitization. Although the resistivity of type III is generally lower that 20 Ω·m, it is still a favorable exploration target in the Southern Sichuan Basin.

In recent years, big data and artificial intelligence technology have developed rapidly and are now widely used in fields of geophysics, well logging, and well test analysis in the exploration and development of oil and gas. The development of shale gas requires a large number of production wells, so big data and artificial intelligence technology have inherent advantages for evaluating the productivity of gas wells and analyzing the influencing factors for a whole development block. To this end, this paper combines the BP neural network algorithm with random probability analysis to establish a big data method for analyzing the influencing factors on the productivity of shale gas wells, using artificial intelligence and in-depth extraction of relevant information to reduce the unstable results from single-factor statistical analysis and the BP neural network. We have modeled and analyzed our model with a large amount of data. Under standard well conditions, the influences of geological and engineering factors on the productivity of a gas well can be converted to the same scale for comparison. This can more intuitively and quantitatively reflect the influences of different factors on gas well productivity. Taking 100 production wells in the Changning shale gas block as a case, random BP neural network analysis shows that maximum EUR can be obtained when a horizontal shale gas well has a fracture coefficient of 1.6, Type I reservoir of 18 m thick, optimal horizontal section of 1600 m long, and 20 fractured sections.