Using the theories of multi-field coupling within a multi-sphere interaction framework and the Whole Petroleum System, this study investigates the formation, distribution and enrichment of hydrocarbon resources, promoting a shift in exploration philosophy from a singular to an integrated approach. By integrating disciplines such as geochemistry, geodynamics and structural geology, it systematically analyzes the coupling effects of tectonic stress, thermal, pressure and fluid potential fields in sedimentary basins and their controlling mechanisms on hydrocarbon generation, migration and accumulation. Combined with typical case studies from various basins, the distribution patterns of conventional, tight and shale oil and gas are revealed. The results demonstrate that multi-sphere interactions govern the ordered distribution of different hydrocarbon types by influencing the accumulation process, thereby establishing a hydrocarbon accumulation model described as “Spheres control Fields, then Fields control Thresholds, and Thresholds define Distribution”. This theoretical framework aids in enhancing exploration efficiency and optimizing resource development strategies, providing novel insights and perspectives for future petroleum exploration.

With the advancement of unconventional oil and gas exploration and development, tight gravelly sandstone reservoirs have garnered increasing attention. In this study, triaxial compression experiments are conducted on gravelly sandstone cores, and correlation analysis is employed to establish the relationships between fractal dimensions of the fractures and rock mechanics parameters. For cores of gravelly sandstone, a positive correlation exists between the fractal dimension and the brittleness index. The prediction model reveals that the error between the predicted and actual values for well 3 is relatively large, which can be attributed to the presence of pure sandstone cores in well 3. Under high confining pressure in the deep strata, rocks exhibit a decreasing trend in fractal dimensions, a phenomenon due to the stress-memory effect. In addition, numerical simulation is further employed to study the effect of the factors that could affect the complexity of the fractures, and the results show that the fractal dimension of gravelly sandstone declines with increasing confining pressure, peak compressive strength, and rock elastic modulus as the loading process intensifies.

The brittleness of shales is critical to hydraulic fracturing since rock with high brittle minerals are more likely to fracture and maintain open fractures. Shale rocks have a wide range of constituting components, and different minerals display distinct elastic behavior. The microscale measurements of mechanical properties indicate that pyrite has the highest Young’s modulus, followed by quartz and feldspar. Organic matter was commonly recognized as the soft component, and has very low Young’s modulus. Alkaline minerals show similar Young’s modulus values to quartz and feldspar, and can be grouped into brittle minerals. The relative content, source and structure of brittle minerals can affect rock brittleness from multiple scales. Understanding the relationship between mineral compositions and geomechanical properties is beneficial for fracability estimation in engineering applications for shales.

The 2D NMR (T₁-T₂) mapping technique, which can be used to separate different proton populations from various sources (hydroxyls, solid organic matter, free water, and free HC) has gained attention in petroleum industry. To separate proton contributions, a fixed straight line is commonly employed to separate different regions representing proton sources on the map. However, some of these regions (Region 1 and 2) might overlap which makes extracting the NMR signal amplitude from these regions inaccurate. In order to solve this issue, in this study, we applied the Gaussian distribution deconvolution method to separate the T₁ and T₂ relaxation distributions and then derived the signal amplitude of each region instead of following the common fixed line approach. Next, we employed this method to analyze several shale samples from the literature and compared the results following both methods to verify our methodology. Finally, samples from the Bakken Shale were studied to separate signals from Region 1 and Region 2 and correlated the results with geochemical properties that were obtained from programmed (Rock Eval) pyrolysis. Results demonstrated an improvement in their relation when our approach is employed compared to the fixed line technique to differentiate signal from overlapping regions. This means the Gaussian distribution deconvolution method can be used with confidence to provide us with more accurate petrophysical and geochemical understanding of complex formations.