Reservoir type classification and water yield prediction based on petrophysical conversion models

In the Chaixi region of the Qaidam Basin’s Qigequan tectonic zone, the compact sandstones are characterized by their low porosity and permeability, featuring intricate pore-throat formations, varied lithologies, assorted clay minerals, and pronounced unevenness among the reservoirs. There’s a weak link between reservoir metrics and logging reactions, making it challenging to assess these reservoir parameters. The microscopic pore structure of the reservoir can be illustrated through both the nuclear magnetic resonance relaxation time distribution and the capillary pressure curve. By using fractal dimensions to classify the reservoir, a conversion model between the transverse relaxation time in nuclear magnetic resonance logging and the capillary pressure in the mercury injection curve is established, enabling the conversion of pseudo-capillary pressure curves. Key elements of the pseudo-capillary pressure curve, specifically discharge and drive pressure, median pressure, and sorting coefficient, were analyzed and integrated with the generalized regression neural network for accurate reservoir type classification. An efficient categorization of reservoir types was accomplished by isolating three key elements from the pseudo capillary pressure curve—displacement pressure, median pressure, and sorting coefficient—and integrating them with the generalized regression neural network. Utilizing a rock physics framework, a correlation between transverse relaxation time of nuclear magnetic resonance and relative permeability conversion was formulated to accurately forecast the rate of water generation in the reservoirs of the western Qaidam Basin. The anticipated outcomes demonstrated a strong link with the real rate of water production. This technique presents an innovative method to forecast the comparative permeability of oil-water stages and the rates of water generation in compact sandstone reservoirs.