LAUSR

Due to the special petrophysical properties of tight reservoirs, such as poor connectivity and low porosity, conventional rock physics models show limitations. Based on an inclusion-based method, a new formula containing fluid pressure is derived without an equilibration assumption of fluid pressures in the inclusions. Then, the formula is simplified with an equivalent pore structure to yield a new fluid identification parameter, the inclusion-based effective fluid modulus (IEFM). By analysis, this fluid identification factor is quite sensitive to water saturation for different pore connectivity. A well-logging data test shows the superiority of the proposed model in identifying tight gas-bearing zones. Seismic data application also demonstrates the validity of the proposed model and the predicted results match well with the well-logging data. In fluid identification, two probabilistic estimation methods are used: Bayes posterior prediction framework is a combination of Bayes’ theory and a deterministic rock physics model; Bayes discriminant method is a statistical rock physics method. The proposed IEFM is a novel identification parameter for tight gas-bearing reservoirs, which can have many applications in the exploration of tight reservoirs.