Generalized mixture Markov chain Monte Carlo inversion for seismic fluid discrimination

ABSTRACT Fluid factor plays a crucial role in seismic fluid discrimination, but it is influenced by the complex petrophysical properties of reservoir rocks, which also leads to the statistical diversity of reservoir parameters. In this study, the commonly used Russell fluid factor is first investigated using the inclusion-based rock-physics theory. Rock-physics analysis reveals that the Russell fluid factor is constrained by lithology and a dry rock parameter that is difficult to determine for real reservoirs. To address these limitations, a new fluid factor is developed. Theoretical analysis and field data application illustrate its wide applicability. In addition, to reduce the prediction bias caused by the complex petrophysical properties, a generalized mixture prior considering the lithology contribution is developed. This innovative approach enables the characterization of the lithology-based probabilistic statistical distributions of different reservoir parameters. Field data tests, such as well-logging and seismic data, demonstrate the advantages of our approach, which is helpful in identifying the hydrocarbon-bearing zones in complex reservoirs.