A lithofacies prediction method based on stochastic simulation

Abstract

AbstractLithofacies prediction can facilitate the search for hydrocarbon reservoirs and is, therefore, crucial for seismic exploration. We proposed a new approach for lithofacies prediction based on stochastic simulation of pseudo-wells. First, we performed a pseudo-well simulation using a continuous-time Markov chain (CTMC), which can accurately describe geological deposition and can be created using logging, core or geological data. We used the CTMC to randomly generate many pseudo-wells with different lithofacies columns. The corresponding reservoir and elasticity parameters for these lithofacies columns were determined using the Monte Carlo random sampling method. Second, we used seismic modeling and seismic matching to predict the lithofacies. We applied the elastic parameters for each pseudo-well to calculate the reflection coefficients and performed seismic modeling to generate synthetic seismic data. Thereafter, we used a weighted matching function based on prior geological constraints to match the synthetic seismic data with actual seismic data. We predicted the lithofacies based on numerous pseudo-wells with the best fit and tested the influencing factors of the method using model data. Finally, we applied this method to the field data, and the predicted lithofacies agreed well with the wells. Compared to conventional methods, the proposed method can use prior information in the log and the geology data, and detects thin layers better.