A lithofacies prediction method based on stochastic simulation

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