Hierarchical reservoir lithofacies and acoustic impedance simulation: Application to an oil field in SW of Iran

Abstract

In this study, a hierarchical simulation algorithm was implemented to produce 3D models of lithofacies and acoustic impedance (AI) in an oil field in southwestern Iran. Hierarchical simulation is a powerful method that takes into account the spatial distribution and hard contact relationships of continuous variables within the categorical variables in reservoir characterization. To implement the hierarchical simulation algorithm in this study, pluri-Gaussian simulation was used to construct the layouts of one categorical variable (lithofacies domains) and turning bands simulation was used to produce one continuous variable (AI models) within each simulated domain. The dataset that is available to test the proposed method includes the lithofacies and AI logs of seven wells. For validation of the proposed model, one of wells was excluded from the initial dataset to check the accuracy of results. The algorithm was implemented on a gridded model to produce 50 realizations of 3D lithofacies and AI models. The statistical analyses of the produced models verified the capabilities of hierarchical simulation in reproducing the lithofacies domains and AI values related to each domain. The simulated AI models were compared with the seismic inversion model to corroborate the reliability of the results. In addition, to show the superiority of the hierarchical approach, the results were compared to the results of direct AI modeling in which the AI models were generated without considering any lithofacies data. The comparisons in the blind well indicated a high correlation between the modeled values and the true AI. The correlation coefficient between average AI models of the hierarchical approach and the true AI was 0.85. Whereas, this value was 0.75 for the average of directly simulated AI models and 0.74 for the seismic inversion, respectively. Histogram analysis also verified the capability of the hierarchical modeling in reproducing the statistical properties of the original data. The resulting AI model obtained using the method proposed in this study also showed a high compatibility with the deterministic seismic inversion cube.