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In this study, we present a procedure for reservoir property modeling in a channelized carbonate reservoir based on hierarchical geostatistical simulation and seismic data integration. Because soft data integration in facies modeling has always been challenging, we used an innovative approach to incorporate seismic data in facies simulations properly. In this regard, we produced a facies proportion model (FPM) using sequential Gaussian co-simulation of facies proportion as primary data and acoustic impedance as secondary data. The facies proportions were extracted from vertical proportion curves, and the impact of seismic data in facies simulation was determined with the help of correlation coefficient maps. An alternative type of seismic-based soft data was also derived using a supervised neural network to create a facies probability cube (FPC) for each facies. Afterward, pluri-Gaussian simulation (PGS) was implemented to these two prepared soft datasets, and consequently, porosity was simulated twice in each of the models—with and without seismic-derived secondary data. Histogram analysis showed that the facies modeled with the PGS–FPM method reproduced the original well data better than the PGS–FPC method. In addition, blind wells validation showed that the PGS–FPM outputs had up to 79% accuracy, while channel geometries were better constructed using the PGS–FPC approach. The difference between the reservoir quality of channel and background was distinguishable in all hierarchical simulated porosity results. At the same time, the predicted results from simulated porosity in PGS–FPM facies had stronger correlation with true values in blind wells. [ABSTRACT FROM AUTHOR]

Facies modeling of Albian-Turonian successions at Tang-e-Bijar gas field in the Lurestan Province, NW of the Zagros region, Iran is the main objective of this study. The sedimentary environment and biozones of the Sarvak and Garau formations (Albian-Turonian time) are different in Lurestan Province when compared with the south Zagros and the Persian Gulf ranges. Synchronous successions of the formations with different lithofacies and laterally changing of biofacies are always controversial. The primary objectives of this paper is to determine the concurrency of the Sarvak and Garau successions using the correlation of foraminifera biozones and subsequent modeling of simultaneous lithofacies via employing the Truncated Gaussian Simulation with a trend (TGSim) algorithm. In this regard, the Tang-e-Bijar and nearby field data such as core data and petrophysical logs, paleontological and stratigraphy studies were taken into consideration. The stratigraphic ranges of the two formations were determined as Albian-Turonian age. Based on these biostratigraphic data, the tongue of the Garau Formation was clarified. The sequence stratigraphy zonation has been used for the entire Tang-e-Bijar field wells, based on Integrated Prediction Error Filter Analysis (INPEFA) logs and core data. In order to capture the heterogeneity of deep sea lithofacies, MRGC clustering method with supervising lithofacies was implemented in this study and five electrofacies were generated and coded from 1 to 5. They correspond to Rudist-Oncoids, packstone to grainstone (RO), Orbitolinid peloid packstone (OP), Fine Orbitolinid wackestone to packstone (FO), Bioturbated echinoderm mudstone to wackestone (BM), and Oligosteginid Mudstone (OM) lithofacies, respectively. The facies modeling in the reservoir such as Tang-e-Bijar where synchronous successions and laterally transient heterogeneous has occurred has always been challenging. To address this challenge, a new workflow has been implemented in this paper. • Facies Modeling of Albian-Turonian successions in Lurestan province at the northwest of the Zagros fold-thrust belt, Iran is the main objective of this study. • Multi Resolution Graph Based Clustering (MRGC) techniques have been used for identifying electrofacies in reservoirs. • Sequence stratigraphy and age determination have been done based INPEFA log and the Zagros biozonation. • The Truncated Gaussian with Trend method has been used to distribute facies parameters. [ABSTRACT FROM AUTHOR]