Identification and modeling of the hydrocarbon-bearing Ghar sand using seismic attributes, wireline logs and core information, a case study on Asmari Formation in Hendijan Field, southwest part of Iran

The prolific Oligocene-Miocene Asmari Formation is the primary reservoir rock hosting important hydrocarbon resources in Iran and consists of fluvial/deltaic siliciclastic and shallow marine carbonate rocks in the Hendijan Field. Due to the significant facies variability of the formation, the presence and quality of the reservoir pose a significant uncertainty in the characterization of the reservoir. This study compares two facies models, one based on well logs only and the second based on estimated facies volumes as a secondary variable in facies modelling. The petrophysical evaluation with microscopic thin sections and electrofacies analysis were used to classify the facies and determine the reservoir quality. As a result, the Ghar Member was identified as a highly porous interval, while the lower part of the Asmari Formation is characterized by tight facies. A sequential Gaussian simulation (SIS) algorithm was used to build the 3D facies model on the basis of the well logs. Acoustic impedance, shear impedance and density (derived from pre-stack inversion) were used as inputs to an artificial neural network to generate acoustic and density log volumes. Using electrofacies cut-offs, facies volume was constructed and used as a secondary variable to improve the initial facies model. The final facies model was compared with the blind well to check the validity of the prediction and satisfactory results were obtained. Since the values are present in all the cells of the reservoir (the traditional facies model only has values for the well location), the estimated facies volume is an accurate variable in the prediction of the facies model for the Asmari reservoir and for this reason the secondary facies model is more reliable than the primary one.

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Reservoir Quality Prediction of Gas-Bearing Carbonate Sediments in the Qadirpur Field: Insights from Advanced Machine Learning Approaches of SOM and Cluster Analysis

Rashid

Luo

Ashraf

et al. 2022

Minerals

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The detailed reservoir characterization was examined for the Central Indus Basin (CIB), Pakistan, across Qadirpur Field Eocene rock units. Various petrophysical parameters were analyzed with the integration of various cross-plots, complex water saturation, shale volume, effective porosity, total porosity, hydrocarbon saturation, neutron porosity and sonic concepts, gas effects, and lithology. In total, 8–14% of high effective porosity and 45–62% of hydrocarbon saturation are superbly found in the reservoirs of the Eocene. The Sui Upper Limestone is one of the poorest reservoirs among all these reservoirs. However, this reservoir has few intervals of rich hydrocarbons with highly effective porosity values. The shale volume ranges from 30 to 43%. The reservoir is filled with effective and total porosities along with secondary porosities. Fracture–vuggy, chalky, and intracrystalline reservoirs are the main contributors of porosity. The reservoirs produce hydrocarbon without water and gas-emitting carbonates with an irreducible water saturation rate of 38–55%. In order to evaluate lithotypes, including axial changes in reservoir characterization, self-organizing maps, isoparametersetric maps of the petrophysical parameters, and litho-saturation cross-plots were constructed. Estimating the petrophysical parameters of gas wells and understanding reservoir prospects were both feasible with the methods employed in this study, and could be applied in the Central Indus Basin and anywhere else with comparable basins.

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Identification and modeling of the hydrocarbon-bearing Ghar sand using seismic attributes, wireline logs and core information, a case study on Asmari Formation in Hendijan Field, southwest part of Iran

Cited by 3 publications

References 51 publications

Reservoir characterization reimagined: a hybrid neural network approach for direct three-dimensional petrophysical property characterization

Reservoir characterization reimagined: a hybrid neural network approach for direct three-dimensional petrophysical property characterization

Improving the 3D facies model with the seismic-derived log volumes: a case study from the Asmari Formation in the Hendijan Field, southwest Iran

Reservoir Quality Prediction of Gas-Bearing Carbonate Sediments in the Qadirpur Field: Insights from Advanced Machine Learning Approaches of SOM and Cluster Analysis

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