Nowadays, there are significant issues in the classification of lithofacies and the identification of rock types in particular. Zamzama gas field demonstrates the complex nature of lithofacies due to the heterogeneous nature of the reservoir formation, while it is quite challenging to identify the lithofacies. Using our machine learning approach and cluster analysis, we can not only resolve these difficulties, but also minimize their time-consuming aspects and provide an accurate result even when the user is inexperienced. To constrain accurate reservoir models, rock type identification is a critical step in reservoir characterization. Many empirical and statistical methodologies have been established based on the effect of rock type on reservoir performance. Only well-logged data are provided, and no cores are sampled. Given these circumstances, and the fact that traditional methods such as regression are intractable, we have chosen to apply three strategies: (1) using a self-organizing map (SOM) to arrange depth intervals with similar facies into clusters; (2) clustering to split various facies into specific zones; and (3) the cluster analysis technique is used to identify rock type. In the Zamzama gas field, SOM and cluster analysis techniques discovered four group of facies, each of which was internally comparable in petrophysical properties but distinct from the others. Gamma Ray (GR), Effective Porosity(eff), Permeability (Perm) and Water Saturation (Sw) are used to generate these results. The findings and behavior of four facies shows that facies-01 and facies-02 have good characteristics for acting as gas-bearing sediments, whereas facies-03 and facies-04 are non-reservoir sediments. The outcomes of this study stated that facies-01 is an excellent rock-type zone in the reservoir of the Zamzama gas field.
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.
Future gas field development and prospect assessment depend on accurate reservoir parameter characterization. The understanding of the tight sand distribution of the Shanxi and Taiyuan Formations within the Hangjinqi area is ambiguous due to the presence of coal and mudstone lithofacies, high heterogeneity, and poor resolution of the seismic data. Thus, it is difficult to determine the reservoir’s thickness. To cope with this challenge, we have employed the advanced method of constrained sparse spike inversion (CSSI) utilizing 3D seismic and nine wells for the distinction of tight sandstone facies from the coal and mudstone facies. Results of petrophysical analysis of studied well J54 show that the coal and mudstone facies are dominant towards the T9c horizon, whereas tight sandstone facies are present towards the T9d horizon. The obtained findings show that the CSSI accurately identified the spatial distribution of sand-ratio in the zone of interest (ZOI) that lies between the T9c and T9d horizons. The acoustic impedance (AI) of coal shows the lowest AI values, whereas the tight sandstone shows the highest AI values. The tight sandstone facies shows moderate values of AI in the range of 8.5 × 106 kg/m2s to 1.20 × 107 kg/m2s. The impedance map of T9c suggested the presence of coal and mud facies, whereas the T9d impedance map suggest the presence of maximum tight sandstone facies. The sand-ratio map of T9d showed maximum reservoir thickness that ranges from 0.65-0-95, whereas the sand-ratio value mostly ranges from 0 to 0.5 on the T9c map. The maximum sand-ratio values on T9d show that the lower Shanxi Formation has good reservoir characteristics. Whereas, due to the presence of coal and mudstone in the Taiyuan Formation, the T9c map shows low values of sand-ratio. The maximum sand-ratio regions within the targeted T9c-T9d layers should be exploited for future gas explorations.
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