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.

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The expansion and exploitation of mining resources are essential for social and economic growth. Remote sensing provides vital tools for surface-mining monitoring operations as well as for reclamation efforts in the central Salt Range of the Indus River Basin, Pakistan. This research demonstrates the applicability of remote sensing techniques to the coal mining monitoring scheme to allow for effective and efficient monitoring and to offset the adverse consequences of coal mining activities. Landsat 8 OLI images from June 2019 and 2020, and a Landsat 7 ETM+ image from June 2002, were used for this study. A three-phase methodology including Normalized Difference Vegetation Index (NDVI) analysis, land cover mapping, and change detection approaches was adopted. Image classification based on Tasseled Cap Transformation and the brightness temperature At-satellite using the K-means algorithm was implemented in a GIS program to identify seven land cover classes within the study area. The results show some level of surface disturbance to the landscape due to the coal mining reclamation activities that had taken place over the 18-year time period. From 2019 to 2020, about 3.622 km2 of coal mines or barren land were converted into bare agricultural land. Over the years, it was also observed that reclamation areas exhibited higher values of NDVI than coal mining areas. The mean NDVI for coal mining areas was 0.252 km2, and for areas of reclamation, it was 0.292 km2 in 2020, while in 2019, the value for coal mining sites was 0.133 km2, and 0.163 km2 for reclamation sites. This trend suggests that coal-mining operations can be monitored using satellite data, and the progress of reclamation efforts can be assessed using satellite NDVI data from the target locations. This study is beneficial to agencies responsible for monitoring land cover changes in a coal mine because it provides a cost-effective, efficient, and robust scientific tool for making mine site allocation decisions and for monitoring the progress of reclamation efforts.

This paper evaluated the oil and gas potential of the Cretaceous Yageliemu clastic reservoir within the Yakela condensed gas field lying in the Kuqa Depression, Tarim Basin, China. The petrophysical properties of the interest zones in the Kuqa area were characterized using geophysical logs from five wells. The results reveal that the gas-bearing zones are characterized by high resistivity, good permeability (K) and effective porosity (Φeff), low water saturation (Sw), and low shale concentration (Vsh), reflecting clean sand. The shale distribution model showed that these shales have no major influence on porosity and fluid saturation. The average shale volume, average effective porosity, and hydrocarbon saturation indicate that the Cretaceous Yageliemu Formation in the studied area contains prospective reservoir properties. The spatial distribution of petrophysical parameters, reservoir rock typing (RRT), and lithofacies were analyzed using the cross plots of litho saturation (volumetric analysis), iso-parametric representations of the petrophysical characteristics, cluster analysis, and self-organizing feature maps, respectively. The southeastern and northeastern regions of the research area should be ignored because of their high water and shale concentrations. The sediments in the southwest and northwest include the most potential reservoir intervals that should be considered for the future exploration and development of oil and gas fields in the study area.

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