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.

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.

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.

Porosity assessment is a vital component for reservoir evaluation in the oil and gas sector, and with technological advancement, reliance on conventional methods has decreased. In this regard, this research aims to reduce reliance on well logging, purposing successive machine learning (ML) techniques for precise porosity measurement. So, this research examines the prediction of the porosity curves in the Sui main and Sui upper limestone reservoir, utilizing ML approaches such as an artificial neural networks (ANN) and fuzzy logic (FL). Thus, the input dataset of this research includes gamma ray (GR), neutron porosity (NPHI), density (RHOB), and sonic (DT) logs amongst five drilled wells located in the Qadirpur gas field. The ANN model was trained using the backpropagation algorithm. For the FL model, ten bins were utilized, and Gaussian-shaped membership functions were chosen for ideal correspondence with the geophysical log dataset. The closeness of fit (C-fit) values for the ANN ranged from 91% to 98%, while the FL model exhibited variability from 90% to 95% throughout the wells. In addition, a similar dataset was used to evaluate multiple linear regression (MLR) for comparative analysis. The ANN and FL models achieved robust performance as compared to MLR, with R² values of 0.955 (FL) and 0.988 (ANN) compared to 0.94 (MLR). The outcomes indicate that FL and ANN exceed MLR in predicting the porosity curve. Moreover, the significant R² values and lowest root mean square error (RMSE) values support the potency of these advanced approaches. This research emphasizes the authenticity of FL and ANN in predicting the porosity curve. Thus, these techniques not only enhance natural resource exploitation within the region but also hold broader potential for worldwide applications in reservoir assessment.

Investigating drinking water quality is crucial for public health, as clean water access is a fundamental requirement for a healthy life. To comprehensively assess Karachi’s drinking water quality, 152 water samples were systematically collected from five districts: Malir, Karachi West, Karachi East, Karachi South, and Karachi Central. The investigation involved analyzing various physicochemical and microbiological parameters in comparison to WHO 2011 guidelines. Additionally, integrated techniques like principal component analysis and water quality index computation offer insights into both potable and non-potable water aspects, with a focus on health-related well-being. Elevated levels of turbidity and chloride were identified across all five districts, with the residual chloride surpassing permissible limits in Karachi West and Karachi Central. Coliform and E. coli levels exhibited significant variations, with the highest mean values recorded in Karachi Central and the lowest in Karachi East. The overall analysis revealed that only 16.67%, 38.71%, 50%, 43.33%, and 58.06% of the water supply is suitable for drinking in Malir, Karachi West, Karachi East, Karachi South, and Karachi Central, respectively, while 83.33%, 61.29%, 50%, 56.67%, and 41.94% are unsuitable for drinking, posing substantial health risks. Urgent interventions in water quality management and public health are imperative to mitigate these risks associated with substandard drinking water.