Data for: Natural Fracture System of the Cambro-Permian Wajid Group, Wadi Al-Dawasir, SW Saudi Arabia

This study investigates the regional and outcrop-scale fracture system within the Wajid Group exposures in Wadi Al-Dawasir, southwest Saudi Arabia. The Wajid Group is a Cambro-Permian siliciclastic succession that forms the main groundwater aquifers in the study area and is considered as a potential hydrocarbon reservoir in the Rub' Al-Khali Basin. The succession is composed of fluvial, marine, and glacial to glaciofluvial deposits. This study aims to characterize and model the fracture system within the Wajid Group in the Wadi Al-Dawasir area using satellite imagery and direct field measurements. A further study objective is to define the geological factors controlling the fracture distribution within the Wajid Group succession. Five sets of regional-scale fractures (lineaments) were defined: N000°, N015°, N035°, N075°, and N135°. In addition, five sets of the outcrop-scale fractures were delineated: N015°, N035°, N075°, N135°, and N165°. The N135°- and N035°-oriented fracture sets are predominant at the regional scale. At the outcrop scale, however, the N165°- and N075°-oriented fracture sets are predominant. The trace-length of the regional-scale fractures is distributed according to the negative exponential distribution. The fractures within the Wajid Group outcrops are vertical to sub-vertical extensional fractures (mode 1). Those fractures are open, however, and at some localities, they are sealed or coated with calcite or iron oxides. Fracture swarms with an orientation of N015° were also observed in the southeastern part of the study area. Hierarchical outcrop conceptual models of two fracture sets are proposed. The proposed models show that the regional-scale fractures are not influenced by stratigraphic or lithological variations. In contrast, the outcrop-scale fractures are controlled by the stratigraphic, lithological, and diagenetic variations in the fracture-hosting sandstone. The diagenetic characteristics (cementation and dissolution), bed thickness, and porosity of the fracture-hosting sandstone play a key role in the fracture distribution within the Wajid succession. The results of this study contribute to the understanding of groundwater flow behaviour within fractured aquifers in the study area and will help to enhance gas production from fractured hydrocarbon reservoirs in the Rub' Al-Khali Basin

Data for: Natural Fracture System of the Cambro-Permian Wajid Group, Wadi Al-Dawasir, SW Saudi Arabia

This study investigates the regional and outcrop-scale fracture system within the Wajid Group exposures in Wadi Al-Dawasir, southwest Saudi Arabia. The Wajid Group is a Cambro-Permian siliciclastic succession that forms the main groundwater aquifers in the study area and is considered as a potential hydrocarbon reservoir in the Rub' Al-Khali Basin. The succession is composed of fluvial, marine, and glacial to glaciofluvial deposits. This study aims to characterize and model the fracture system within the Wajid Group in the Wadi Al-Dawasir area using satellite imagery and direct field measurements. A further study objective is to define the geological factors controlling the fracture distribution within the Wajid Group succession. Five sets of regional-scale fractures (lineaments) were defined: N000°, N015°, N035°, N075°, and N135°. In addition, five sets of the outcrop-scale fractures were delineated: N015°, N035°, N075°, N135°, and N165°. The N135°- and N035°-oriented fracture sets are predominant at the regional scale. At the outcrop scale, however, the N165°- and N075°-oriented fracture sets are predominant. The trace-length of the regional-scale fractures is distributed according to the negative exponential distribution. The fractures within the Wajid Group outcrops are vertical to sub-vertical extensional fractures (mode 1). Those fractures are open, however, and at some localities, they are sealed or coated with calcite or iron oxides. Fracture swarms with an orientation of N015° were also observed in the southeastern part of the study area. Hierarchical outcrop conceptual models of two fracture sets are proposed. The proposed models show that the regional-scale fractures are not influenced by stratigraphic or lithological variations. In contrast, the outcrop-scale fractures are controlled by the stratigraphic, lithological, and diagenetic variations in the fracture-hosting sandstone. The diagenetic characteristics (cementation and dissolution), bed thickness, and porosity of the fracture-hosting sandstone play a key role in the fracture distribution within the Wajid succession. The results of this study contribute to the understanding of groundwater flow behaviour within fractured aquifers in the study area and will help to enhance gas production from fractured hydrocarbon reservoirs in the Rub' Al-Khali Basin.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Integrated Geological, Hydrogeological, and Geophysical Investigations of a Barchan Sand Dune in the Eastern Region of Saudi Arabia

In arid countries such as Saudi Arabia, aeolian sand often covers a large area of the country. Understanding the variations of sand properties in dunes, including grain size, sorting, mineral composition and water content, can be important for groundwater recharge, environmental, and construction applications. Earlier studies examined properties of sand dunes by collecting samples from the surface. This study aims to investigate variations of sand properties within a Barchan sand dune in the coastal area of Saudi Arabia, by collecting samples and measurements from two vertically drilled boreholes up to the ground water level; one drilled in the dune crest and another one in the limb. Representative samples were collected and analyzed for their texture parameters, water content, and mineralogy. Electrical resistivity survey data was also acquired to map water content variation in the dune limb, and for comparison with well bore data. The reported results show no vertical variations in grain size or sorting in the dune crest. In contrast, the upper 0.5 m of the dune limb shows a relatively poorer sorting than found in deeper parts of the dune. Laterally, no variations in minerology were observed between crest and limb sands while grain size tended to be slightly coarser in the dune limb compared to the crest. Regarding the water content, it was found to vary vertically, probably due to previous cycles of rainfall infiltration through the sand body. Such observed variation in water content is consistent with the measured resistivity profile which could clearly identify the water table and areas with higher water content. This study concludes that beyond the upper 0.5 m, the Barchan sand dune body can be treated as a homogeneous medium in terms of mineralogy and sorting while grain size increases slightly toward the limb side

Computational Machine Learning Approach for Flood Susceptibility Assessment Integrated with Remote Sensing and GIS Techniques from Jeddah, Saudi Arabia

Floods, one of the most common natural hazards globally, are challenging to anticipate and estimate accurately. This study aims to demonstrate the predictive ability of four ensemble algorithms for assessing flood risk. Bagging ensemble (BE), logistic model tree (LT), kernel support vector machine (k-SVM), and k-nearest neighbour (KNN) are the four algorithms used in this study for flood zoning in Jeddah City, Saudi Arabia. The 141 flood locations have been identified in the research area based on the interpretation of aerial photos, historical data, Google Earth, and field surveys. For this purpose, 14 continuous factors and different categorical are identified to examine their effect on flooding in the study area. The dependency analysis (DA) was used to analyse the strength of the predictors. The study comprises two different input variables combination (C1 and C2) based on the features sensitivity selection. The under-the-receiver operating characteristic curve (AUC) and root mean square error (RMSE) were utilised to determine the accuracy of a good forecast. The validation findings showed that BE-C1 performed best in terms of precision, accuracy, AUC, and specificity, as well as the lowest error (RMSE). The performance skills of the overall models proved reliable with a range of AUC (89–97%). The study can also be beneficial in flash flood forecasts and warning activity developed by the Jeddah flood disaster in Saudi Arabia

Integrated Hydrogeological, Hydrochemical, and Isotopic Assessment of Seawater Intrusion into Coastal Aquifers in Al-Qatif Area, Eastern Saudi Arabia

Seawater intrusion (SWI) is the main threat to fresh groundwater (GW) resources in coastal regions worldwide. Early identification and delineation of such threats can help decision-makers plan for suitable management measures to protect water resources for coastal communities. This study assesses seawater intrusion (SWI) and GW salinization of the shallow and deep coastal aquifers in the Al-Qatif area, in the eastern region of Saudi Arabia. Field hydrogeological and hydrochemical investigations coupled with laboratory-based hydrochemical and isotopic analyses (18O and 2H) were used in this integrated study. Hydrochemical facies diagrams, ionic ratio diagrams, and spatial distribution maps of GW physical and chemical parameters (EC, TDS, Cl−, Br−), and seawater fraction (fsw) were generated to depict the lateral extent of SWI. Hydrochemical facies diagrams were mainly used for GW salinization source identification. The results show that the shallow GW is of brackish and saline types with EC, TDS, Cl−, Br− concentration, and an increasing fsw trend seaward, indicating more influence of SWI on shallow GW wells located close to the shoreline. On the contrary, deep GW shows low fsw and EC, TDS, Cl−, and Br−, indicating less influence of SWI on GW chemistry. Moreover, the shallow GW is enriched in 18O and 2H isotopes compared with the deep GW, which reveals mixing with recent water. In conclusion, the reduction in GW abstraction in the central part of the study area raised the average GW level by three meters. Therefore, to protect the deep GW from SWI and salinity pollution, it is recommended to implement such management practices in the entire region. In addition, continuous monitoring of deep GW is recommended to provide decision-makers with sufficient data to plan for the protection of coastal freshwater resources

Geochemical and Spatial Distribution of Topsoil HMs Coupled with Modeling of Cr Using Chemometrics Intelligent Techniques: Case Study from Dammam Area, Saudi Arabia

Unconsolidated earthen surface materials can retain heavy metals originating from different sources. These metals are dangerous to humans as well as the immediate environment. This danger leads to the need to assess various geochemical conditions of the materials. In this study, the assessment of topsoil materials’ contamination with heavy metals (HMs) was conducted. The material’s representative spatial samples were taken from various sources: agricultural, industrial, and residential areas. The materials include topsoil, eolian deposits, and other unconsolidated earthen materials. The samples were analyzed using the ICP-OES. The obtained results based on the experimental procedure indicated that the average levels of the heavy metals were: As (1.21 ± 0.69 mg/kg), Ba (110.62 ± 262 mg/kg), Hg (0.08 ± 0.18 mg/kg), Pb (6.34 ± 14.55 mg/kg), Ni (8.95 ± 5.66 mg/kg), V (9.98 ± 6.08 mg/kg), Cd (1.18 ± 4.33 mg/kg), Cr (31.79 ± 37.9 mg/kg), Cu (6.76 ± 12.54 mg/kg), and Zn (23.44 ± 84.43 mg/kg). Subsequently, chemometrics modeling and a prediction of Cr concentration (mg/kg) were performed using three different modeling techniques, including two artificial intelligence (AI) techniques, namely, generalized neural network (GRNN) and Elman neural network (Elm NN) models, as well as a classical multivariate statistical technique (MST). The results indicated that the AI-based models have a superior ability in estimating the Cr concentration (mg/kg) than MST, whereby GRNN can enhance the performance of MST up to 94.6% in the validation step. The concentration levels of most metals were found to be within the acceptable range. The findings indicate that AI-based models are cost-effective and efficient tools for trace metal estimations from soil