Arabian Journal of Geosciences最新文献

During the shale oil development process in the Mahu Sag, there is a significant difference in the initial crude oil production after soaking, which is considered to be related to the soak. Therefore, 7 wells in the research area were selected to study the relationship between various parameters in soaking and initial crude oil production under similar control engineering parameters. Selecting parameters with strong correlation to establish a multiple linear regression equation with initial crude oil production and predict the initial crude oil production of Well M57. The results indicate that the initial oil production is negatively correlated with pressure drop rate, porosity, and soaking time. Establishing a multiple linear regression equation as Y =  − 127.1823A—5.20913B + 1.35608C + 73.81473 and predicting initial oil production of well M57 is 16.58 m³/d. Based on the relationship between pressure drop rate and soak time, the pressure curve is divided into four phases. The reasonable soak time is between the lower and upper limits. The favorable soak time for initial crude oil production is between the II and III stage. This study fills the gap in predicting the initial production of shale crude oil in Mahu Sag and can provide guidance for the efficient development of shale oil in the region in the later stage.

This paper presents a comprehensive study on predicting bulk density in missed intervals of the Nubian reservoir in the Sirt Basin, Libya, leveraging both empirical and machine learning methodologies. Bulk density is one of the most significant and crucial parameters for rock physics modeling, geomechanical analysis, and reservoir characterization; however, this measurement is not present in all intervals of the Nubian reservoir in the Sirt Basin to predict an accurate, reliable prediction and save cost. Empirical equations such as Gardner, Lindseth, and Khandelwal models, alongside machine learning algorithms including random forest (RF), multi-layer perceptron (MLP), and support vector machine (SVM), are employed using conventional logs that were collected from four vertical wells. The data set undergoes a pre-processing step before being divided into 50%, 20%, and 30% for training, testing, and validation, respectively. The optimization is performed using the Grid search CV function. Based on the findings, using machine learning rather than empirical models to predict bulk density is more effective. The machine learning model achieves a higher correlation coefficient above 0.89 and lower mean absolute error than the empirical approaches. Conclusively, a predicted bulk density by supervised machine learning approaches can be used as a reference in all intervals that lack the density log.

This paper presents the results of a comprehensive analysis of five gamma ray logs from the offshore western Niger Delta, Nigeria. The investigation was aimed at using gamma ray logs to estimate the shale volume, identify lithology, correlate between formations, and establish the comparative rate of accretion of deposits of the five studied wells. Five lithologies were recognized, namely, sandy mudstone, shale/mudstone, fine-medium grained sandstone, argillaceous sandstone, and coarse-grained sandstone. The lithology thicknesses varied down the profile of the studied wells due to the discrepancy in the subsidence with erosion and onlap of sediments. The shale volume (Vsh) investigation revealed low Vsh values for argillaceous, fine-medium, and coarse-grained sandstones and high values for shale/mudstone and sandy mudstone lithologies. However, lithologies are discriminated into three types of formation, namely, clean formation (Vsh < 10%), shaly formation (Vsh ranged from 10 to 30%), and shale formation (Vsh is more than 33%). These differentiated lithologies consist of laminated, structural, and dispersed shale, respectively. It is shown that when the Vsh values increase, the Vsh/lithological thickness ratios increase, and when the Vsh/lithology thickness ratios increase, the lithology thicknesses decrease. The graphic correlation revealed that for 1 m of sediment accretion in MOX4 well, 0.10 m, 0.30 m, 0.31 m, and 0.42 m of rocks were accumulated in MOX1, MOX2, MOX3, and MOX5 wells, correspondingly. These comparative rates of sedimentary particle accumulation proved that there were either fewer erosion occurrences or extra accommodation gaps within MOX4 than in MOX1, MOX2, MOX3, and MOX5 wells.

The northern region of Ethiopia, particularly the Dichinama locality, holds significant potential for marble resources. Saba Dimension Stone Private Limited Company is currently mining at three key locations in this region: Lidge, Ela-wedizare, and Dugub. However, the company faced challenges with the existing mine layout, which resulted in decreased production capacity. To address this, a comprehensive research initiative was conducted to assess the current mine design and develop a new open-pit layout. The study included geological mapping, rock characterization, sampling, laboratory testing, and geotechnical evaluations, alongside an in-depth analysis of geological formations such as joints, faults, and shear zones. Geotechnical techniques, including rock mass rating (RMR), rock quality designation (RQD), and slope mass rating (SMR), were utilized to inform the development of the new mine design. The findings from the laboratory tests and geotechnical analysis were applied to create a revised mine layout. The new open-pit design has led to significant improvements in production and recovery rates. Specifically, production volumes are projected to increase from 9976 to 12,470 m3 at Ela-wedizare, from 36,150 to 45,187.5 m3 at Lidge, and from 4620 to 5775 m3 at Dugub. Recovery rates are also expected to improve, from 20.5 to 25.6% at Ela-wedizare, from 23.4 to 29.3% at Lidge, and from 35.7 to 44.6% at Dugub. This research underscores the importance of advanced geological and geotechnical approaches in optimizing mining procedures and improving operational efficiency in the Dichinama marble resource region. The implementation of the new design has helped Saba Dimension Stone Private Limited Company overcome prior obstacles, achieving substantial gains in both productivity and recovery.

Rock slope stability, having a plane mode of failure, can be assessed by different methods. The traditional analytical approaches used in the analysis are limited to those in which the upper slope surface is horizontal and the tension crack is inclined, and generally imply the resolution of nonlinear equations which require an exhaustive calculation. The aim of this study is to develop a systematic analytical solution for estimating the safety factor of a rock slope with an inclined upper surface. By employing the basic assumptions of the limit equilibrium method, simplified expressions considering the nonlinear Barton-Bandis and linear Mohr–Coulomb failure criteria were proposed to analyze the stability of a slope with no tension cracks and sliding on a planar failure surface. Furthermore, some other expressions for the normal stress, length of the planar failure line, and self-weight of the block masses are presented. Finally, the relationships between the derived closed-form solutions and some main parameters, such as the height, cohesion, total unit weight, internal friction angle, slope face angle, failure plane angle, basic friction angle, joint roughness coefficient, and joint compressive strength and upper surface angle, are illustrated with typical examples. These results are in good agreement with practical case studies in literature and numerical simulation results. This method can be effectively utilized in rock and soil slope engineering to provide a reference for preventing and controlling planar slope failure.

This research focuses on characterizing the Late Miocene Qawasim Formation in the El Basant Gas Field, located in the Eastern part of the East Nile Delta onshore, 12.5 km south of El Manzala Lake, covering approximately 30.66 km². By integrating well-log analysis from five wells and seismic data interpretation from 30 2D seismic lines, this study identifies opportunities for enhancing our understanding of the subsurface geology, reservoir characterization, and reservoir potential of the El Basant Gas Field. The study area is characterized by step normal faults oriented northeast-southwest and northwest-southeast. Lithological analysis reveals abundant shale and sandstone facies, indicating significant gas potential. Petrophysical analysis confirms the Qawasim Formation as the primary reservoir, with a net pay thickness of about 23 m, effective porosity of around 34%, and hydrocarbon saturation of about 52%. A detailed 3D model assessed the influence of structural setting, facies, and petrophysical properties on the Qawasim Formation. Integrating these findings, two highly prospective locations were identified, with estimated Gas Initial In Place (GIIP) values of approximately 29.563 million cubic feet.

Grain size parameters and major, trace, and rare earth elements of beach sands along the west Atlantic coast of Cameroon between the coastal basins and the Cameroon Volcanic Line were evaluated to decipher their source, weathering, depositional conditions, textural maturity, and tectonic settings with the application of new formulae. Grain size analysis shows that most of the sediments are fine to medium-grained, moderately sorted to well sorted, and deposited in moderate to low energy environments with clear fluvial and beach signatures. Log(Fe₂O₃/K₂O) vs log (SiO₂/Al₂O₃) indicates that most of the sediments are iron sands while trace element plots such as 10*Th/Co, Cr/Th, 100*Th/Sc, Th/Yb, and 10*La/Co indicate mafic to intermediate sources for most of the sediments. This is further confirmed by rare earth elements (REE) normalized patterns revealing enriched light rare earth elements (LREEs) over heavy rare earth elements (HREEs) by moderate amounts, positive Eu anomalies from Post Archean Australian Shale (PAAS), and upper continental crust (UCC) normalized plots. Newly developed indices CIX and PIX, from elimination of CaO, suggest moderate to high weathering intensity which conforms with the climatic conditions of Cameroon. La/Yb chondrite normalized vs Zr plots positioned a significant portion of the sediments in a mantle environment, with minimal zircon enrichment. The volcaniclastic sands were identified as first cycle, non-recycled sediments. High ICV values (ICV > 1) indicate that most of the sands are immature, but the newly introduced ICV_new, whereby oxides of Fe and Ca are eliminated, shows that some of the sediments are mature and recycled, confirmed also by SiO₂/Al₂O₃ ratios especially at the tail ends of the study area. DF(A-P)M and DF (A-P)MT plots indicate that the sediments are of the passive margin setting.

This study examines rainfall patterns in Central Tunisia’s highlands using 60 years of data from 21 stations. By analyzing both monthly and yearly rainfall data, the research investigates how precipitation varies across space and time. Interestingly, a “downward approach” was used, starting with simpler analyses of yearly data, and progressing to incorporate more detailed monthly information. The analysis revealed that while most areas saw an increase in the intensity of rainfall events, some low-altitude semi-arid areas experienced more frequent dry spells. This suggests a potential rise in drought risk for these specific locations. Additionally, the study highlights significant spatial variability in rainfall patterns across the region, with a decrease from east to west and north to south. This irregularity may impact land use and hinder planning strategies in these regions.

The pre-installation assessment criteria for solar energy parks have been simulated through a variety of machine learning algorithms, with predictors categorized into three different climatic time frames (present, 2050, and 2070 bio-climatic time frames) and four distinct Socio-Economic Emission Scenarios, namely, RCPs 2.6, 4.5, 6.0, and 8.5, which represent projections for future levels of radiative forcing and greenhouse gas emissions W/m². A promising new location identification was speedily achieved through the development of an ensemble distribution model using a machine learning algorithm. The total capacity (in MW) and covered area of 78 different solar parks across India from various agro-climatic zones were examined (Sq. KM). Predictions about the future viability of existing solar parks are made in this study, and the best places for new ones are suggested. It was found that 2.08% of India’s total land area, or 68,369.69 sq. km, is optimum for solar parks, given the existing climatic, solar, and land cover characteristics. Across the board, the optimal locations were increased for RCPs 2.6 (3.87% of India’s total land area), 4.5 (2.72%), and 8.5 (4.47%) by 2050. Upward trends were similarly observed in the RCP 2.6 (3.40) and RCP 6.0 (2.27%) for 2070. Solar parks are considered ideal in the western half of the country, while more moderate locations are expected to emerge in the west, south-west, and central India.