Natural Resources Research最新文献

The exploration level of tight sandstone gas reservoirs in the Denglouku Formation (DF) in the Xujiaweizi Fault Depression is low, and hydrocarbon source and accumulation process remain unclear. Through the analysis of natural gas geochemistry, X-ray diffraction, thin section observation, scanning electron microscope, physical property testing, cathodoluminescence, fluid inclusions, and basin simulation, we examine source of hydrocarbon, the period of hydrocarbon accumulation, and the evolution of physical properties of the tight sandstone gas in the DF. Additionally, the formation process of tight sandstone gas reservoirs in the DF is discussed. The results reveal the following: (1) the tight sandstone gas in the DF is categorized as III kerogen cracking gas, primarily sourced from dark mudstone of the second member of the DF, with some contribution from dark mudstone of the Shahezi Formation. (2) The tight sandstone in the DF is in stage B of middle diagenesis. Based on apparent compaction rate, apparent cementation rate, and apparent dissolution rate, it can be divided into four diagenetic facies: (1) strong compaction–medium cementation–weak dissolution facies; (2) medium compaction–strong cementation–medium dissolution facies; (3) medium compaction–strong cementation–weak dissolution facies; and (4) medium compaction–medium cementation–medium dissolution facies. The predominant diagenetic facies are the medium compaction–strong cementation–medium dissolution facies and medium compaction–medium cementation–medium dissolution facies. (3) The hydrocarbon charging period of the DF in study area ranged from 98 to 67.5 Ma. This period is earlier than the tight period of the strong compaction–medium cementation–weak dissolution facies sandstone and later than the tight period of the medium compaction–strong cementation–medium dissolution facies, medium compaction–strong cementation–weak dissolution facies, and medium compaction–medium cementation–medium dissolution facies sandstone. Consequently, two types of tight sandstone gas reservoirs exist in study area: (1) tight sandstone gas reservoir with tight first and then accumulation type and (2) composite tight sandstone gas reservoirs. Our research offers theoretical instruction for exploration of deep tight sandstone gas in northern Songliao Basin.

In China, significant progress has been made in the exploration and development of coalbed methane (CBM) in medium- to high-rank coals. However, the exploration and development potential of CBM in low-rank coals in the Zhalainuoer coalfield is unknown. In this study, various testing methods were utilized, including low temperature N₂/CO₂ adsorption, field emission scanning electron microscopy, and nuclear magnetic resonance, to investigate the pore structure characteristics of low-rank coals in the Zhalainuoer coalfield, so as to further evaluate the occurrence space of CBM therein. The results revealed that the prevalent pore types in the low-rank coals were “ink bottle” shaped pores and semi-closed pores, and micropores provide the main specific surface area (SSA) and total pore volume (TPV). Moreover, there is no significant correlation between vitrinite content and fractal dimension, while pore SSA and TPV are correlated positively with D₁ but negatively with D₂. The coalification degree significantly impacts the pore characteristics of the coal reservoirs. With coalification degree increasing, the SSA and TPV of micropores and transition pores generally exhibited a pattern of initially decreasing and then increasing. These research findings establish a theoretical foundation for the exploration and development of CBM in the Zhalainuoer coalfield.

Data-driven prospectivity modeling based on deep learning, particularly supervised learning, has demonstrated outstanding performance for mineral exploration targeting in the past years, thanks to its powerful feature learning ability. However, this approach necessitates a substantial amount of large, high-quality labeled training data, and the scarcity of known mineral deposits poses significant challenges in constructing a high-performance mineral prospectivity prediction model. Self-supervised contrastive learning can alleviate this problem by exploiting large amounts of readily available unlabeled data. In this study, we utilized geochemical element data from the Malanyu district to train a self-supervised contrastive learning model. This model was then employed to predict gold mineral prospectivity, and its accuracy was compared with supervised learning method. The results show that the self-supervised contrastive learning model has higher performance in prospectivity prediction than the supervised learning model and its recognition accuracy reaches 100.00%, which is 7.41% higher than that of the supervised learning model ResNet50 and 14.81% higher than that of the supervised learning model MobileNetV2. At the same time, the prediction results of gold prospecting have a strong consistency with the known gold deposits in this district. This study demonstrates the feasibility of applying the self-supervised comparative learning model to the prediction of gold prospects, and it is of great significance to realize intelligent prediction of mineral resources.

Hydrothermal disseminated gold mineralization in the Sanshandao gold belt, Jiaodong Peninsula, China, is closely associated with regional NE–NNE fault zones. To investigate the structural controls on this mineralization, we conducted 3D numerical modeling of coupled heat transport, tectonic deformation, and fluid flow, of which two sets of models, designed simple models and actual models, were involved. The simple models were used to examine how general fault geometries (fault bend length, fault bend angle, and fault dip) influenced dilation (positive volume strain) and fluid flow and further influenced hydrothermal mineralization. In contrast, actual modeling was carried out to further understand the structural controls and mineralization localization in a specific geological condition at Sanshandao. Following this, numerical simulation experiments with variable paleo-stresses on these two models were carried out in FLAC^3D platform. The simulation results of the simple models showed that long fault bend lengths, large absolute fault bend angles, and large changes in fault dip were more likely to promote dilation in the fault zone. The dilation zones are related to the small intersection angle of maximum principal stress and fault dip. The simulation results of the actual model illustrate that the gold mineralization distribution at Sanshandao was controlled by the coupling of fault strike–dip bends. Specifically, the discontinuous mineralization in the vertical direction was caused by local fluid focusing due to fault dip changes, particularly where the bend length was long. In addition, the oblique orientation of ore shooting depended on the variable strain orientations relative to the fault, which appeared to be fault strike variations. The results further determined the NNW–SSE-directed compression as the paleo-stress regime at Sanshandao during the ore-forming period. Our data also illustrated the deep fluid flow pathways in the Sanshandao gold belt and the Xinli S–SSE deep and the Sanshandao and Beibuhaiyu E–NE deep areas deserve to be the focus of the next gold exploration.

In the present study, the Soil and Water Assessment Tool was applied to determine the impacts of changing Land Use and Land Cover (LULC), Geophysical Fluid Dynamics Laboratory – Earth System Model Version 2, and Representative Concentration Pathways (RCP) 8.5 climate scenario on the monthly streamflow in the Subarnarekha basin of India. The results showed increased flow due to a reduction in agricultural area, a rise in built-up area, and a reduction in water bodies due to LULC change. In addition, lower annual precipitation and increased projected temperature were observed under RCP8.5. Although annual precipitation is decreasing, some components of the water balance are slightly increasing. From 2013 to 2020, surface flow increased by 98.85 mm and water yield decreased by 13.33 mm. However, in the climate change scenario, surface flow increased by 142.85 mm. Water yield decreased by 21.88 mm, lateral flow slightly decreased by 7.06 mm, and a further significant decrease 68.37% was noted in groundwater flow. The downward trend in groundwater flow is a serious concern, and therefore, more surface water storage structures must be planned to increase groundwater recharge and capture the increased surface flow. The model performance was statistically tested for NSE (Nash–Sutcliffe efficiency), R², and PBIAS (percent bias). During the calibration period and validation stages, NSE, R², and PBIAS were found to be 0.72, 0.83, and − 15.20%, and 0.85, 0.82, and − 27%, respectively, with the 2013 LULC map. The decreased monthly water availability and declining trend of winter rainfall need to be taken care of while planning the cropping pattern of the basin.

Liquid nitrogen (LN₂) fracturing has various advantages, such as low reservoir damage, minimal environmental impact, and excellent permeability. In this study, the cracking pattern and damage evolution characteristics of bedded coal subjected to LN₂ fracturing were investigated. The deterioration features of the mechanical parameters and failure mechanisms were examined in a comparable manner using Brazilian splitting tests. Additionally, the damage characteristics of bedded coal during LN₂ fracturing were explored. The results indicated that LN₂ cooling promoted the development of thermal cracks, consequently reducing the effective bearing capacity of the coal. Randomly distributed thermal cracks actively contributed to macroscopic crack propagation, increasing the proportion of shear cracks and the complexity of the fracture surface. Different bedding angles led to distinct failure modes, significantly impacting the proportion of shear cracks and the fracture surface complexity. Moreover, the bedding planes constantly influenced the propagation direction of the fracturing cracks, resulting in a macroscopic damage zone that expanded preferentially at the weak bedding planes with the borehole at the center. With increasing bedding angles, both the degree and rate of damage of coal decreased sequentially. Consequently, it was feasible to employ LN₂ fracturing in low-permeability reservoirs along the bedding planes, facilitating swift and efficient reservoir fracturing.

Coalbed gas pressure and content are fundamental parameters for mine gas recovery and disaster prevention. In response to the lengthy measurement cycles and low accuracy of existing models, this research proposes a new model for determining coalbed gas pressure and content based on image analysis. Utilizing dual-threshold edge detection and dynamic cycle extraction algorithms, a desorption image database was developed, enabling rapid inversion of gas pressure/content through an enhanced image similarity calculation method and cycle comparison algorithm. Field experiments demonstrate the high accuracy of the image analysis model in determining gas pressure/content, controlling the absolute error of gas pressure below 0.08 MPa and maintaining relative errors of 2.27–8.05%; for gas content, the absolute errors range 0.105–0.674 ml/g, with relative errors of 1.32–8.21%. Compared to previous desorption models, the image analysis model improves accuracy by 6.30% and reduces the measurement time to within 1.5 h, thus facilitating rapid and precise determination of coalbed gas pressure/content. Furthermore, by applying image recognition principles, this study delves into the critical points and significant change areas of the desorption rate curve, providing new insights into gas desorption behavior and expanding the application potential of image analysis technology in coalbed methane recovery.

The uncertainty inherent in three-dimensional (3D) mineral prospectivity mapping (MPM) encompasses (a) mineral system conceptual model uncertainty stemming from geological conceptual frameworks, (b) aleatoric uncertainty, attributable to the variability and noise due to multi-source geoscience datasets collection and processing, as well as 3D geological modeling process, and (c) epistemic uncertainty due to predictive algorithm modeling. Quantifying the uncertainty of 3D MPM is a prerequisite for accepting predictive models in exploration. Previous MPM studies were centered on addressing the mineral system conceptual model uncertainty. To the best of our knowledge, few studies quantified the aleatoric and epistemic uncertainties of 3D MPM. This study proposes a novel uncertainty-quantification machine learning framework to qualify aleatoric and epistemic uncertainties in 3D MPM by the uncertainty-quantification random forest. Another innovation of this framework is utility of the accuracy–rejection curve to provide a quantitative uncertainty threshold for exploration target delineation. The Bayesian hyperparameter optimization tunes the hyperparameters of the uncertainty-quantification random forest automatically. The case study of 3D MPM for exploration target delineation in the Wulong gold district of China demonstrated the practicality of our framework. The aleatoric uncertainty of the 3D MPM indicates that the 3D Early Cretaceous dyke model is the main source of this uncertainty. The 3D exploration targets delineated by the uncertainty-quantification machine learning framework can benefit subsurface gold exploration in the study area.

The most crucial elements in the oil and gas sector are predicting subsurface lithofacies utilizing geophysical logs for reservoir characterization and sweet spot assessment procedures. Nevertheless, accurately predicting payable lithofacies in a complex heterogeneous geological setting, such as the lower goru formation, poses considerable difficulty because conventional methods fall short in delivering highly accurate outcomes. Hence, this research proposes an advanced cost and time-saving data intelligence strategy using multiple classifiers to predict lithofacies with maximum accuracy that will aid in sweet spot evaluation in oil and gas fields globally. Geophysical log data of five wells from a mature gas field were used. The targeted reservoir formation was classified into seven facies types. We evaluated the performance of seven different models: support vector machine (SVM), K-nearest neighbors (KNN), random forest (RF), decision tree (DTr), naive Bayes (NB), adaptive boosting (AB), and ensemble (an integrated SVM, KNN, RF, and DTr classifier). RF and ensemble classifiers predicted the lithofacies with accuracies of 97.5 and 97.3%, respectively. Their efficacy in lithofacies prediction with high accuracy renders them as valuable tools in the domain of sweet spot evaluation. The proposed digital intelligence strategy could help operators identify drilling sites based on in-depth reservoir characterizations.

Coal is a crucial energy source globally, but it poses environmental challenges due to high temperatures and harmful missions during combustion. This study investigates bituminous coal's oxidation combustion in low-oxygen environments using thermogravimetry and differential thermogravimetry tests. We explore the thermal behavior and kinetic properties of three coal samples during combustion. Our findings reveal that, as oxygen concentration decreases, the combined combustion index of the coal samples also decreases during the oxygen-absorption stage. Additionally, the apparent activation energy of coal increases with its conversion rate (temperature). We observe a shift in the reaction mechanism from three-dimensional dissipation mode to two-dimensional as the oxygen concentration decreases. Notably, the activation energy initially rises and then decreases with increasing conversion (temperature) during the pyrolysis combustion stage, with a shortened phase of increased activation energy at lower oxygen concentrations. Furthermore, the kinetic mechanism transitions from stochastic nucleation and growth to one-dimensional phase-boundary mode with decreasing oxygen concentration. These insights enhance our understanding of coal oxidation combustion in low-oxygen environments, contributing to strategies for mitigating coal spontaneous combustion.