Natural Resources Research最新文献

The deterioration of coal strength caused by geological conditions of high gas in deep mines and disturbance from mining operations is one of the elements that influence the incidence of dynamic disasters like gas outbursts and rock bursts. To study how gas pressure and cyclic loads interact to determine the mechanisms and phenomena of coal dynamics, the split Hopkinson pressure bar apparatus was used to perform cyclic impact test on coal samples to investigate the mechanical behavior of gas-bearing coal samples under cyclic dynamic load and gas pressures. The findings indicated that there are three stages in the stress–strain evolution of gas-bearing coal: linear elastic stage, plastic stage, and post-peak stress attenuation. As cycle time grows, the peak stress and attenuation stress of the coal samples decrease, while the maximum and peak strains exhibit a general increasing trend. Under the impact of dynamic load, the macroscopic damage form of the coal sample is mainly a macroscopic crack, and the microscopic examination revealed that the coal samples interior crystal was primarily a trans-granular fracture. By considering dynamic load, gas pressure, and number of cycles, the test results can be more accurately verified by the mechanical damage constitutive model. Finally, based on cyclic dynamic load and gas pressure, the proposed fatigue prediction model of gas-bearing coal can better anticipate coal samples dynamic load-bearing capability.

Optimizing hydrocarbon recovery in the Illizi Basin requires precise reservoir characterization. Traditional methods face challenges in efficiently handling large datasets from multiple wells. This paper employs principal components analysis (PCA) to evaluate the petrophysical properties of the reservoir intervals (IV-3, IV-1b, IV-1a) using wells P8, P4, and P6, situated in the northern, center, and south of our reservoir, respectively. PCA reduced the dimensionality of the data, while preserving original information, facilitating the analysis of the reservoir's geological and sedimentological features. The results showed that unit IV-3 has the highest average porosity (average NET porosity) and the lowest average water saturation (average PAY log sw) across all wells, indicating significant hydrocarbon production potential. In contrast, units IV-1b and IV-1a exhibited higher water saturations, suggesting less favorable conditions for hydrocarbon extraction. Strong negative correlations between petrophysical properties and water saturation in unit IV-3 highlighted its potential for hydrocarbon production. PCA correlation circles illustrated these relationships, with unit IV-3 showing predominantly hydrocarbon saturation, Unit IV-1b exhibited mixed saturation, whereas unit IV-1a was characterized by high water saturation. These findings demonstrate the effectiveness of PCA in guiding hydrocarbon resource management and exploitation strategies in the Illizi Basin; therefore, we recommend prioritizing drilling in zones with optimal reservoir properties, as identified through PCA. These zones are likely to have higher porosity, permeability, and lower water saturation, we also recommend Considering implementing suitable enhanced oil recovery techniques, such as waterflooding, polymer flooding, or gas injection, to improve recovery factors, especially in low-permeability zones. Finally, we recommend implementing a robust monitoring system to track reservoir performance and adjust production strategies as needed. This may involve real-time monitoring of pressure, temperature, and flow rates. These recommendations, can significantly enhance hydrocarbon recovery from unit IV-3, maximizing economic benefits, while minimizing environmental impact. This study demonstrates the practical application of PCA in reservoir characterization and provides valuable insights for optimizing field development and production strategies in the Illizi Basin.

Mineral prospectivity mapping (MPM) can be deemed a binary classification task, with classifiers trained and validated on labels indicating the presence or absence of the targeted mineralized zones. Using economically viable mineral deposits as positive labels could, in theory, yield prospectivity models with geometallurgical reliability, thereby aiding land management and decision-making. The inherent scarcity of economically viable deposits, however, ultimately affects MPM products. The positive class label, therefore, often requires augmentation with either mineral occurrences (i.e., mineralized sites lacking economic viability) or synthetically generated labels. This paper examines how augmented positive labels and different negative label selection procedures geospatially represent economically viable mineral deposits and affect deep learning-based MPM’s classification performance and its spatial selectivity (i.e., MPM’s capability to efficiently narrow the exploration search space). To achieve this objective, large ensembles of deep learning classifiers were trained and validated with diverse combinations of positive and negative labels. Two positive class label sets were created by augmenting mineral deposits with either synthetic labels, generated using generative adversarial networks, or mineral occurrences, paired with distinct negative label sets selected based on (1) locations distant from known mineral deposits, (2) areas geospatially dissimilar to known mineral deposits, and (3) mineralized areas unrelated to the targeted style of mineralization, resulting in six unique class configurations. This study ultimately provides insights into how different label sets affect MPM's classification performance and spatial selectivity. The results indicate that selecting negative class labels from geospatially different localities enhances classification performance and MPM's spatial selectivity compared to other negative label selection procedures.

Machine learning (ML) is increasingly being used in geosciences for complex classification tasks. Mica minerals are commonly found in deposits of precious metals, rare metals, and rare earth elements, including tungsten, tin, lithium, and copper, among others. These minerals can provide insights into the formation environment and age of various deposits. While ML has been applied mainly for optical recognition and compositional analysis of mica, its use for classification of deposit types and mineralization types remains underexplored. This study aimed to fill this gap by developing a stacking multi-classification model, which integrates multiple ML algorithms, and logistic regression as the meta-model. Trained with a dataset of 3479 and 4005 mica major element compositions, both models achieved 0.99 accuracy on the test set. Precision, recall, and F1-scores were all reported at 0.99, indicating excellent classification performance. Feature importance analysis revealed that elements such as F, MgO, FeO, MnO, and Al₂O₃ are crucial for classification, reflecting distinct geological conditions across different types of ore deposits. Copper and gold deposits typically form around 700 °C under high oxygen fugacity and low fluorine fugacity, while W and Sn deposits form in the temperature range of 600–700 °C with varying oxygen fugacity. Lithium and beryllium deposits form at temperatures ranging 500–650 °C, exhibiting moderate oxygen fugacity and a wide range of fluorine fugacity. This paper presents a robust model for classifying deposit types and mineralization types based on mica composition and emphasizes the strong link between ML outcomes and geological characteristics.

Shale gas reserves represent a significant source of natural gas, but unlocking their full potential depends on effective hydraulic fracturing. This research investigates the application of machine learning (ML) techniques to predict fracability index (FI), offering a faster and more cost-effective alternative to traditional experimental methods. Focusing on the Upper Ordovician Wufeng to Lower Silurian Longmaxi Formation in the Weiyuan shale gas field, Sichuan Basin, China, this study employed deep neural networks that integrate two metaheuristic algorithms—genetic algorithm (GA) and particle swarm optimization (PSO)—with the back-propagation technique. These combined algorithms—termed GABPNN and PSOBPNN—were utilized to predict the FI. Model performance was assessed using three metrics: R², RMSE, and MAE. The GABPNN achieved R², RMSE, and MAE of 0.97531, 0.024754, and 0.0042875, respectively, while the PSOBPNN yielded values of 0.97494, 0.024938, and 0.0048962, respectively. Notably, when predicting FI values for the test well, the PSOBPNN model attained a R² of 0.99848, and the GABPNN model achieved a R² of 0.9993, indicating exceptional predictive accuracy. Both models demonstrated nearly perfect prediction accuracy for FI in the testing dataset, underscored by their high R² values. Importantly, the GABPNN model exhibited superior capability in mitigating overfitting, a common challenge in ML applications. Overall, the GABPNN and PSOBPNN models offer effective alternatives for assessing the fracability of shale gas reservoirs. By facilitating the identification of sweet spots for fracturing, these ML-based approaches have the potential to optimize operations in shale gas reservoirs.

Handling the micro-occurrence mechanisms of fluids is vital for the exploitation of shale gas. As the research hotspots shift towards the deep strata, the gas storage and transport capacity in shale relies to a great extent on the nanostructure. In this work, the grand canonical Monte Carlo and molecular dynamics simulations were performed to systematically study the adsorption and diffusion behaviors of water and methane in illite pores of marine shale. We aimed at providing a molecule-level insight into the thermodynamic and kinetic properties of fluids. The results demonstrate that water molecules tend to form two adsorption layers on each side of the illite surface in micropores. Specifically, the adsorbates are preferentially distributed between K+ and adsorbed above the tetrahedral silicon oxide layer through the hydrogen bonds. With the addition of methane in the system, the second adsorption layers of water disappear. Meanwhile, the density of free water at the pore center decreases and displays some small fluctuations. The variation in burial depth is mainly manifested by the controlling effects of temperature on the fluids. In general, it is manifested as a decrease in the adsorption capacity and an increase in the diffusion ability under the deep geological conditions. In this paper, the molecular dynamics simulation is shown to be an efficient and effective tool to further improve microscopic theory of the gas–water enrichment in shale nanopores.

Fine-grained carbonate-siliciclastic mixed sequences, formed in saline lacustrine settings, constitute substantial unconventional and conventional hydrocarbon resources. Clarifying the key controlling factors of hydrocarbon accumulation is pivotal for predicting potential resources and enhancing exploration strategies. However, there is still a lack of research on how hydrocarbons accumulate in fine-grained carbonate-siliciclastic mixed sequences. Here, we present integrated research on a unique saline lacustrine petroleum system with fine-grained mixed deposits in the northwestern Qaidam Basin based on geochemical analysis, reservoir data, fluid inclusion analysis, and basin modeling. The saline lacustrine source rocks have low organic abundance, with type II–III organic matter. The high content of soluble organic matter and large thickness of saline lacustrine source rock provided sufficient hydrocarbon for the petroleum system. The reservoir rocks exhibit unusual mixed characteristics of carbonate and siliceous minerals. Dissolution and microfracture development are critical for the formation of high-quality reservoirs. Hydrocarbon charging began during the Middle Miocene, and initially, it occurred in those areas where early traps were formed. By comparison, hydrocarbon began to charge late traps during the Late Miocene or Pliocene. The crucial controlling factors of hydrocarbon accumulation in the saline lacustrine basin include (1) adequate hydrocarbon supply, (2) high-quality fine-grained mixed reservoirs, (3) favorable source–reservoir–caprock assemblage, (4) many anticlinal traps generated by tectonic movements in the central lacustrine basin, and (5) suitable matching relationship of geological elements. This research also established hydrocarbon accumulation models of early trap and late trap to promote future exploration. This research provides new insights into a saline lacustrine petroleum system, which may serve as an efficient template for other saline lacustrine basins worldwide to promote future petroleum exploration.

The Eocene Cave Peak intrusive complex in western Texas was the site of a major exploration program in the late 1960s, principally for Mo in a porphyry mineral system. Although neither Texas nor the US federal government have protocols for the archiving of data and materials resulting from private sector exploration programs, much of the Cave Peak exploration results has been preserved through a fortuitous series of events. This information was utilized to construct modern 3D geological and mineralization models, serving as an example of the opportunities and challenges of working with legacy data. In addition to Mo and Cu, the Cave Peak system is enriched in the critical raw materials Nb, W, Sn, REE, and F. Despite the limitations and uncertainties of geological and resource models constructed from incomplete and problematic legacy information, such models may serve to accelerate new exploration and evaluation activities for diverse targets in similar geologic terranes. This information may provide an invaluable starting point for current assessments of the US critical mineral resources toward supply chain security.

Lithium-rich (Li-rich) sedimentary brine has emerged as a valuable unconventional resource, driven by the blooming global market, advancements in direct extraction technologies, and a lower environmental impact compared to traditional mining methods. However, resource delineation and estimation remain challenging due to inefficient field sampling and unreliable correlations between Li concentration ([Li]) and environment-sensitive geochemical indicators. Supported by public data and newly acquired measurements of water chemistry for Alberta Devonian brines, we developed a cutoff-based data-driven approach to extract Li-rich environmental characteristics in the probability domain to predict [Li] levels at locations with water chemistry data but without [Li] measurements. The approach relies solely on commonly available geospatial (coordinates, stratigraphic position) and geochemical features, including contents of total dissolved solids (TDS) and cations of Na, K, Mg, and Ca. Validated against about one hundred Li-labeled samples measured after May 2022, the approach achieved a minimum precision and accuracy of 97% and 84%, respectively, for predicting three [Li] cutoff levels (i.e., > 35 mg/L, > 50 mg/L, and > 75 mg/L). It was subsequently applied to predict [Li] levels of formation water from 897 different locations with legacy water chemistry data. The results align spatially with observed trends of Li-rich brines in Alberta Devonian formations and expand resource delineation and estimation capabilities to areas and formations with limited [Li] data availability.

Injecting mixed gas (CO₂/N₂) into coal seams is an effective method to realize a win-win situation of CO₂ sequestration and enhanced coalbed methane (ECBM) recovery. The ratio of gas mixtures is a critical factor in pore structure evolution. In this study, we used high-pressure saturated systems to examine the effects of different gas mixture ratios on anthracite. The pore structure and mineral content of the CO₂/N₂-treated coal samples were analyzed by LP-N₂ (low-pressure N₂ adsorption), NMR (nuclear magnetic resonance), SEM (scanning electron microscopy), and XRD (X-ray diffractometry). The results of NMR and LP-N₂ showed that the coal samples’ pore volume, specific surface area, porosity increased after CO₂/N₂ treatment. The XRD analysis revealed that mineral consumption was dependent on CO₂ partial pressure and phase state (especially supercritical state). N₂ on the micropore and mesopore was mainly for high-pressure compression, prompting the closure of micropore and transforming mesopores to micropores; on the macropores and microfracture, it was mainly dilatation. This significantly alters pore roughness and complexity and leads to a shift in pore morphology from ink-bottle to slit type. Mineral dissolution, high-pressure compression, and pore throat unblocking were mainly responsible for the pore structure evolution under CO₂ and N₂ synergistic injection. The highest porosity and micropore volume were obtained when treating coal samples with CO₂: N₂ ratio of 8:2. Therefore, this ratio is expected to be optimal for implementing long-term gas mixture-ECBM and geologic CO₂ sequestration.