Natural Resources Research最新文献

Weathered water-saturated coal (WWSC) reserves in oxygen-poor environments in a goaf are present in large amounts, dispersed and pose a high risk of spontaneous combustion (SC). To determine the thermodynamic behavior and disaster-causing tendency of WWSCs stored in oxygen-poor environments, WWSCs with different weathering cycles were prepared. The oxidative–thermal behaviors of WWSCs in atmospheres with different oxygen concentrations were analyzed by using thermogravimetric analysis–differential scanning calorimetry (TG–DSC), and systematic combustion thermodynamic analyses were carried out. The results showed that the weathering time and environmental oxygen concentration synergistically affected the conversion rate of WWSC, thus affecting the length of the reaction stage. The reaction and transformation ability of WWSC weathered for 27 days at the low-temperature stage was better; the heat production of WWSC with short-term weathering (O_15-3d) was higher in the oxygen-poor environment, with maximum heat release and heat flow of 15751.5 J and 15 W/g, respectively. Different coal temperature stages of the WWSCs have different reaction dynamic models; these included low temperature–first-order reaction model and high temperature–two-dimensional diffusion Valensi model. The treatment of high oxygen concentration–long weathering time and low oxygen concentration–short weathering time caused a decrease in the E, ΔH and ΔG of WWSC and an increase in the D_f and H_F of coal. The synergistic effect of weathering time and oxygen concentration led to the greater SC tendency of the water-saturated coal with high oxygen concentration–long weathering time and low oxygen concentration–short weathering time, and the risk of thermal disaster was high. Our research results provide an important theoretical basis for goaf fire prevention and resource and environmental protection in deep coal mining and goaf remining and other projects.

Free hydrocarbons are among the fundamental indicators of shale organic matter richness and potential for hydrocarbon generation. The traditional experimental analysis method based on rock pyrolysis is time-consuming and expensive. This study aimed to predict free hydrocarbons in the Qingshankou Formation shale of the Changling Depression in the Songliao Basin. Using 521 sets of logging data as input, a stacking ensemble model for predicting shale free hydrocarbons content was developed based on six base learner models including decision tree (DT), random forest (RF), gradient boosting decision tree (GBDT), support vector machine (SVM), K-nearest neighbors (KNN), and artificial neural network (ANN), combined with meta model (linear regression). The performance analysis and ranking of models are based on three error evaluation metrics: coefficient of determination, root mean square error, and mean absolute error. The results indicated that model performance ranking from high to low was Stacking, RF, SVM, KNN, GBDT, ANN, and DT. The stacking ensemble model with the best performance was successfully applied to predict the free hydrocarbons curve on the connected well profile. Shapley additive explanations were used explain the best performing stacking ensemble model, and the results indicated that gamma ray log in the logging sequence contributed the most to the prediction of shale free hydrocarbons content. This study provides a model interpretation experience for predicting free hydrocarbons to help evaluate source rocks and select the “sweet spot” for shale oil.

Accurately characterizing reservoir petrophysical parameters and delineating lithofacies is challenging in heterogeneous formations. Traditional seismic interpretations may be uncertain, but probabilistic neural network (PNN) modeling and seismic inversion constrained by well log data have improved interpretation accuracy and reduced uncertainty in determining reservoir properties such as volume and distribution. It is necessary to determine reservoir assessment parameters precisely and conduct a thorough integrated study of promising blocks that hold paramount potential and will help reduce drilling risk and increase the recovery of oil and gas resources. This paper provides a comprehensive integrated approach to differentiate lithofacies within a gas-prone reservoir (Lower Goru Formation) and predict the potential for hydrocarbon resources in the Sinjhoro Block of Pakistan. This approach involves petrophysical analysis, rock physics, seismic attributes, seismic inversion, multi-attribute analysis, and PNN for estimating petrophysical parameters for source and reservoir rock evaluation. The trace-based 2D extracted attributes, such as pronounced root mean square amplitude anomalies within the Talhar Shale, indicate that hydrocarbon indicators are aligned with the seismic structure interpretation and are considered an appropriate tool for extracting information from poststack seismic data. The results obtained through an integrated approach effectively optimize lateral and vertical facies heterogeneities in target formations, enabling the precise prediction of reservoir parameter distributions. The petrophysical analysis results indicated the presence of gas sands in Basal Sands (hydrocarbon saturation = 53%) and Massive Sands (hydrocarbon saturation = 66%). The current findings demonstrate that the PNN method is the most accurate for estimating petrophysical parameters (volume of shale, total porosity, effective porosity, and water saturation), with a correlation of approximately 0.97–0.99, whereas multi-attribute regression analysis has a correlation of approximately 0.56–0.67. The well log analysis results revealed that the average total organic carbon content of the Talhar Shale in all the wells ranges 1.20–2.20%, its average porosity is 10–16%, its Poisson’s ratio is low (0.20–0.27), and its Young's modulus is high (05–08). Thus, the proposed methodology outlined in the current study has potential applicability in comparable geological settings across various basins in Pakistan and globally.