Natural Resources Research最新文献

The deep coalbed methane (CBM) resources in the Ordos Basin are enormous, and their exploration and development breakthrough are among the critical ways to improve CBM production in China. The occurrence state of deep CBM has unique characteristics caused directly by the change in methane density (ρ). By predicting key adsorption parameters and solving directly for adsorbed methane density (ρ_a), it is concluded that ρ_a decreases with increasing temperature and increases rapidly at first and then tends to stabilize with increasing pressure. Considering the characteristics of supercritical methane adsorption, a porosity (φ) prediction model for deep coal reservoirs was established based on these unique occurrence characteristics. A new equation for predicting gas content in deep coal seams was developed by combining the free gas content (V_fg) calculation method for unconventional oil and gas reservoirs and the adsorbed gas content (V_ad) method based on ρ_a. It was observed that the V_fg increased with pressure and φ but decreased with increasing water saturation and temperature. However, as temperature and pressure increased, the rate of increase in V_fg slowed down, probably because of the influence of φ decreasing with increasing temperature and pressure, which is similar to the change in ρ_a. Meanwhile, the V_ad increased with temperature and pressure, showing a trend of rapid increase followed by a decrease. These indicate that, as the depth and pressure increase and the temperature rises in deep coal seams, the negative effect of temperature gradually outweighs the positive effect of pressure. When φ increased to a specific value in low- to medium-rank coal, the V_fg can exceed the V_ad at depths between 2000 and 2500 m. Compared to high-rank coal, which has high V_ad, low- to medium-rank coals are more prone to experience the saturation phenomenon where the V_fg exceeds the V_ad.

Combining multiple monitoring methods can improve the accuracy of coal damage and fracture behavior detection. In this study, nine coal samples, each with similar P-wave velocities and masses, were subjected to joint monitoring experiments involving multiple physical parameters. The acoustic emission (AE) and resistance information of coal samples were assessed from the initiation of loading to eventual failure under diverse uniaxial loading rates. The characteristic electrical and acoustic parameters were analyzed in combination with coal damage conditions. The results show that, throughout the loading process, resistivity declined gradually with escalation of coal strain, followed by an abrupt nonlinear increase. Deformation before failure reduced coal resistivity by up to 11.39%. As the coal crack area expanded, the resistivity post-failure reached threefold the initial value. The AE ring count peak value corresponded to crack growth, and the AE energy had a power law distribution feature. The frequency band effect of the AE peak frequency was significant, and shear cracks accounted for more than 80%. Resistance and AE ring count exhibited simultaneous responses to coal failure, and the characteristic parameters of acoustic-electrical behavior demonstrated consistent patterns for cracks induced by various loading rates. The time sequence characteristics of the RSD index, which quantified the degree of resistivity fluctuation, corresponded almost exactly to the development process of coal damage described by AE, and the peak value of this index corresponded to the AE event in the time scale. The overall fluctuation degrees in resistivity of coal samples with varying damage levels showed positive correlation with the AE ring count. An acoustic-electric method for characterizing coal damage is summarized, and corresponding resistivity characteristic parameters are proposed. These parameters have a significant response law to coal damage, which is helpful in supplementing a new index for early warning of geological disasters.

Metamorphic buried hills are characterized as fractured reservoirs with immense potential for hydrocarbon exploration and exploitation. Identifying their effective reservoirs is crucial for prioritizing exploration and development efforts. However, current methods are inadequate for such reservoirs. In this study, we established a new evaluation method, the metamorphic reservoir quality index (MRQI), based on analyses of wallrock cores, cuttings, well logs, and test data in the Bozhong (BZ) 19-6 area. The MRQI method integrates three main control factors for the formation of metamorphic buried hill reservoirs, namely lithology, tectonism, and weathering. Our results indicate that exploratory wells in the BZ19-6 area have MRQI values ranging from 29.91 to 86.47, with average of 56.45, showcasing the wide distribution of metamorphic rocks with moderate reservoir quality. We also observed a significant increasing trend between fracture development and MRQI values, suggesting that MRQI can effectively characterize reservoir development. Moreover, individual well production displays an exponentially increasing trend with higher MRQI, with a clear turning point at MRQI of 65, representing the lower limit of an effective reservoir. Finally, we applied the MRQI method to classify the reservoir through depth in two exploration wells, demonstrating its effectiveness. The MRQI method enables quick and effective decision-making on exploratory and developmental projects in metamorphic buried hills. Hence, this method provides a valuable tool for reservoir management and enhancing the economic benefits of exploration and exploitation in such reservoirs.

Prediction of well production from unconventional reservoirs is a complex problem even with considerable amounts of data especially due to uncertainties and incomplete understanding of physics. Data analytic techniques (DAT) with machine learning algorithms are an effective approach to enhance solution reliability for robust forward recovery forecasting from unconventional resources. However, there are still some difficulties in selecting and building the best DAT models, and in using them effectively for decision making. The objective of this study is to explore the application of DAT and Monte-Carlo simulation for forecasting and enhancing oil production of a horizontal well that has been hydraulically fractured in a tight reservoir. To do this, a database was first generated from 495 simulations of a tight oil reservoir, where the oil production in the first year depends on 16 variables, including reservoir characteristics and well design parameters. Afterward, using the random forest algorithm, the most influential parameters were determined. Considering the optimum hyperparameters for each algorithm, the best algorithm, which was identified through a comparative study, was then integrated with Monte-Carlo simulation to determine the quality of the production well. The results showed that oil production was mainly affected by well length, reservoir permeability, and number of fracture stages. The results also indicated that a neural network model with two hidden layers performed better than the other algorithms in predicting oil production (lower mean absolute error and standard deviation). Finally, the probabilistic analysis revealed that the completion design parameters were within the appropriate range.

It is paramount that solutions to questions of energy security for a developing nation be addressed through its internal resources. India, endowed with 23.8 billion tons of deep un-minable lignite, faces the challenge of economically sustainable extraction. This study presents a comprehensive assessment of lignite's suitability for underground coal gasification (UCG) compared to bituminous coal. Employing a multi-dimensional approach, combining single-step and distributed activation energy model of pyrolysis kinetic modeling with extensive physicochemical analysis (proximate and ultimate analyses, FTIR, SEM–EDX, XRD), revealed that lignite has a lower activation energy making it suitable for UCG. This finding, highlighted by kinetic modeling, is substantiated by the lignite’s structural properties as identified in physicochemical analysis. This study leverages machine learning for higher heating value prediction, finding long short-term memory as the most effective model compared to five other models based on the R² score and error values. Additionally, an XGBoost algorithm-based model predicts syngas heating value and yield while showcasing the application of machine learning in enhancing energy prediction accuracy. The economic analysis, applied for a 50 MW power plant framework, determines the unit costs for syngas and electricity production to be 7.49 and 6.71 $/GJ and 53.68 and 59.93 $/MWh for the samples of lignite and bituminous coal, respectively. The sensitivity analysis revealed that the energy content in syngas is the most significant parameter. These comprehensive findings validate lignite's potential for energy production in India, offering insights for similar resource optimization in other developing countries.

Quantitative modeling of geological heterogeneity is critical for resource management and decision-making. However, in the early stages of a mining project, the only data available for modeling the spatial variability of the variables are from a limited number of exploration drill holes. This means that the empirical cumulative distribution function of the data, which is one of the key inputs for the geostatistical simulation, is uncertain, and ignoring this uncertainty may lead to biased resource risk assessments. The parameter uncertainty can be quantified by the multivariate spatial bootstrap procedure and propagated through geostatistical simulation workflows. This methodology is demonstrated in a case study using the data from the former lead and zinc mine at Lisheen, Ireland. The joint modeling of the lead and zinc grades is carried out by using (1) all of the available data, (2) a representative subset (approximately 10% of the available data) without parameter uncertainty, and (3) the same subset with parameter uncertainty. In all cases, the turning bands simulation approach generates realizations of lead and zinc grades. In the third case, the uncertainty in the lead and zinc grade distributions is first quantified (i.e., prior uncertainty) by the correlated bootstrap realizations. This joint prior uncertainty is then updated in simulation by the conditioning data and domain limits, which results in posterior uncertainty. The results indicate that a more realistic resource risk assessment can be achieved when parameter uncertainty is considered.

A combination of physical and numerical simulations is employed to compare the differences in desorption deformation and desorption volumes of coal samples under varying depressurization paths, aiming to understand their impact on coalbed methane (CBM) extraction. In this work, two medium-rank coal samples from the central-eastern region of the Qinshui Basin were chosen for the desorption–strain experiments. The experiment facilitated real-time observation of desorption gas volumes and coal matrix deformation under various depressurization paths. Finite element analysis was utilized to model and analyze the evolution of pore pressure during depressurization and desorption. The research outcomes indicate a dependency of desorption gas volumes on the chosen depressurization path. With the slow depressurization path, the desorption gas volume over 12 h was 8% higher than that achieved with the rapid depressurization path. When the pressure difference across the pores fell below the pressure difference required for gas migration, the gas cannot overcome the resistance, leading to residual gas being trapped in the pores. With the slow depressurization path, the coal matrix exhibited notably lower residual pore pressure and remaining gas volume compared to the rapid depressurization path. The differences in desorption volumes under various depressurization paths were mainly driven by the pore structure and matrix strain. Rapid depressurization led to pore contraction, which decreased pore size and connectivity, increasing resistance to gas migration and decreasing absorption rates. Conversely, the slow depressurization path led to a more gradual pore contraction and minimal strain, supporting the continuous production of CBM.

Characterization of reservoir rock samples under in situ conditions is crucial for evaluating the quantity and exploitable potential of geothermal energy. However, reservoir characterization is impeded by the lack of precise assessments of rock properties at in situ temperatures. To address this, high-temperature micro-computed tomography was deployed, integrating digital volume correlation (DVC) technology to ascertain the strain exhibited by pores and minerals. The findings reveal the neglect of the effects of mineral displacement at high temperatures previously. The strain within the sandstone is heterogeneous and primarily concentrated at the edges of large grains of brittle minerals and the fillings among them. The weak interfaces among diverse large-grain brittle minerals and their fillings cause strain in sandstone. At 105 °C, the average equivalent strain in sandstone was 0.03275 determined by DVC, significantly surpassing the strain of mineral thermal expansion, which remained below 0.001. Most of the strain was caused by mineral displacement, not mineral thermal expansion. The porosity of the sandstone decreased from 5.02 to 4.84% as the temperature increased from 30 to 105 °C, and some of the connected pores were transformed into independent pores at high temperatures. The tortuosity of the sample increased from 3.88 to 3.97 from 30 to 105 °C, respectively, and the temperature increase caused permeability reduction from 67.9 to 58.2 mD (1 mD = 9.869233 × 10⁻¹⁶ m²). The thermal treatment experiments demonstrated that mineral displacement in sandstones is a universal phenomenon at high temperatures and it leads to changes in sandstone pore structure and permeability. This study advances a new path to investigate geothermal reservoir properties at high temperatures and offers novel understanding.

Although narrow deposits hold economic and environmental importance, the assessment of their mineral resources with kriging is often challenging due to the shortage of paired data for variogram analysis along the narrow dimension. This study addresses the problem of modeling alumina and silica grades in a layered bauxite deposit using three geostatistical approaches. The first one is the direct approach, where grades are cokriged inside the mineralized layer interpreted by geologists; the second one (indirect approach) substitutes the grades with the layer thickness and accumulations as service variables; the third approach trades the traditional random field representation for a deterministic representation to jointly predict the alumina and silica grades inside the mineralized layer, based on an innovative technique called transitive cokriging. A comparative analysis of the results highlights the strengths and weaknesses of each approach, in terms of data preparation, geological modeling, and mineral resources modeling. The study concludes that the transitive approach is a promising alternative for mapping ore grades in deposits with narrow dimensions, due to a more congenial structural analysis and a reduced smoothing effect in comparison with the direct approach, while it avoids scaling down the representation of the deposit to two dimensions as in the indirect approach.

With increasing emphasis on low-carbon environmental protection, CO₂ enhanced coalbed methane production and methane reuse in abandoned mines (rich in N₂) have gradually become one of the future development directions. These scenarios involve the coordinated migration of different gases such as CO₂, CH₄, and N₂, and the differences in properties of different gases that affect the flow process. Previous studies often focused on the adsorption differences between gases, neglecting the differences during desorption process. In view of this, the current work conducted experiments and finite element numerical analysis on the desorption process of CO₂, CH₄, and N₂, clarified the differences and influencing factors of desorption among the gases, and analyzed the flow change rules under different permeability and diffusion capabilities. The results indicated that the main differences among CO₂, CH₄, and N₂ during desorption are reflected in the parameters of Langmuir volume, permeability, and diffusion coefficient. These parameters showed that CO₂ has the highest value during desorption, while N₂ has the lowest. The factors affecting the magnitude of differences between CO₂, CH₄, and N₂ are mainly their compositions. Specifically, ash content significantly affects the difference in adsorption capacity, while moisture content influences permeability and diffusion coefficient. During desorption, permeability plays a continuous role throughout the whole process, while diffusion coefficient is exhibited mainly in the initial stage of desorption. Different gases have varying sensitivities to permeability and diffusion coefficients during desorption. Changes in permeability and diffusion coefficient significantly affect the CO₂ desorption process. N₂, on the other hand, is the least sensitive, especially to changes in diffusion coefficient. During gas flow, when reservoir permeability is less than 0.01 mD (= 9.869233 × 10⁻¹⁸ m²), permeability becomes the main factor that affects flow. When the diffusion coefficient is less than 5 × 10⁻⁹ m²/s, increasing the diffusion coefficient is necessary to effectively promote gas outflow. To effectively increase gas production, it is necessary to comprehensively consider the magnitudes of permeability and diffusion coefficient.