Natural Resources Research最新文献

{"title":"Evaluation of Reservoir Porosity and Permeability from Well Log Data Based on an Ensemble Approach: A Comprehensive Study Incorporating Experimental, Simulation, and Fieldwork Data","authors":"Edwin E. Nyakilla, Sun Guanhua, Hao Hongliang, Grant Charles, Mouigni B. Nafouanti, Emanuel X. Ricky, Selemani N. Silingi, Elieneza N. Abelly, Eric R. Shanghvi, Safi Naqibulla, Mbega R. Ngata, Erasto Kasala, Melckzedeck Mgimba, Alaa Abdulmalik, Fatna A. Said, Mbula N. Nadege, Johnson J. Kasali, Li Dan","doi":"10.1007/s11053-024-10402-9","DOIUrl":"https://doi.org/10.1007/s11053-024-10402-9","url":null,"abstract":"<p>Permeability and porosity are key parameters in reservoir characterization for understanding hydrocarbon flow behavior. While traditional laboratory core analysis is time-consuming, machine learning has emerged as a valuable tool for more efficient and accurate estimation. This paper proposes an ensemble technique called adaptive boosting (AdaBoost) for porosity and permeability estimation, utilizing methods such as support vector machine (SVM), Gaussian process regression (GPR), multivariate analysis, and backpropagation neural network (BPNN) for prediction based on well logs. Performance evaluation metrics including root mean square error, mean square error, and coefficient of determination (<i>R</i>²) were used to compare the models. The results demonstrate that AdaBoost outperformed GPR, SVM, and BPNN models in terms of processing time and accuracy, achieving <i>R</i>² values of 0.980 and 0.962 for permeability and porosity during training, respectively, and 0.960 and 0.951 during testing, respectively. This study highlights AdaBoost as a robust and accurate technique that can enhance reservoir characterization.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"36 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142245446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"In Situ Gas Content and Extraction Potential of Ultra-Deep Coalbed Methane in the Sichuan Basin, China","authors":"Haiqi Li, Guoxiao Zhou, Shida Chen, Song Li, Dazhen Tang","doi":"10.1007/s11053-024-10410-9","DOIUrl":"https://doi.org/10.1007/s11053-024-10410-9","url":null,"abstract":"<p>China’s deepest coalbed methane (CBM) exploratory well was drilled to over 4000 m in the Sichuan Basin, providing an effective database for analyzing the depth effect on methane accumulation. The results indicate that the in situ gas content generally continues to increase with depth, from 11.61 m³/t at 453.75–1829.6 m to 23 m³/t at 2467.98–4324.29 m. For coals at depths less than 2000 m, poor preservation conditions that are unable to seal free gas and adsorbed gas are dominant, with gas saturation ranging 45.79–97.61%. At depths deeper than 2000 m, the total gas content tends to be greater than in situ gas adsorption capacity, with gas saturation of 137.9–150.72%, indicating the coexistence of free gas (4.54–10.22 m³/t) and adsorbed gas (11.04–21.98 m³/t) as better preservation conditions. Similar to shale gas, deep coal seams exhibit excellent productivity potential as free gas can be extracted without the necessity of a drainage-pressure reduction stage for desorption, while a large amount of adsorbed gas can support long lifecycle of production wells. This research fills the gap in understanding the gas-bearing characteristics of ultra-deep CBM and is of great significance in guiding the exploration and development of deep CBM.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"7 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142235239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A New Sphalerite Thermometer Based on Machine Learning with Trace Element Geochemistry","authors":"Hongtao Zhao, Yu Zhang, Yongjun Shao, Jia Liao, Shuling Song, Genshen Cao, Ruichang Tan","doi":"10.1007/s11053-024-10408-3","DOIUrl":"https://doi.org/10.1007/s11053-024-10408-3","url":null,"abstract":"<p>Mineralization temperature determination is fundamental to economic geology research, yet quantifying it across mineralization remains a challenge. Sphalerite is ubiquitous in various types of mineral deposits and particularly abundant in Pb–Zn deposits, and its trace element composition is temperature-dependent, making it an ideal candidate for geothermometry. Here, we first compiled a global sphalerite trace element composition dataset (n = 1416, T = 75–430 °C), encompassing different Pb–Zn deposit types (Mississippi Valley-type, epithermal, sedimentary-exhalative, skarn-type, and volcanic massive sulfide deposits). After data processing following statistical norms, the different machine learning algorithms (random forest (RF), gradient boosted decision trees, artificial neural networks, least absolute shrinkage and selection operator, support vector machine, k-nearest neighbors, and linear regression) were employed to train different models to explore the potential link between the sphalerite-forming temperature and trace element geochemistry. Each of the model’s performance was evaluated using the leave-one-out cross-validation approach, which revealed the RF (R² = 0.88, RMSE = 26 °C) as the best-performing algorithm. Meanwhile, five-fold cross-validation results indicated that the RF model (R² = 0.87, RMSE = 25 °C) outperformed the GGIMFis thermometer (R² = 0.53, RMSE = 50 °C). Meanwhile, the feature importance analysis revealed that Ge and Mn displayed significant impacts on temperature prediction as the high temperature generally favors Mn, but not Ge, incorporation into the sphalerite structure. Finally, a model was trained with the entire dataset, generating a reliable sphalerite thermometer (SPRFT software, freely provided here) suitable for low to moderate temperature (75–430 °C) hydrothermal environments. This SPRFT thermometer was applied to evaluate the temperature of Pb–Zn mineralization in the Sichuan–Yunnan–Guizhou Pb–Zn metallogenic belt (SW China) and it provides an innovative perspective into the ore-fluid evolution. This study demonstrated a robust approach for calculating mineralization temperatures using machine learning. This novel methodology opens new avenues for investigating and recalculating more mineral geothermometers.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"30 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142234459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Risk-Aware Quantitative Mineral Prospectivity Mapping with Quantile-based Regression Models","authors":"Jixian Huang, Shijun Wan, Weifang Mao, Hao Deng, Jin Chen, Weiyang Tang","doi":"10.1007/s11053-024-10403-8","DOIUrl":"https://doi.org/10.1007/s11053-024-10403-8","url":null,"abstract":"<p>In the realm of deep resource exploration, risk is a factor that cannot be neglected. This study innovated existing quantitative mineralization prediction models with consideration of risk. Different from conventional approaches, which primarily focus on the conditional means and show obvious limitations in handling enriched or barren mineralization that deviate significantly from the mean, quantile regression (QR), as a method to predict the conditional distribution instead of conditional means, is expected to break through these limitations and to be used further for risk prediction. Drawing upon data from the Xiadian gold deposit, five geological factors were extracted as explanatory variables and gold grade was used as response variable. Four QR-based regression models were employed to predict the conditional distributions of gold grade. The comprehensive performance evaluation and comparison of these models focus on reliability, clarity, and their combination. The results unequivocally indicate that the quantile regression forest (QRF) model outperformed the other QR-based prediction models. Subsequently, a detailed performance analysis was conducted on the optimal QRF model, followed by a comparison with the RF model to validate its effectiveness. Building upon this foundation, by analyzing the predictive results of QRF models at certain quantiles in unknown regions, several discernible targets were delineated at different risk levels. Overall, this paper introduces the consideration of risk in mineral prospectivity prediction and attempts to predict the conditional distribution of mineralization in deep mineral prospectivity mapping contexts. These insights can offer valuable guidance in identifying optimal targets and in reducing exploration risks.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"9 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142174736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deformation Characteristics and Mechanical Constitutive Model of Coal Under Stress Wave Action","authors":"Zhoujie Gu, Rongxi Shen, Siqing Zhang, Xin Zhou, Zhentang Liu, Enlai Zhao, Xiulei Wang, Jianbin Jia","doi":"10.1007/s11053-024-10388-4","DOIUrl":"https://doi.org/10.1007/s11053-024-10388-4","url":null,"abstract":"<p>The three-dimensional (3D) stress waves of coal samples were studied using a true triaxial split Hopkinson pressure bar compression rod. The results indicate that the 3D strain of the coal samples increased gradually under vibration load. The 3D stress wave of coal samples showed attenuation characteristics, and the change amplitude of the stress wave of coal samples along the direction of dynamic load was the most obvious. The amplitude of stress wave was the largest in the axial direction constrained by pre-stressing 3 MPa, while the amplitude of stress wave in the lateral 2 MPa pre-stressing was smaller than that under the constraint of 1 MPa. The results showed that the main deformation of coal samples was along the impact direction, while the larger horizontal and vertical lateral binding forces limited the deformation of coal samples. The Fourier transform was performed on the 3D stress wave of the coal samples, and the change in the amplitude of the stress wave spectrum was correlated positively with the vibration. The spectrum amplitude of the coal samples under the pre-stressed 3 MPa constraint (axial) direction was the largest, while the spectrum amplitude of the coal samples under the lateral 2 MPa pre-stressed constraint was smaller than that under the binding 1 MPa. However, the main frequency of the three-way stress wave was distributed in 0–10 kHz. By calculating the energy consumption rate and wave velocity decay rate, it was verified that the damage of coal samples increased with increase in dynamic load. This experimental testing provides an effective testing method for studying the 3D stress waves of coal samples under complex stress medium conditions. In addition, a dynamic constitutive model of coal was constructed according to the mechanical behavior of coal and rock mass and the measured data.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"20 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142022057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interpretable SHAP Model Combining Meta-learning and Vision Transformer for Lithology Classification Using Limited and Unbalanced Drilling Data in Well Logging","authors":"Youzhuang Sun, Shanchen Pang, Zhiyuan Zhao, Yongan Zhang","doi":"10.1007/s11053-024-10396-4","DOIUrl":"https://doi.org/10.1007/s11053-024-10396-4","url":null,"abstract":"<p>Recent advances in geological exploration and oil and gas development have highlighted the critical need for accurate classification and prediction of subterranean lithologies. To address this, we introduce the Meta-Vision Transformer (Meta-ViT) method, a novel approach. This technique synergistically combines the adaptability of meta-learning with the analytical prowess of ViT. Meta-learning excels in identifying nuanced similarities across tasks, significantly enhancing learning efficiency. Simultaneously, the ViT leverages these meta-learning insights to navigate the complex landscape of geological exploration, improving lithology identification accuracy. The Meta-ViT model employs a support set to extract crucial insights through meta-learning, and a query set to test the generalizability of these insights. This dual-framework setup enables the ViT to detect various underground rock types with unprecedented precision. Additionally, by simulating diverse tasks and data scenarios, meta-learning broadens the model's applicational scope. Integrating the SHAP (SHapley Additive exPlanations) model, rooted in Shapley values from cooperative game theory, greatly enhances the interpretability of rock type classifications. We also utilized the ADASYN (Adaptive Synthetic Sampling) technique to optimize sample representation, generating new samples based on existing densities to ensure uniform distribution. Our extensive testing across various training and testing set ratios showed that the Meta-ViT model outperforms dramatically traditional machine learning models. This approach not only refines learning processes but it also adeptly addresses the inherent challenges of geological data analysis.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"143 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142002828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Strength Evolution Characteristics of Coal with Different Pore Structures and Mineral Inclusions Based on CT Scanning Reconstruction","authors":"Cun Zhang, Sheng Jia, Zhaopeng Ren, Qingsheng Bai, Lei Wang, Penghua Han","doi":"10.1007/s11053-024-10397-3","DOIUrl":"https://doi.org/10.1007/s11053-024-10397-3","url":null,"abstract":"<p>Water–rock interactions affect mineral inclusions and the pore structure of rock, subsequently affecting its mechanical and seepage properties. A method for quantitative analysis of the pore and mineral inclusion evolution characteristics of coal samples based on CT scanning is proposed. Accordingly, numerical model construction and block division of mineral inclusions and pores in coal samples were realized. The effects of mineral inclusions and the pore structure on coal failure were simulated and analyzed. The results showed that the porosity and pore distribution in coal influence its strength. The development of plastic zones in coal affected by pores can be divided into three stages: (1) tensile failure initiation stage, (2) shear failure penetration stage, and (3) failure rapid expansion stage. The higher the fractal dimension of the pores is, the greater the strength of coal. Pores and mineral inclusions degrade the strength of coal and accelerate the development of plastic zones. In the loading process, plastic zones preferentially emerge around pores and mineral inclusions. The plastic zones around mineral inclusions connect gradually with those around pores, thus accelerating coal failure.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"3 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141992056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mineral Reconnaissance Through Scientific Consensus: First National Prospectivity Maps for PGE–Ni–Cu–Cr and Witwatersrand-type Au Deposits in South Africa","authors":"Glen T. Nwaila, Steven E. Zhang, Julie E. Bourdeau, Emmanuel John M. Carranza, Stephanie Enslin, Musa S. D. Manzi, Fenitra Andriampenomanana, Yousef Ghorbani","doi":"10.1007/s11053-024-10390-w","DOIUrl":"https://doi.org/10.1007/s11053-024-10390-w","url":null,"abstract":"<p>We present here the first experimental science (consensus)-based mineral prospectivity mapping (MPM) method and its validation results in the form of national prospectivity maps and datasets for PGE–Ni–Cu–Cr and Witwatersrand-type Au deposits in South Africa. The research objectives were: (1) to develop the method toward applicative uses; (2) to the extent possible, validate the effectiveness of the method; and (3) to provide national MPM products. The MPM method was validated by targeting mega-deposits within the world’s largest and best exploited geological systems and mining districts—the Bushveld Complex and the Witwatersrand Basin. Their incomparable knowledge and mega-deposit status make them the most useful for validating MPM methods, serving as “certified reference targets”. Our MPM method is built using scientific consensus via deep ensemble construction, using workflow experimentation that propagates uncertainty of subjective workflow choices by mimicking the outcome of an ensemble of data scientists. The consensus models are a data-driven equivalent to expert aggregation, increasing confidence in our MPM products. By capturing workflow-induced uncertainty, the study produced MPM products that not only highlight potential exploration targets but also offer a spatial consensus level for each, de-risking downstream exploration. Our MPM results agree qualitatively with exploration and geological knowledge. In particular, our method identified areas of high prospectivity in known exploration regions and geologically and geospatially corresponding to the known extents of both mineral systems. The convergence rate of the ensemble demonstrated a high level of statistical durability of our MPM products, suggesting that they can guide exploration at a national scale until significant new data emerge. Potential new exploration targets for PGE–Ni–Cu–Cr are located northwest of the Bushveld Complex; for Au, promising areas are west of the Witwatersrand Basin. The broader implications of this work for the mineral industry are profound. As exploration becomes more data-driven, the question of trust in MPM products must be addressed; it can be done using the proposed scientific method.</p><h3 data-test=\"abstract-sub-heading\">Graphical Abstract</h3>\u0000","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"94 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141980912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Uncertainty Quantification in Mineral Resource Estimation","authors":"Oltingey Tuya Lindi, Adeyemi Emman Aladejare, Toochukwu Malachi Ozoji, Jukka-Pekka Ranta","doi":"10.1007/s11053-024-10394-6","DOIUrl":"https://doi.org/10.1007/s11053-024-10394-6","url":null,"abstract":"<p>Mineral resources are estimated to establish potential orebody with acceptable quality (grade) and quantity (tonnage) to validate investment. Estimating mineral resources is associated with uncertainty from sampling, geological heterogeneity, shortage of knowledge and application of mathematical models at sampled and unsampled locations. The uncertainty causes overestimation or underestimation of mineral deposit quality and/or quantity, affecting the anticipated value of a mining project. Therefore, uncertainty is assessed to avoid any likely risks, establish areas more prone to uncertainty and allocate resources to scale down potential consequences. Kriging, probabilistic, geostatistical simulation and machine learning methods are used to estimate mineral resources and assess uncertainty, and their applicability depends on deposit characteristics, amount of data available and expertise of technical personnel. These methods are scattered in the literature making them challenging to access when needed for uncertainty quantification. Therefore, this review aims to compile information about uncertainties in mineral resource estimation scatted in the literature and develop a knowledge base of methodologies for uncertainty quantification. In addition, mineral resource estimation comprises different interdependent steps, in and through which uncertainty accumulates and propagates toward the final estimate. Hence, this review demonstrates stepwise uncertainty propagation and assessment through various phases of the estimation process. This can broaden knowledge about mineral resource estimation and uncertainty assessment in each step and increase the accuracy of mineral resource estimates and mining project viability.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"100 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141915091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sand Production Prediction with Machine Learning using Input Variables from Geological and Operational Conditions in the Karazhanbas Oilfield, Kazakhstan","authors":"Ainash Shabdirova, Ashirgul Kozhagulova, Yernazar Samenov, Nguyen Minh, Yong Zhao","doi":"10.1007/s11053-024-10389-3","DOIUrl":"https://doi.org/10.1007/s11053-024-10389-3","url":null,"abstract":"<p>This paper describes a comprehensive approach to predict sand production in the Karazhanbas oilfield using machine learning (ML) techniques. By analyzing data from 2000 wells, the research uncovered the complex dynamics of sand production and emphasized the critical need for accurately predicting the peak sand mass and its occurrence time. ML techniques can have a significant impact on prediction of sand production and on the optimization of oilfield operation, which can be improved with the combined use of enriched training data and domain-specific knowledge. The research underscored the influence of geological factors, especially fault proximity, on prediction accuracy. Domain and field knowledge is needed to formulate different production scenarios for prediction purposes such that the relevant data can be selected for the training of ML models. Moreover, new metrics are needed to evaluate model performance as the applied method is tailored for different operational strategies. As the peak sand mass is considered a pivotal event in field operation, new metrics in terms of peak prediction accuracy and peak time prediction accuracy were introduced to evaluate the performance of ML models. A suite of ML algorithms was employed in the study, which demonstrated notable accuracy in the classification of sand-producing wells.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"72 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141910299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}