Natural Resources Research最新文献

{"title":"STNet: Advancing Lithology Identification with a Spatiotemporal Deep Learning Framework for Well Logging Data","authors":"Qingwei Pang, Chenglizhao Chen, Youzhuang Sun, Shanchen Pang","doi":"10.1007/s11053-024-10413-6","DOIUrl":"https://doi.org/10.1007/s11053-024-10413-6","url":null,"abstract":"<p>In the realm of oil and gas exploration, accurate identification of lithology is imperative for the assessment of resources and the refinement of extraction strategies. While artificial intelligence techniques have garnered considerable success in lithology identification, existing methodologies encounter difficulties when addressing highly heterogeneous and geologically intricate unconventional oil and gas reservoirs. Specifically, they struggle to account for the dynamic variations in sample characteristics across spatial dimensions and temporal sequences. This separate treatment of spatial and temporal dynamics not only confines the precision of fluid prediction but also significantly attenuates the robustness of the models. To address this challenge, we propose the spatiotemporal network (STNet), a dual-branch deep learning framework that integrates seamlessly spatial feature graph methods with time-sequential prediction methods. By employing a graph structure that accounts for spatial characteristics to capture the complex spatial relationships within logging data, and by utilizing a temporal model to discern the dynamic properties of time series data, this dual-mechanism framework enables a more comprehensive understanding of the multidimensional attributes of subsurface fluids, thereby enhancing the accuracy of lithology identification. Experimental results from multiple wells in different regions of the Tarim and Daqing oilfields demonstrate that STNet not only achieves detection accuracy exceeding 95% but also exhibits strong generalizability. The results indicate a significant improvement in the accuracy of lithology identification compared to seven other advanced models. Integrating both temporal and spatial elements of logging data provides a new perspective for enhancing fluid prediction capabilities.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"108 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142325512","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":"Non-Monotonic Transformation for Gaussianization of Regionalized Variables: Modeling Aspects","authors":"Farzaneh Khorram, Xavier Emery, Mohammad Maleki, Gabriel País","doi":"10.1007/s11053-024-10400-x","DOIUrl":"https://doi.org/10.1007/s11053-024-10400-x","url":null,"abstract":"<p>This paper proposes an extension of the traditional multigaussian model, where a regionalized variable measured on a continuous quantitative scale is represented as a transform of a stationary Gaussian random field. Such a model is popular in the earth and environmental sciences to address both spatial prediction and uncertainty assessment problems. The novelty of our proposal is that the transformation between the original variable and the associated Gaussian random field is not assumed to be monotonic, which offers greater versatility to the model. A step-by-step procedure is presented to infer the model parameters, based on the fitting of the marginal distribution and the indicator direct and cross-covariances of the original variable. The applicability of this procedure is illustrated with a case study related to grade control in a porphyry copper-gold deposit, where the fit of the gold grade distribution is shown to outperform the one obtained with the traditional multigaussian model based on a monotonic transformation. This translates into a better assessment of the uncertainty at unobserved locations, as proved by a split-sample validation.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"67 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142321637","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":"Non-monotonic Transformation for Gaussianization of Regionalized Variables: Conditional Simulation","authors":"Farzaneh Khorram, Xavier Emery, Mohammad Maleki, Gabriel País","doi":"10.1007/s11053-024-10398-2","DOIUrl":"https://doi.org/10.1007/s11053-024-10398-2","url":null,"abstract":"<p>The problem addressed in this work is the conditional simulation of a regionalized variable that is modeled as a realization of a non-monotonic transform of a Gaussian random field. As an alternative to Markov Chain Monte Carlo methods that often suffer from a slow convergence to the target distribution, we propose the use of sequential Monte Carlo approaches, with different variants of particle filtering. These variants are tested on synthetic and real datasets, to showcase their applicability and effectiveness under a proper setup of the importance sampling strategy, visiting sequence, number of particles, block size and kriging neighborhood used. The real case study, which deals with the simulation of gold grades in a porphyry copper-gold deposit, shows that the multi-Gaussian model based on a non-monotonic anamorphosis better assesses uncertainty than the traditional model based on a strictly monotonic anamorphosis, and that a moving neighborhood implementation of sequential Monte Carlo approaches can be successful, opening the door to applications to large-size problems in spatial uncertainty modeling.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"46 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142317575","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 Method of Rockburst Prediction for Categories with Sparse Data Using Improved XGBoost Algorithm","authors":"Ming Tao, Qizheng Zhao, Rui Zhao, Memon Muhammad Burhan","doi":"10.1007/s11053-024-10412-7","DOIUrl":"https://doi.org/10.1007/s11053-024-10412-7","url":null,"abstract":"<p>Rockburst prediction significantly affects the development and utilization of underground resources. Currently, an increasing number of artificial intelligence algorithms are being applied for rockburst prediction. However, owing to the scarcity of data for certain rockburst grades, machine learning models have struggled to accurately train and learn their characteristics, resulting in bias or overfitting. In this study, 321 worldwide cases of rockbursts were collected. Seven indices considering both rock mechanics and stress conditions were selected as input parameters for the model. To address the issue of limited data for certain rockburst grades, the Synthetic Minority Over-sampling TEchnique (SMOTE) algorithm was used for comprehensive oversampling and synthesis of the rockburst data. The theoretical rationality of this method was corroborated by the Spearman’s correlation coefficient. Additionally, the model hyperparameters were optimized using the Bayesian optimization method, and an improved eXtreme gradient boosting (XGBoost) rockburst prediction model (SM–BO–XGBoost) was established. The constructed SM–BO–XGBoost model was compared with decision tree, random forest, support vector machine, and k-nearest neighbor classification machine learning models. The results showed a significant improvement in the prediction accuracy for the None and Strong rockburst categories, which had limited data in the original rockburst dataset. To address the poor interpretability of the XGBoost model, the SHapley Additive exPlanations (SHAP) method was introduced to explain the constructed model, and to analyze the marginal contributions of different features to the model output across various rockburst grades. The SM-BO-XGBoost model was validated using field rockburst records from the Xincheng and Sanshandao gold mines. As indicated by the results, the model demonstrated favorable performance and applicability, with wide potential for predicting engineering rockbursts.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"31 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142313686","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":"Thermal Generation Behavior, Key Groups and Disaster-Causing Mechanism of Unloaded Bulk Coal Under High Ground Temperature Conditions","authors":"Xiaodong Yu, Huiyong Niu, Haiyan Wang, Hongyu Pan, Qingqing Sun, Siwei Sun, Xi Yang","doi":"10.1007/s11053-024-10414-5","DOIUrl":"https://doi.org/10.1007/s11053-024-10414-5","url":null,"abstract":"<p>A coal mining area is more susceptible to the danger of coal spontaneous combustion due to elevated ground temperature and high stress from deep mining. To investigate the heat generation behavior and the evolution of critical groups of unloaded bulk coal under high primary temperature in a deep mine, the thermogravimetric and heat release characteristics of unloaded bulk coal were measured using simultaneous thermal analyzer, and the migration and shifts in micro-groups of unloaded bulk coal were investigated by in situ diffuse reflectance. The key groups contributing most to the thermal weightlessness and heat release of coal during the oxidation phase at low temperatures were identified by grey correlation analysis. The results indicated that, as the deep thermal action temperature and initial load stress increase, the characteristic temperature, thermal equilibrium temperature, and initial exothermic temperature of coal decrease gradually, the combustion performance and exothermic capacity increase progressively, the aliphatic structure of coal is detached more easily, and the amount of hydroxyl and oxygenated functional group active groups increases. The key reactive groups that affect thermal weightlessness and heat release were determined by grey correlation analysis to be hydroxyl and carbonyl groups. The increase in thermal environment temperature and initial load in deep wells leads to the enhancement of cryogenic oxidative self-heating tendency of deep residual coals and the growth of spontaneous combustion risk. The research results established a theoretical basis for strategies to curb and manage coal fires in the complex milieu of deep-seam coal mining operations.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"23 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142317578","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":"Pattern-Based Multiple-point Geostatistics for 3D Automatic Geological Modeling of Borehole Data","authors":"Jiateng Guo, Yufei Zheng, Zhibin Liu, Xulei Wang, Jianqiao Zhang, Xingzhou Zhang","doi":"10.1007/s11053-024-10405-6","DOIUrl":"https://doi.org/10.1007/s11053-024-10405-6","url":null,"abstract":"<p>Urban 3D geological modeling is an essential approach for quickly understanding the underground geological structure of a city and guiding underground engineering construction. Modeling methods based on multiple-point geostatistics can provide probabilistic results regarding geological structure. The traditional multiple-point geostatistics modeling approach is characterized by low efficiency and typically relies on data from geological sections or conceptual models; therefore, it cannot be well applied to practical geological exploration projects that are based primarily on borehole data. In this paper, we propose a pattern-based multiple-point geostatistics modeling method PACSIM (pattern attribute classification simulation). This method uses borehole data as the primary data. First, geological structural information is extracted based on the borehole data to establish a training image database. Next, based on the distribution patterns of geological structures, a method for establishing attribute-based pattern databases is proposed to enhance modeling accuracy. Finally, a probability constraint strategy is introduced to address the distribution of complex strata and filter out grids with high certainty, thereby further improving the modeling accuracy. Based on the aforementioned strategies, a multiple-point geostatistics modeling workflow specifically targeting underground geological structures in urban areas was designed and subjected to practical verification. The results indicate that the proposed method required less time than the PSCSIM method, and improved the modeling efficiency by 72.87% while ensuring the accuracy of the modeling results. It can accurately identify relationships among complex strata and match the stratum distribution patterns revealed by borehole data, providing a reference for high-precision geological modeling in cases with high uncertainty.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"65 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142317657","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":"Evolution Patterns and Anisotropic Connectivity Characteristics of Pores and Fissures in Oil Shale After Steam Heating at Different Temperatures","authors":"Xudong Huang, Dong Yang, Guoying Wang, Kaidong Zhang, Jing Zhao","doi":"10.1007/s11053-024-10406-5","DOIUrl":"https://doi.org/10.1007/s11053-024-10406-5","url":null,"abstract":"<p>This paper presents a thorough investigation into the evolutionary patterns of pore–fissure networks and their anisotropic connectivity characteristics within oil shale. We utilized CT digital core analysis after steam heating at varying temperatures. The study revealed that untreated oil shale has a densely compacted internal structure without distinguishable pore–fissure networks. However, steam exposure at temperatures exceeding 314 °C induced penetrating cracks along the bedding plane. This significantly modifies the mass transfer properties in the parallel bedding direction. Beyond 382 °C, continuous thermal cracking occurred, leading to numerous fissures along sedimentary bedding planes. This was accompanied by clustered pores formed through organic matter pyrolysis. These aggregated pores gradually interconnected adjacent parallel fissures, forming distinctive pore–crack clusters. Notably, as the temperature surpassed 500 °C, these pore–crack clusters continued to expand perpendicular to the lamination plane, profoundly influencing the mass transfer performance in this orientation. This phenomenon underscores the fundamental mechanism altering oil shale's mass transfer behavior perpendicular to the layer plane. From the perspective of percolation theory, the percolation threshold parallel to the lamination orientation was approximately 3%, with the transition around 300 °C. Conversely, the percolation threshold vertical to the sedimentary rock layers was approximately 14%, with the transition at temperatures surpassing 500 °C.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"14 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142277002","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":"Representing the Uncertainty of a 3D Geological Model via Global Optimum Truth Discovery Technology","authors":"Nan Li, Keyan Xiao, Shitao Yin, Cangbai Li, Xianglong Song, Wenkai Chu, Weihua Hua, Rui Cao","doi":"10.1007/s11053-024-10404-7","DOIUrl":"https://doi.org/10.1007/s11053-024-10404-7","url":null,"abstract":"<p>Three-dimensional (3D) geological modeling is a process of interpretation that integrates multiple source inputs and knowledge into geometry to represent the understanding of geologists. When geologists build a high-quality 3D geological model, this process still involves some issues such as sparse drillhole data, imperfect prior knowledge, and sensitive modeling algorithms. Therefore, taking uncertainty as the measurement criterion for the variation extent of the posterior likelihood of the 3D geological model and assisting in increasing the quality of the model are crucial issues in this domain. This paper proposes a novel method based on a (1 + <i>ε</i>)-approximation global optimum strategy, which is a type of big data and machine learning technique, to determine and present the uncertainty hidden in geometry. Compared with previous approaches, our strategy made the following new contributions: (1) the global optimum solution calculated by potential models is utilized to represent the uncertainty at each location; (2) the strategy offers a quantifiable reliability to each model that is involved in the evaluation process, and values of reliability are unknown before the commencement, meaning that they do not depend on expert experience; moreover, they can also be verified by comparing prior knowledge with information that such 3D models possess; (3) compared with previous studies, the number of perturbing models is no longer a key prerequisite for this kind of study to evaluate the quality of one geological model, thereby greatly reducing the computational complexity and improving the practicability. Finally, a case study was conducted to assess the uncertainty of a real 3D geological model in northwest Hunan Province, China.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"1 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142313685","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":"Diffusion of Surface CO2 in Coalfield Fire Areas by Surface Temperature and Wind","authors":"Junpeng Zhang, Haiyan Wang, Cheng Fan, Zhenning Fan, Haining Liang, Jian Zhang","doi":"10.1007/s11053-024-10401-w","DOIUrl":"https://doi.org/10.1007/s11053-024-10401-w","url":null,"abstract":"<p>In the early stages of a coalfield fire, CO₂ emissions are relatively low, and it is challenging to detect CO₂ concentrations in the soil surface due to the impact of surface temperature and wind. Investigating CO₂ concentration changes under surface temperature and wind conditions can provide experimental evidence and theoretical foundation for selecting optimal sampling locations and time. Using an automated monitoring platform for shallow soil CO₂, this study analyzed how surface wind speed and temperature affect the diffusion of CO₂ gas of surface sands. The effects of surface wind and temperature on CO₂ concentrations growth at different depths of the shallow surface were studied experimentally. When the surface temperature was 40 ℃ higher than the ambient temperature, the decrease of CO₂ concentrations for coarse sands with permeability of 2.13 × 10^-9 m² was most significant under high surface temperature conditions. However, the effect of high surface temperature on fine sands with permeability of 1.1 × 10^-12 m² was insignificant. Coarse sand with high medium permeability was most vulnerable to the fluctuation of surface wind speed. The surface CO₂ concentrations decreased by 93% at a depth of 22 cm in the coarse sands on the downwind side of the surface compared to natural convection conditions. In comparison, the CO₂ concentrations decreased by 37.5% on the upwind sides under small wind speeds. The coupling effect of high temperature and wind speed on the surface had a greater disturbance depth on fine and medium sands than low windy conditions. In addition, detecting shallow surface concentrations of CO₂ for the localization of fire sources was more advantageous during low temperature detection periods. In order to describe gas diffusion at the surface, mathematical and physical equations were developed. A combination of experimental and simulation theory was used to predict the depth of penetration of shallow surface gas by wind speed and temperature. The critical Darcy–Rayleigh number for temperature disturbance to shallow surface gas was approximately 6.3 when using medium and coarse sands with high permeability. Simulations show that the wind-induced penetration depth was 40.8 cm for coarse sand and 23.5 cm for medium sand at a surface wind speed of about 0.4 m/s combined with the experiments. It is necessary to detect CO₂ concentrations at least at depth of 23.5 cm in conditions of low surface wind speed, particularly in the overlying medium with high porosity.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"190 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142275880","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":"Microstructural Changes and Kinetic Analysis of Oxidation Reaction in Coal–Oil Symbiosis","authors":"Lintao Hu, Hongqing Zhu, Binrui Li, Rui Li, Linhao Xie, Ruoyi Tao, Baolin Qu","doi":"10.1007/s11053-024-10407-4","DOIUrl":"https://doi.org/10.1007/s11053-024-10407-4","url":null,"abstract":"<p>During the coal mining process, fractures generated can lead to crude oil infiltrating into coal seams, forming coal–oil symbiosis (COS). The complex three-phase interaction of coal–oil–oxygen makes the mechanism of COS spontaneous combustion filled with uncertainties. This study utilized synchronous thermal analysis to analyze the physico-chemical behavior of raw coal and COS at different heating rates. Additionally, detailed characterization of their surface morphology and functional groups was conducted using scanning electron microscopy (SEM) and in situ FTIR technology. The findings suggest that the coverage of crude oil on the surface of coal inhibits the adsorption of oxygen by the coal, leading to the disappearance of the stage where COS absorbs oxygen and gains weight. Moreover, the continuous decline of –OH groups and aliphatic hydrocarbons in the later stages suggests that crude oil acts as a catalyst for combustion during the latter stages of the reaction. The Kissinger–Akahira–Sunose, Starink, and Flynn–Wall–Ozawa methods showed that the apparent activation energy of COS is 23.3 and 19.7% lower than that of raw coal in thermal decomposition and combustion stages, respectively.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"2 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142245338","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}