Natural Resources Research最新文献

{"title":"Toward Precise Long-Term Rockburst Forecasting: A Fusion of SVM and Cutting-Edge Meta-heuristic Algorithms","authors":"Danial Jahed Armaghani, Peixi Yang, Xuzhen He, Biswajeet Pradhan, Jian Zhou, Daichao Sheng","doi":"10.1007/s11053-024-10371-z","DOIUrl":"https://doi.org/10.1007/s11053-024-10371-z","url":null,"abstract":"<p>Rockburst is one of the most hazardous geological disasters in underground engineering due to its complex causes and destructive nature. To address this, there is an imperative for methodologies that can predict rockbursts quickly and effectively to mitigate preemptively the risks and damages. In this study, 259 rockburst instances were analyzed, employing six rockburst feature parameters: maximum tangential stress (<i>σ</i>_<i>θ</i>), uniaxial compressive strength of rock (<i>σ</i>_<i>c</i>), uniaxial tensile strength of rock (<i>σ</i>_<i>t</i>), stress coefficient (<i>σ</i>_<i>θ</i><i>/σ</i>_<i>t</i>), rock brittleness coefficient (<i>σ</i>_<i>c</i><i>/σ</i>_<i>t</i>), and elastic energy index (<i>Wet</i>) as inputs. By integrating three novel meta-heuristic algorithms—dingo optimization algorithm (DOA), osprey optimization algorithm (OOA), and rime-ice optimization algorithm (RIME)—with support vector machine (SVM), hybrid models for long-term rockburst trend prediction were constructed. Performance evaluations through fivefold cross-validation revealed that for the no rockbursts, DOA–SVM (Pop = 200) demonstrated superior predictive performance, achieving an accuracy of 0.9808, precision of 0.9231, recall of 1, and an F1-score of 0.96. For moderate rockbursts, OOA–SVM (Pop = 100) emerged as the most effective, registering an accuracy of 0.9808, precision of 0.9545, recall of 1, and an F1-score of 0.9767. For light and severe rockbursts, DOA–SVM, OOA–SVM, and RIME–SVM showcased comparable predictive outcomes. However, these hybrid models outperformed traditional SVM models optimized with conventional algorithms in terms of accuracy across all rockburst hazard levels. Moreover, the hybrid models underwent additional validation with a new dataset of 20 rockburst instances collected globally, confirming their robust efficacy and exceptional generalization capabilities. An ensuing analysis using local interpretable model-agnostic explanations (LIME) on the six key feature parameters revealed a significant positive correlation between <i>σ</i>_<i>θ</i> and <i>Wet</i> with the severity of rockbursts. These results not only affirm the superior optimization performance of the DOA, OOA, and RIME algorithms but also their substantial potential to enhance the predictive accuracy of machine learning models in forecasting long-term rockbursts.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"44 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141425520","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":"Predictive Modeling of Canadian Carbonatite-Hosted REE +/− Nb Deposits","authors":"Mohammad Parsa, Christopher J. M. Lawley, Renato Cumani, Ernst Schetselaar, Jeff Harris, David R. Lentz, Steven E. Zhang, Julie E. Bourdeau","doi":"10.1007/s11053-024-10369-7","DOIUrl":"https://doi.org/10.1007/s11053-024-10369-7","url":null,"abstract":"<p>Carbonatites are the primary geological sources for rare earth elements (REEs) and niobium (Nb). This study applies machine learning techniques to generate national-scale prospectivity models and support mineral exploration targeting of Canadian carbonatite-hosted REE +/− Nb deposits. Extreme target feature label imbalance, diverse geological settings hosting these deposits throughout Canada, selecting negative labels, and issues regarding the interpretability of some machine learning models are major challenges impeding data-driven prospectivity modeling of carbonatite-hosted REE +/− Nb deposits. A multi-stage framework, exploiting global hierarchical tessellation model systems, data-space similarity measures, ensemble modeling, and Shapley additive explanations was coupled with convolutional neural networks (CNN) and random forest to meet the objectives of this work. A <i>risk</i>–<i>return</i> analysis was further implemented to assist with model interpretation and visualization. Multiple models were compared in terms of their predictive ability and their capability of reducing the search space for mineral exploration. The best-performing model, derived using a CNN that incorporates public geoscience datasets, exhibits an area under the curve for receiver operating characteristics plot of 0.96 for the testing labels, reducing the search area by 80%, while predicting all known carbonatite-hosted REE +/− Nb occurrences. The framework used in our study allows for an explicit definition of input vectors and provides a clear interpretation of outcomes generated by prospectivity models.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"10 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141334227","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-linear Response of Acoustic Emission and Electric Potential During Creep Failure of Coal under Stepwise Increasing Loads: Insights from Multifractal Theory","authors":"Dongming Wang, Enyuan Wang, Xiaofei Liu, Xiaojun Feng, Mingyao Wei, Dexing Li, Baolin Li, Quanlin Liu, Xin Zhang, Hengze Yang, Changfang Guo","doi":"10.1007/s11053-024-10366-w","DOIUrl":"https://doi.org/10.1007/s11053-024-10366-w","url":null,"abstract":"<p>The combination of acoustic emission and electrical potential monitoring methods holds promise for monitoring and warning of rock bursts due to its comprehensive reflection of the damage process. However, the response features during the creep failure process remain unclear. In this paper, a coal creep test was conducted using a combination of electric potential and acoustic emission monitoring. The response characteristics were analyzed, their multifractal characteristics were analyzed, and the joint response mechanism was explored. This research demonstrated a significant correlation among acoustic emission and electrical potential signals and creep deformation and failure. At the start of loading, a brief increase in both signals was observed. As deformation progressed, the signals became steady, and their intensity and fluctuation notably increased during accelerated creep failure. Quantitative analysis of acoustic emission count rates and electric potential intensity during creep processes revealed a quadratic relationship of acoustic emission count rates with stress and strain variations, in addition to an exponential correlation with mean electric potential intensity. Additionally, the statistical analysis of the multifractal characteristics before coal sample instability and failure revealed consistent trends in the characteristic values of Δ<i>α</i> and Δ<i>f</i>(<i>α</i>), with initial decrease followed by slight fluctuations, culminating in a sudden abnormal change preceding failure. Finally, leveraging the mechanisms of acoustic emission and electrification under load, this study discusses the multifractal characteristics of acoustic-electric signals and verifies their complementary roles in accurately predicting coal rock creep failure. These studies provide essential theoretical groundwork and references for improving dynamic disaster monitoring in coal mines.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"6 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141425503","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":"Characteristics and Evolution of Water-Occurrence in Coal Based on a New Classification Method","authors":"Ding Liu, Hao Xu, Dazhen Tang, Shida Chen, Fudong Xin, Heng Wu, Qiong Wang, Peng Zong, Tiantian Zhao","doi":"10.1007/s11053-024-10370-0","DOIUrl":"https://doi.org/10.1007/s11053-024-10370-0","url":null,"abstract":"<p>The presence of water in coal and its interaction plays pivotal roles in the storage and migration of coalbed methane (CBM), making it imperative to understand the water-occurrence across different coal ranks to guide CBM exploitation effectively. Here, a novel method for categorizing water into condensed and adsorbed forms based on their dehydration dynamics is proposed using differential thermogravimetric curve and the Arrhenius equation, offering a straightforward process and enabling the assessment of the interaction strength between water and coal. The result indicates that the total water capacity decreases initially before subsequently increasing as coal rank increases from 0.28 to 2.33%<i> R</i>_o,max, with the ratio of condensed water exhibiting an S-shaped curve. Remarkably, the condensed water capacity is correlated linearly with the total pore volume. The adsorbed water in low-rank coal is controlled primarily by the level of oxygen functional groups, whereas in medium-high rank coal it is controlled primarily by the specific surface area. Based on this, the controlling equations of water capacity and coal–water structure models were established. Additionally, coal–water interaction strength decreases significantly after the first coalification jump, with the strength of low-rank coal being approximately 2.54 times higher than that of medium-high rank coal. This discrepancy arises from the combined influence of multiple oxygen functional groups in low-rank coal on adsorbed water. This paper enhances the understanding of drainage process in coal reservoirs of varying ranks, which can facilitate the efficient extraction of CBM.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"43 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141333764","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":"Iron Ore Price Forecast based on a Multi-Echelon Tandem Learning Model","authors":"Weixu Pan, Shi Qiang Liu, Mustafa Kumral, Andrea D’Ariano, Mahmoud Masoud, Waqar Ahmed Khan, Adnan Bakather","doi":"10.1007/s11053-024-10360-2","DOIUrl":"https://doi.org/10.1007/s11053-024-10360-2","url":null,"abstract":"<p>Iron ore has had a highly global market since setting a new pricing mechanism in 2008. With current dollar values, iron ore concentrate for sale price, which was $39 per tonne (62% Fe) in December 2015, reached $218 per tonne (62% Fe) in mid-2021. It is hovering around $120 in October 2023 (cf. https://tradingeconomics.com/commodity/iron-ore). The uncertainty associated with these fluctuations creates hardship for iron ore mine operators and steelmakers in planning mine development and making future sale agreements. Therefore, iron ore price forecasting is of special importance. This paper proposes a cutting-edge multi-echelon tandem learning (METL) model to forecast iron ore prices. This model comprises variational mode decomposition (VMD), multi-head convolutional neural network (MCNN), stacked long short-term-memory (SLSTM) network, and attention mechanism (AT). In the proposed METL (i.e., the combination of VMD, MCNN, SLSTM, AT) model, the VMD decomposes the time series data into sub-sequential modes for better measuring volatility. Then, the MCNN is applied as an encoder to extract spatial features from the decomposed sub-sequential modes. The SLSTM network is adopted as a decoder to extract temporal features. Finally, the AT is employed to capture spatial–temporal features to obtain the complete forecasting process. Extensive computational experiments are conducted based on daily-based and weekly-based iron ore price datasets with different time scales. It was validated that the proposed METL model outperformed its single-echelon and other categorized models by 10–65% in range. The proposed METL model can improve the prediction accuracy of iron ore prices and thus help mining and steelmaking enterprises to determine their sale or purchase strategies.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"19 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319936","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":"Petrographic Characterization and Derivation of Sedimentary Environments and Coal Use from Petrographic Composition: Morupule, Mmamabula, and Mabesekwa Coalfields, Botswana","authors":"Kamogelo P. Keboletse, Freeman Ntuli, Oluseyi P. Oladijo","doi":"10.1007/s11053-024-10365-x","DOIUrl":"https://doi.org/10.1007/s11053-024-10365-x","url":null,"abstract":"<p>The Ecca equivalent coal deposits in the Morupule, Mmamabula, and Mabesekwa coalfields exist within the Kalahari Karoo Basin of the Karoo Super Group. Only the Morupule coal has proved its potential for power generation; while, the utilization value of the Mmamabula and Mabesekwa coals is yet to be determined. The current study presents petrographical characteristics of the three seams from each coalfield. Reflected light microscopy combined with scanning electron microscopy was used in the study. The analyses revealed that the Morupule and Mabesekwa coals are rich in inertinite; while, the Mmamabula coal is rich in vitrinite. The vitrinite reflectance indicates that the coal rank stretches between high volatile bituminous B and high volatile bituminous A. The compositions of macerals and coal facies indicate variations in the depositional conditions for the three studied coalfields. The Morupule and Mabesekwa coals were accumulated in a terrestrial bedmont dry forest swamp through fluvial vegetation transportation; while, the Mmamabula coal was deposited in a limnic upper plain wet forest swamp through alluvial vegetation transportation. Hydrological conditions were rheotrophic except in the Mmamabula South, which had ombrotrophic conditions. Based on petrographic compositions, coals from the Mmamabula East, Mmamabula South, Morupule East Main, Morupule West Main and Morupule South would be suitable for carbonization, gasification and liquefaction processes. However, several challenges with coals from the Mmamabula South, Mabesekwa Seam B, Mabesekwa Seam C and Mabesekwa Seam E would be encountered during carbonization, gasification and liquefaction due to high ash content and inert semifusinite content.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"6 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141326895","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":"Contribution to Groundwater Research in the World’s Largest Hot Desert: Hydrogeophysical Study for the Apprehension of the Jurassic Aquifer in the Tunisian “Sahara”","authors":"Ibtissem Makhlouf, Rihab Guellala, Rafika Ben Lasmar, Noomen Dkhaili, Lotfi Salmouna, Elkods Chahtour","doi":"10.1007/s11053-024-10364-y","DOIUrl":"https://doi.org/10.1007/s11053-024-10364-y","url":null,"abstract":"<p>Southern Tunisia belongs to the Sahara desert, one of the driest regions of the world, where groundwater research is crucial to satisfy the water demand. In this region, the Jurassic aquifer appears as a potential resource. Nevertheless, the related information is too limited to develop a suitable plan for exploitation. The present study aimed for a thorough understanding of the Jurassic series using borehole and seismic reflection data. Well logs from 40 petroleum boreholes were analyzed both qualitatively and quantitatively to define precisely the potential water reservoirs and determine their petrophysical characteristics. Comparison of the various recordings revealed the abundance of sandstone and dolomite deposits in the Sebaia Formation (Dogger–Malm) and the evaporitic composition of the Abreghs Formation (Lias). The Sebaia Formation is enriched in clays toward the south as indicated by well logs correlation and computed shale volumes (<i>V</i>_sh reaching 27.8%). The south-east part of Southern Tunisia contains mostly sandy Jurassic reservoirs, exhibiting the highest estimated porosities (22.8–31%). Lithostratigraphic correlations were established to firstly approach the geometry of the Jurassic aquifer. These correlations highlighted that the Jurassic series have variable depth and thickness along the Dahar structure, which thicken and deepen from the Dahar to the west and disappear in the Jeffara. These findings were further refined by the interpretation of 198 seismic profiles, which display several NW–SE-, E–W- and NE–SW-trending normal faults that influenced the Jurassic reservoirs depth, thickness, facies and petrophysical characteristics as well as groundwater circulation. The present study yielded interesting results that may enormously guide the investigation of the Jurassic aquifer in Southern Tunisia. Furthermore, it may be considered as an example for hydrogeophysical applications in the “Sahara” and other arid zones worldwide.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"26 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141251675","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":"Distribution Law of Occurrence State and Content Prediction of Deep CBM: A Case Study in the Ordos Basin, China","authors":"Cunlei Li, Zhaobiao Yang, Xia Yan, Guoxiao Zhou, Geoff Wang, Wei Gao, Changqing Liu, Benju Lu, Yuhui Liang","doi":"10.1007/s11053-024-10367-9","DOIUrl":"https://doi.org/10.1007/s11053-024-10367-9","url":null,"abstract":"<p>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 (<i>ρ</i>). By predicting key adsorption parameters and solving directly for adsorbed methane density (<i>ρ</i>_<i>a</i>), it is concluded that <i>ρ</i>_<i>a</i> 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 (<i>φ</i>) 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 (<i>V</i>_<i>fg</i>) calculation method for unconventional oil and gas reservoirs and the adsorbed gas content (<i>V</i>_<i>ad</i>) method based on <i>ρ</i>_<i>a</i>. It was observed that the <i>V</i>_<i>fg</i> increased with pressure and <i>φ</i> but decreased with increasing water saturation and temperature. However, as temperature and pressure increased, the rate of increase in <i>V</i>_<i>fg</i> slowed down, probably because of the influence of <i>φ</i> decreasing with increasing temperature and pressure, which is similar to the change in <i>ρ</i>_<i>a</i>. Meanwhile, the <i>V</i>_<i>ad</i> 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 <i>φ</i> increased to a specific value in low- to medium-rank coal, the <i>V</i>_<i>fg</i> can exceed the <i>V</i>_<i>ad</i> at depths between 2000 and 2500 m. Compared to high-rank coal, which has high <i>V</i>_<i>ad</i>, low- to medium-rank coals are more prone to experience the saturation phenomenon where the <i>V</i>_<i>fg</i> exceeds the <i>V</i>_<i>ad</i>.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"36 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141251738","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 and Correlation of Acoustic Emission and Resistance Parameters During Coal Fracture Propagation","authors":"Song Mingyang, Li Quangui, Hu Qianting, Zhang Yuebing, Xu Yangcheng, Hu Liangping, Zheng Xuewen, Zhao Zhengduo, Liu Suyu, Wang Mingjie","doi":"10.1007/s11053-024-10362-0","DOIUrl":"https://doi.org/10.1007/s11053-024-10362-0","url":null,"abstract":"<p>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.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"313 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141264842","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":"Property Evaluation of Metamorphic Rocks Using a New Metamorphic Reservoir Quality Index: Buried Hill of Bozhong 19-6 Area, Bohai Bay Basin, China","authors":"Xiaona Zhang, Yanbin Yao, Guibin Zhang, Ruying Ma, Zefan Wang, Veerle Vandeginste","doi":"10.1007/s11053-024-10368-8","DOIUrl":"https://doi.org/10.1007/s11053-024-10368-8","url":null,"abstract":"<p>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.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"72 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141246496","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}