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J., 1945, Analysis of Decline Curves, Transactions of the American Institute of Mining, Metallurgical and Petroleum Engineers, 1945, v. 160, p. 228–247.\nBabadagli, T., 2007, Development of mature oil fields — A review, Journal of Petroleum Science and Engineering, v. 57, no.3–4, p. 221–246.\nBentley, R., Mannan, S. A., and Wheeler, S. J., 2007, Assessing the date of the global oil peak: The need to use 2P reserves, Energy Policy, v. 35, no. 12, p. 6364–6382.\nCERA, 2007, Finding the critical numbers: what are the real decline rates for global oil production?: Private report, 21 p.\nCampbell, C., and Laherrère, J., 1998, The end of cheap oil: Scientific American, March 1998.\nCampbell, C. J., and Sivertsson, A., 2003, Updating the depletion model, in Proceedings of the Second International Workshop on Oil Depletion, Paris, France, 26–27 May 2003, 16 p. See also: http:\u002F\u002Fwww.peakoil.net\u002Fiwood2003\u002Fpaper\u002FCampbellPaper.doc.\nCampbell, C. J., and Heapes, S., 2008, An atlas of oil and gas depletion: Jeremy Mills Publishing, Lindley, 396 p.\nDoublet, L. E., Pande, P. K., McCollom, T. J., and Blasingame, T. A., 1994, Decline curve analysis using type curves-analysis of oil well production data using material balance time: application to field cases, Society of Petroleum Engineers Paper Presented at the International Petroleum Conference and Exhibition of Mexico, 10–13 October 1994, Veracruz, Mexico, SPE paper 28688-MS, 24 p.\nFeygin, M. V., and Ryzhik, V. M, 2001, Estimation of the Oil Reserves Requirement to Meet a Given Production Level - Mathematical Modeling, Natural Resources Research, Vol. 10, No. 1, p. 51–58.\nFetkovich, M. J., 1980, Decline Curve Analysis Using Type Curves, Journal of Petroleum Technology, v. 32, no. 6, p. 1065–1077.\nGowdy, J., Juliá, R., 2007, Technology and petroleum exhaustion: Evidence from two mega-oilfields, Energy, v. 32, no. 8, p. 1448–1454.\nHirsch, R., 2008, Mitigation of maximum world oil production: Shortage scenarios, Energy Policy, v. 36, no. 2, p. 881–889.\nHubbert, M. K., 1956, Nuclear energy and the fossil fuels, Publication no. 95: Shell Development Company, Houston, Texas, 40 p. See also: http:\u002F\u002Fwww.hubbertpeak.com\u002FHubbert\u002F1956\u002F1956.pdf.\nHurst, R., 1934, Unsteady flow of fluids in Oil Reservoirs, Journal of Applied Physics, v. 5, no. 20, p. 20–30.\nHöök, M., Aleklett, K., 2008, A decline rate study of Norwegian Oil Production, Energy Policy, 36(11): 4262–4271.\nHöök, M., Hirsch, R., and Aleklett, K., 2008, Giant oil field decline rates and their influence on world oil production: Global Energy Systems, report GES PP-09:2, see also: http:\u002F\u002Fwww.tsl.uu.se\u002Fuhdsg\u002FPublications\u002FGOF_decline_Article.pdf.\nIEA, 2008, World Energy Outlook 2008: see also: http:\u002F\u002Fwww.worldenergyoutlook.org\u002F.\nJakobsson, K., Söderbergh, B., Höök, M., and Aleklett, K., 2008, The maximum depletion rate model for forecasting oil production: its uses and misuses: Global Energy Systems, report PP-09:1, see also: http:\u002F\u002Fwww.tsl.uu.se\u002Fuhdsg\u002FPublications\u002FMDRM_Article.pdf.\nJames, K. H., 2000, The Venezuelan hydrocarbon habitat, part 2: hydrocarbon occurrences and generated-accumulated volumes, Journal of Petroleum Geology, v. 23 no.2, p. 133–164.\nLynch, M. C., (2003) Petroleum resources pessimism debunked in Hubbert model and Hubbert modeler’s assumptions, Oil Gas J. 101(27): 38–47.\nMueller, R. K., Eggert, D. J., Swanson, H. S., 1981, Petroleum decline analysis using time series, Energy Economics 3(4):256–267.\nMäkivierikko, A., 2007, Russian oil—a depletion rate model estimate of the future Russian oil production and export: Diploma thesis from Uppsala University, see also: http:\u002F\u002Fwww.tsl.uu.se\u002Fuhdsg\u002FPublications\u002FAram_Thesis.pdf.\nNehring, R., 1978, Giant oil fields and world oil resources: Report prepared by the RAND Corporation for the Central Intelligence Agency (CIA), see also: http:\u002F\u002Fwww.rand.org\u002Fpubs\u002Freports\u002F2006\u002FR2284.pdf.\nRobelius, F., 2007, Giant oil fields—the highway to oil: giant oil fields and their importance for future oil production: Doctoral thesis from Uppsala University, 156 p. See also: http:\u002F\u002Fpublications.uu.se\u002Fabstract.xsql?dbid=7625.\nSaudi-Aramco, 2004, Fifty-year crude oil supply scenarios: Saudi Aramco’s perspective. Presented by Mahmoud M. Abdul Baqi and Nansen G. Saleri, 24 February 2004 at Center for Strategic and International Studies, Washington, USA. Available from: http:\u002F\u002Fwww.csis.org\u002Fmedia\u002Fcsis\u002Fevents\u002F040224_baqiandsaleri.pdf.\nSimmons, M., 2002, The world’s giant oilfields: White paper, 9 January 2002, see also: http:\u002F\u002Fwww.simmonsco-intl.com\u002Ffiles\u002Fgiantoilfields.pdf.\nSimmons, M., 2005, Twilight in the desert: the coming Saudi oil shock and the world economy: Wiley, New york, 448 p.\nvan Everdingen, A. F., Hurst, W., 1949, The application of the Laplace transformation to flow problems in reservoirs. Transactions of the American Institute of Mining, Metallurgical and Petroleum Engineers 186:305–324.\nZittel, W., 2001, Analysis of the UK oil production. Contribution to the Association for the Study of Peak Oil & Gas (ASPO), see also: http:\u002F\u002Fwww.peakoil.net\u002FPublications\u002F06_Analysis_of_UK_oil_production.pdf.",{"EN":199},"The giant oil fields of the world are only a small fraction of the total number of fields, but their importance is huge. Over 50% of the world’s oil production came from giants by 2005 and more than half of the world’s ultimate reserves are found in giants. Based on this, it is reasonable to assume that the future development of the giant oil fields will have a significant impact on the world oil supply. In order to better understand the giant fields and their future behavior, one must first understand their history. This study has used a comprehensive database on giant oil fields in order to determine their typical parameters, such as the average decline rate and life-times of giants. The evolution of giant oil field behavior has been investigated to better understand future behavior. One conclusion is that new technology and production methods have generally led to high depletion rates and rapid decline. The historical trend points towards high decline rates of fields currently on plateau production. The peak production generally occurs before half the ultimate reserves have been produced in giant oil fields. A strong correlation between depletion-at-peak and average decline rate is also found, verifying that high depletion rate leads to rapid decline. Our result also implies that depletion analysis can be used to rule out unrealistic production expectations from a known reserve, or to connect an estimated production level to a needed reserve base.",{"EN":201},"The Evolution of Giant Oil Field Production Behavior",{"VOID":203},"10.1007\u002Fs11053-009-9087-z","PUBLICATION","VERIFIED","Auto 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R. M., & Clarkson, C. R. (1999). Free gas storage in matrix porosity: a potentially significant coalbed resource in low rank coals. In Proceedings International Coalbed Methane Symposium (pp. 197–214).\nCharrière, D., & Behra, P. (2010). Water sorption on coals. Journal of Colloid and Interface Science, 344(2), 460–467.\nChen, D., Pan, Z., Liu, J., & Connell, L. D. (2012). Modeling and simulation of moisture effect on gas storage and transport in coal seams. Energy and Fuels, 26(3), 1695–1706.\nCrosdale, P. J., Moore, T. A., & Mares, T. E. (2008). Influence of moisture content and temperature on methane adsorption isotherm analysis for coals from a low-rank, biogenically-sourced gas reservoir. International Journal of Coal Geology, 76(1–2), 166–174.\nDay, S., Sakurovs, R., & Weir, S. (2008). Supercritical gas sorption on moist coals. International Journal of Coal Geology, 74(3–4), 203–214.\nFu, H., Yan, D., Su, X., Wang, J., Li, Q., Li, X., Zhao, W., Zhang, L., Wang, X., & Li, Y. (2022). Biodegradation of early thermogenic gas and generation of secondary microbial gas in the Tieliekedong region of the northern Tarim Basin, NW China. International Journal of Coal Geology, 261, 104075.\nFu, H., Yan, D., Yang, S., Wang, X., Wang, G., Zhuang, X., Zhang, L., Li, G., Chen, X., & Pan, Z. (2021). A study of the gas–water characteristics and their implications for the coalbed methane accumulation modes in the Southern Junggar Basin, China. AAPG Bulletin, 105(1), 189–221.\nHan, L., Shen, J., Wang, J., & Shabbiri, K. (2021). Characteristics of pore evolution and its maceral contributions in the huolinhe lignite during coal pyrolysis. Natural Resources Research, 30(3), 2195–2210.\nJian, S., Lei, D., Yong, Q., Peng, Y., Xuehai, F., & Gang, C. (2015). Three-phase gas content model of deep low-rank coals and its implication for CBM exploration: A case study from the Jurassic coal in the Junggar Basin. Natural Gas Industry, 35(03), 30–35.\nLi, C., Yang, Z., Chen, J., & Sun, H. (2022). Prediction of critical desorption pressure of coalbed methane in multi-coal seams reservoir of medium and high coal rank: A case study of eastern Yunnan and western Guizhou, China. Natural Resources Research, 31(3), 1443–1461.\nLi, G., Qin, Y., Yao, Z., & Hu, W. (2021). Differentiation of carbon isotope composition and stratabound mechanism of gas desorption in shallow-buried low-rank multiple coal seams: Case study of well DE-A, Northeast Inner Mongolia. Natural Resources Research, 30(2), 1511–1526.\nLi, X., Fu, X., Liu, A., An, H., Wang, G., Yang, X., Wang, L., & Wang, H. (2016). Methane adsorption characteristics and adsorbed gas content of low-rank coal in China. Energy and Fuels, 30(5), 3840–3848.\nLievens, C., Ci, D., Bai, Y., Ma, L., Zhang, R., Chen, J. Y., Gai, Q., Long, Y., & Guo, X. (2013). A study of slow pyrolysis of one low rank coal via pyrolysis-GC\u002FMS. Fuel Processing Technology, 116, 85–93.\nLiu, A., Fu, X., Wang, K., An, H., & Wang, G. (2013). Investigation of coalbed methane potential in low-rank coal reservoirs—Free and soluble gas contents. Fuel, 112, 14–22.\nMallick, N., & Prabu, V. (2017). Energy analysis on Coalbed Methane (CBM) coupled power systems. Journal of CO2 Utilization, 19, 16–27.\nMcCutcheon, A. L., Barton, W. A., & Wilson, M. A. (2003). Characterization of water adsorbed on bituminous coals. Energy and Fuels, 17(1), 107–112. https:\u002F\u002Fdoi.org\u002F10.1021\u002Fef020101d\nObasi, C., & Pashin, J. (2018). Effects of internal gradients on pore-size distribution in shale. AAPG Bulletin, 102(9), 1825–1840.\nOu, C., Li, C., Zhi, D., Xue, L., & Yang, S. (2018). Coupling accumulation model with gas-bearing features to evaluate low-rank coalbed methane resource potential in the southern Junggar Basin. China. AAPG Bulletin, 102(1), 153–174.\nPratt, T. J., Mavor, M. J., & Debruyn, R. P. (1999). Coal gas resource and production potential of subbituminous coal in the powder river basin. In Society of Petroleum Engineers - SPE Rocky Mountain Regional Meeting 1999, RMR 1999.\nRen, P., Wang, Q., Tang, D., Xu, H., & Chen, S. (2022). In situ stress-coal structure relationship and its influence on hydraulic fracturing: A case study in Zhengzhuang area in Qinshui Basin, China. Natural Resources Research, 31(3), 1621–1646.\nRoss, H. E., Hagin, P., & Zoback, M. D. (2009). CO2 storage and enhanced coalbed methane recovery: Reservoir characterization and fluid flow simulations of the Big George coal, Powder River Basin, Wyoming, USA. International Journal of Greenhouse Gas Control, 3(6), 773–786.\nSampath, K. H. S. M., Perera, M. S. A., Elsworth, D., Ranjith, P. G., Matthai, S. K., & Rathnaweera, T. (2018). Experimental investigation on the mechanical behavior of Victorian brown coal under brine saturation. Energy and Fuels, 32(5), 5799–5811.\nShi, J., Jia, Y., Wu, J., Xu, F., Sun, Z., Liu, C., Meng, Y., Xiong, X., & Liu, C. (2021a). Dynamic performance prediction of coalbed methane wells under the control of bottom-hole pressure and casing pressure. Journal of Petroleum Science and Engineering, 196, 107799.\nShi, J., Wu, J., Lv, M., Li, Q., Liu, C., Zhang, T., Sun, Z., He, M., & Li, X. (2021b). A new straight-line reserve evaluation method for water bearing gas reservoirs with high water production rate. Journal of Petroleum Science and Engineering, 196, 107808.\nTaylor, G. H., Teichmüller, M., Davis, A., Diessel, C. F. K., Littke, R., & Robert, P. (1998). Organic petrology.\nWang, K. (2010). Physical simulation and numerical simulation of adsorbed state, soluble state and free state gas volume in low rank coal reservoir. China Uni. Min. Techno.\nXin, F., Xu, H., Tang, D., & Cao, L. (2019a). Properties of lignite and key factors determining the methane adsorption capacity of lignite: New insights into the effects of interlayer spacing on adsorption capacity. Fuel Processing Technology, 196, 106181.\nXin, F., Xu, H., Tang, D., & Cao, L. (2020a). An improved method to determine accurate porosity of low-rank coals by nuclear magnetic resonance. Fuel Processing Technology, 205(February), 106435.\nXin, F., Xu, H., Tang, D., Chen, Y., Cao, L., & Yuan, Y. (2020b). Experimental study on the change of reservoir characteristics of different lithotypes of lignite after dehydration and improvement of seepage capacity. Fuel, 277, 118196.\nXin, F., Xu, H., Tang, D., Liu, D., & Cao, C. (2021). Problems in pore property testing of lignite: Analysis and correction. International Journal of Coal Geology, 245, 103829.\nXin, F., Xu, H., Tang, D., Yang, J., Chen, Y., Cao, L., & Qu, H. (2019b). Pore structure evolution of low-rank coal in China. International Journal of Coal Geology, 205, 126–139. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.coal.2019.02.013\nXu, H., Tang, D., Chen, Y., Ming, Y., Chen, X., Qu, H., Yuan, Y., Li, S., & Tao, S. (2018). Effective porosity in lignite using kerosene with low-field nuclear magnetic resonance. Fuel, 213, 158–163.\nYang, Z., Qin, Z., Wang, G., & Li, C. (2021). Environmental effects of water product from coalbed methane wells: A case study of the Songhe well group, western Guizhou, China. Natural Resources Research, 30(5), 3747–3760.\nZhang, Z., Qin, Y., You, Z., & Yang, Z. (2021). Distribution characteristics of in situ stress field and vertical development unit division of CBM in western Guizhou. China. Natural Resources Research, 30(5), 3659–3671.\nZhao, J., Xu, H., Tang, D., Mathews, J. P., Li, S., & Tao, S. (2016). Coal seam porosity and fracture heterogeneity of macrolithotypes in the Hancheng Block, eastern margin, Ordos Basin, China. International Journal of Coal Geology, 159, 18–29.\nZhao, J., Shen, J., Qin, Y., Wang, J., Zhao, J., & Li, C. (2021). Coal petrology effect on nanopore structure of lignite: Case study of no. 5 coal seam, Shengli Coalfield, Erlian Basin, China. Natural Resources Research, 30(1), 681–695.\nZhong, J., Meng, Y., Liu, Z., & Zeng, F. (2022). A novel method for the intelligent recognition of lattice fringes in coal HRTEM images based on semantic segmentation and fuzzy superpixels. ACS Omega, 7(17), 15037–15047.\nZhu, J. F., Liu, J. Z., Yang, Y. M., Cheng, J., Zhou, J. H., & Cen, K. F. (2016). Fractal characteristics of pore structures in 13 coal specimens: Relationship among fractal dimension, pore structure parameter, and slurry ability of coal. Fuel Processing Technology, 149, 256–267.",{"EN":309},"The reservoir properties and gas-bearing characteristics of different lithotypes of lignite are different, resulting in complex migration and accumulation laws of methane in lignite. Following systematic collections of samples of different lithotypes from the Erlian Basin, occurrence modes and storage potential of methane in lignite were explored through a series of isothermal adsorption experiments and NMR-based experiments on original water-containing samples. The maceral composition affects the reservoir characteristics and hydrophilicity of different lithotypes of lignite, which control the reservoir’s gas–water competition. Xylite lignite has a strong adsorption capacity, poor development of macropores, and high irreducible water content. Therefore, among various lithotypes of lignite, xylite lignite has the highest occurrence potential for adsorbed gas and soluble gas and the lowest potential for free gas. Notably, the soluble gas in lignite is never dominant in the gas composition. Therefore, gas in xylite lignite is mainly adsorbed. Due to carbonization, the fusain-rich lignite retains many unexpanded primary plant tissue structures and has developed macropore spaces and weak hydrophilicity. Therefore, the fusain-rich lignite has high free fluid porosity and the highest free gas storage potential. When the burial depth of the matrix lignite is less than 500 m, the methane is mainly adsorbed. The storage potential of free gas gradually exceeds that of adsorbed gas as the burial depth increases. There are apparent differences in the occurrence states and accumulation patterns of methane in different lithotypes of lignite. Clarifying methane’s occurrence and storage potential in different lithotypes of lignite are significant for evaluating methane resources and exploring the methane enrichment model.",{"EN":311},"Storage Potential of Multi-State Fluids in Different Lithotypes of Lignite: An In Situ Water-Gas-Bearing Analysis Based on Nuclear Magnetic Resonance",{"VOID":313},"10.1007\u002Fs11053-023-10172-w","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11053-023-10172-w",[316,331,346,358],{"id":317,"sortIndex":161,"researcher":18,"roles":318,"affiliations":319,"properties":328},"c31559b1-eaf2-43e2-9e0c-c4e99be277e6",[212],[320],{"id":18,"sortIndex":19,"affiliation":321,"properties":18},{"id":322,"createTime":323,"updateTime":323,"relativeEntities":324,"slug":18,"properties":325,"entityType":37,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"a9a1a17a-2d6b-4c65-9b65-80ac0826f4b3","2023-12-27T17:32:08.082+00:00",[],{"title":326},{"VI":327},"School of Energy Resources, China University of Geosciences (Beijing), Beijing, China",{"title":329},{"VI":330},"Hao Xu",{"id":332,"sortIndex":239,"researcher":18,"roles":333,"affiliations":334,"properties":343},"5558bc68-b473-4e97-951f-2d6e7365d2a4",[212],[335],{"id":18,"sortIndex":19,"affiliation":336,"properties":18},{"id":337,"createTime":338,"updateTime":338,"relativeEntities":339,"slug":18,"properties":340,"entityType":37,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"ccde0c9c-dc69-4856-93e2-d0eb1b0eb394","2024-01-20T11:40:30.498+00:00",[],{"title":341},{"VI":342},"Executive Leadership Academy, Ministry of Emergency Management of China, Beijing, China",{"title":344},{"VI":345},"Can Cao",{"id":347,"sortIndex":147,"researcher":18,"roles":348,"affiliations":349,"properties":355},"824cf4ac-f0af-4269-90bb-689c1738b871",[212],[350],{"id":18,"sortIndex":19,"affiliation":351,"properties":18},{"id":322,"createTime":323,"updateTime":323,"relativeEntities":352,"slug":18,"properties":353,"entityType":37,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":354},{"VI":327},{"title":356},{"VI":357},"Dazhen Tang",{"id":359,"sortIndex":19,"researcher":18,"roles":360,"affiliations":361,"properties":389},"61a73eec-33ee-48ed-bda9-94ea0fae1b13",[212],[362,372,379],{"id":363,"sortIndex":147,"affiliation":364,"properties":371},"48c3087a-055f-4798-8dae-6010db1a27a1",{"id":365,"createTime":366,"updateTime":366,"relativeEntities":367,"slug":18,"properties":368,"entityType":37,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"296cb67d-909f-4a55-9f17-40b309387966","2024-01-11T18:04:56.243+00:00",[],{"title":369},{"VI":370},"PetroChina Shenzhen New Energy Research Institute, Shenzhen, China",{},{"id":373,"sortIndex":161,"affiliation":374,"properties":378},"222f474e-dd84-4da2-ba97-9bb6f9a6ae24",{"id":322,"createTime":323,"updateTime":323,"relativeEntities":375,"slug":18,"properties":376,"entityType":37,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":377},{"VI":327},{},{"id":18,"sortIndex":19,"affiliation":380,"properties":18},{"id":381,"createTime":382,"updateTime":383,"relativeEntities":384,"slug":385,"properties":386,"entityType":37,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"aee60a32-4852-4cf9-b7eb-c0e3845ed051","2024-01-10T02:07:58.005+00:00","2024-12-25T19:32:17.436+00:00",[],"PetroChina-Research-Institute-of-Petroleum-Exploration-Development-Beijing-China",{"title":387},{"VI":388},"PetroChina Research Institute of Petroleum Exploration & Development, Beijing, China",{"title":390},{"VI":391},"Fudong Xin",{"url":314,"publisher":393,"properties":420},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":394,"slug":10,"properties":395,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":398,"manageAffiliations":399,"indexDatabases":400,"url":18,"thumbnailPath":18,"statistic":415,"gsStatistic":18,"type":18,"analyzePriority":18},[],{"issn":396,"title":397},{"VOID":13},{"EN":15},[],[],[401,408],{"id":53,"indexDatabase":402,"url":66,"indexYears":67,"academicFieldIds":407,"indexDatabaseRanking":70},{"id":55,"createTime":56,"updateTime":57,"relativeEntities":403,"label":404,"description":405,"key":63,"publicationTags":406,"standard":18},[],{"EN":60,"VI":60},{"EN":60,"VI":62},[65],[69],{"id":72,"indexDatabase":409,"url":87,"indexYears":18,"academicFieldIds":414,"indexDatabaseRanking":18},{"id":74,"createTime":75,"updateTime":76,"relativeEntities":410,"label":411,"description":412,"key":83,"publicationTags":413,"standard":18},[],{"EN":79,"VI":79},{"VI":81,"EN":82},[85,86],[89],{"impactFactor":19,"impactFactorByYear":416,"i10Index":103,"i10IndexLast5Year":104,"totalPublication":105,"totalPublicationByYear":417,"totalCitation":133,"totalCitationByYear":418,"totalCitationPerPublication":157,"totalCitationPerPublicationByYear":419,"hindexLast5Year":125,"hindex":125},{"2012":92,"2013":92,"2014":93,"2015":94,"2016":95,"2017":96,"2018":97,"2019":98,"2020":99,"2021":100,"2022":101,"2023":102},{"1992":107,"1993":108,"1994":109,"1995":110,"1996":111,"1997":112,"1998":113,"1999":114,"2000":115,"2001":116,"2002":115,"2003":117,"2004":118,"2005":119,"2006":120,"2007":121,"2008":110,"2009":122,"2010":123,"2011":124,"2012":111,"2013":125,"2014":119,"2015":122,"2016":113,"2017":126,"2018":127,"2019":128,"2020":129,"2021":130,"2022":131,"2023":132,"2024":109},{"1992":135,"1993":136,"1994":109,"1995":125,"1996":137,"1997":117,"1998":138,"2004":139,"2005":140,"2007":141,"2008":142,"2009":143,"2010":144,"2011":145,"2012":146,"2013":147,"2014":148,"2015":145,"2016":127,"2017":149,"2018":150,"2019":151,"2020":152,"2021":153,"2022":154,"2023":155,"2024":156},{"1992":159,"1993":160,"1994":161,"1995":162,"1996":163,"1997":164,"1998":165,"2004":166,"2005":167,"2007":168,"2008":169,"2009":170,"2010":171,"2011":172,"2012":173,"2013":174,"2014":175,"2015":176,"2016":177,"2017":178,"2018":179,"2019":180,"2020":181,"2021":182,"2022":183,"2023":98,"2024":184},{"volume":421,"pages":423},{"VOID":422},"32",{"VOID":424},"1199-1214","2023-02-28",2023,{"id":428,"createTime":429,"updateTime":430,"relativeEntities":431,"slug":432,"properties":433,"entityType":204,"verifyStatus":205,"verifyTime":430,"verifyNote":206,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":442,"fullTextUrl":18,"authors":443,"publicationType":262,"publisherRelationship":476,"citationCount":18,"citationInfo":18,"publishDate":509,"publishYear":510,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":18,"openAccess":18,"references":18,"isForceReanalyzing":298},"e09fa4c4-df71-436d-993e-e8f5df7bbfe7","2023-12-24T02:23:29.419+00:00","2025-01-10T23:56:27.261+00:00",[],"Application-of-Land-Magnetic-and-Geoelectrical-Techniques-for-Delineating-Groundwater-Aquifer-Case-Study-in-East-Oweinat-Western-Desert-Egypt",{"references":434,"abstract":436,"title":438,"doi":440},{"VOID":435},"Al Temamy, A. M. M., & Barseem, M. S. M. (2010). Structural impact on the groundwater occurrence in the Nubia sandstone aquifer using geomagnetic and geoelectrical techniques, northwest Bir Tarfawi, east El Oweinat area, Western Desert. EGS Journal, 8(1), 47–63.\nAraffa, S. A. S. (2013). Delineation of under groundwater aquifer and subsurface structures on north Cairo Egypt, used integrated interpretation of magnetic, gravity, and geoelectrical data. Geophysical Journal International, 192(1), 94–112.\nAraffa, S. A. S., Abdelazium, M., Sabet, H. S., & Al Dabour, A. (2021). Hydrogeophysical investigation at El Moghra area, North Western Desert, Egypt. Environmental Earth Sciences, 80(2), 55–80.\nAraffa, S. A. S., Alrefaee, H. A., & Nagy, M. (2020). Potential of groundwater occurrence using geoelectrical and magnetic data: A case study from south Wadi Hagul area, the northern part of the Eastern Desert, Egypt. Journal of African Earth Sciences, 172, 103970.\nAraffa, S. A. S., El Shayeb, H. M., Abu-Hashish, M. F., & Hassan, N. M. (2015). Integrated geophysical interpretation for delineating the structural elements and under groundwater aquifers at the central part of Sinai Peninsula. Arabian Journal of Geosciences, 8, 7993–8007.\nArchie, G. E. (1942). The electrical resistivity log as an aid in determining some reservoir characteristics. Transactions of the AIME, 146, 54–62.\nAttia, O. E. A., & Hussien, K. H. (2015). Sedimentological characteristics of continental sabkha, south Western Desert, Egypt. Arabian Journal of Geosciences , 8, 7973–7991.\nAweto, K. E. (2013). Resistivity methods in hydrogeophysical investigation for groundwater in Aghalokpe, Western Niger Delta. Global Journal of Geological Science., 11, 47–55.\nBaranov, V., & Naudy, H. (1964). Numerical calculation of the formula of reduction to the magnetic pole. Geophysics, 29, 67–79.\nBobachev, A, Modin, I., & Shevinin, V. (2008). IPI2Win V2.0: user’s Guide. Moscow State University, Moscow. http:\u002F\u002Fgeophys.geol.msu.ru\u002Fipi2win.htm.\nCONOCO. (1987). Geological map of Egypt, scale 1:500,000.\nEl Osta, M. M. (2006). Evaluation and management of groundwater in East El Oweinat Area, Western Desert, Egypt. Ph.D. Thesis. Geology Department, Faculty of Science, Minufiya University.\nEL-Badrawy, H. T., Araffa, S. A. S., & Gabr, A. F. (2021). Application of the multi-potential geophysical techniques for under groundwater evaluation in a part of central Sinai Peninsula Egypt. Acta Geodynamics Et Geomaterialia, 18(1), 61–70.\nElbarbary, S., Araffa, S. A. S., El-Shahat, A., AbdelZaher, M., & Khedher, K. M. (2021). Delineation of water potentiality areas at Wadi El-Arish, Sinai, Egypt, using hydrological and geophysical techniques. Journal of African Earth Sciences, 174, 104056.\nGoldman, M., & Neubauer, F. M. (1994). Groundwater exploration using integrated geophysical techniques. Survey in Geophysics, 15, 331–361.\nHendriks, F., Luger, P., Bowitz, J., & Kallenbach, H. (1987). Evolution of the depositional environments of the SE-Egypt during the cretaceous and lower tertiary. Subproject A3 “Sedimentary basin of Southern Egypt.” Berl. Geowiss., 75, 49–80.\nIssawi, B. (1971). The geology of Darb El-Arbeain, Western Desert, Egypt. Annals of the Geological Survey of Egypt, 1, 53–92.\nIssawi, B. (1973). Nubia sandstone type section. Bulletin AAPG, 57, 741–745.\nKlitzsch, E. (1978). Geologische Bearbeitung Sudwest-Agyptens. Geologische Rundschau, 67, 509.\nKlitzsch, E., & Lejal-Nicol, A. (1984). Flora and Fauna from Strata in Southern Egypt and Northern Sudan. Berliner Geowissenschaftliche Abhandlungen, 50, 47–79.\nMasoud, M. H., Schneider, M., & El Osta, M. M. (2013). Recharge flux to the Nubian Sandstone aquifer and its impact on the present development in southwest Egypt. Journal of African Earth Sciences, 85, 115–124.\nOasis Montaj Programs. (2015). Geosoft mapping and processing system: Version 8.3.2 (HJ), Inc Suit 500, Richmond St. West Toronto, ON Canada N5SIV6.\nParker, R. L. (1973). The rapid calculation of potential anomalies. Geophysical Journal International, 31(4), 447–455.\nWinsauer, W. O., Shearin, H. M., Jr., Masson, P. H., & Williams, M. (1952). Resistivity of brine saturated sands in relation to pore geometry. American Association of Petroleum Geologists Bulletin, 36(2), 253–277.",{"EN":437},"Two geophysical tools were used to delineate the configuration of the Nubian sandstone aquifer in the study area. Three hundred magnetic points were measured and analyzed to evaluate the subsurface structural setting and to trace the basement relief, which control the aquifer’s geometry. The magnetic interpretations refer to dominant faults that strike in various directions, namely N–S, NE–SW, and NW–SE. The top of the basement complex was recorded at depths of 384–1286 m, and the aquifer thickness ranged from 299 to 1169 m. Thirty vertical electrical sounding points of AB\u002F2 with depths ranging from 1.5 to 700 m were used to estimate the parameters of the Nubian sandstone aquifer. The geoelectrical data indicate that the area consists of 5 units; the first unit is composed of sand and gravel, the second unit of ferruginous sandstone, the third unit of clay, the fourth unit of dry sandstone, and the last unit of sandstone saturated with groundwater. The groundwater in the study area is freshwater of high quality usable for all purposes.",{"EN":439},"Application of Land Magnetic and Geoelectrical Techniques for Delineating Groundwater Aquifer: Case Study in East Oweinat, Western Desert, Egypt",{"VOID":441},"10.1007\u002Fs11053-021-09937-y","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11053-021-09937-y",[444,459],{"id":445,"sortIndex":161,"researcher":18,"roles":446,"affiliations":447,"properties":456},"38a96908-0364-45dd-9739-c3ae38b1d541",[212],[448],{"id":18,"sortIndex":19,"affiliation":449,"properties":18},{"id":450,"createTime":451,"updateTime":451,"relativeEntities":452,"slug":18,"properties":453,"entityType":37,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"3c36965c-0e36-4260-a50b-d120dbf39483","2023-12-24T02:23:29.459+00:00",[],{"title":454},{"VI":455},"National Water Research Center (NWRC), Research Institute for Ground Water (RIGW), Cairo, Egypt",{"title":457},{"VI":458},"Sayed Bedair",{"id":460,"sortIndex":19,"researcher":18,"roles":461,"affiliations":462,"properties":473},"5066dcc4-e866-4f80-a83c-63a5afb94110",[212],[463],{"id":18,"sortIndex":19,"affiliation":464,"properties":18},{"id":465,"createTime":466,"updateTime":467,"relativeEntities":468,"slug":469,"properties":470,"entityType":37,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"16dcb792-fcb5-435e-8ab8-4f749df1558d","2024-01-16T08:05:19.371+00:00","2024-10-06T12:14:25.744+00:00",[],"National-Research-Institute-of-Astronomy-and-Geophysics-Helwan-Cairo-Egypt",{"title":471},{"VI":472},"National Research Institute of Astronomy and Geophysics, Helwan, Cairo, Egypt",{"title":474},{"VI":475},"Sultan Awad Sultan 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H., Holsapple, C. W. and Whinston, A. B., 1981, Foundations of decision support systems, NY, Academic Press.\nEom, H. B. and Lee, S. M., 1990, A survey of decision support system applications (1971–1988), Interfaces, v. 20, n. 3, p. 65–79.\nKeen, P. W. and Scott-Morton, M. S., 1978, Decision support systems: An organizational perspective, Reading, MS, Addison-Wesley.\nKeeney, R. L. and Raiffa, H., 1976, Decisions with multiple objectives: Preference and value tradeoffs, NY, John Wiley and Sons.\nSilver, M. S., 1990, Decision support systems: Directed and non-directed change, Information Systems Research, v. 1, n. 1, p. 47–70.\nSprague, R. H. and Carlson, E. D., 1982, Building effective decision support systems, Englewood Cliffs, NJ, Prentice-Hall.\nThompson, R. S., 1993, Integrated design by design: Educating for multidisciplinary teamwork, Society of Petroleum Engineers Paper No. 26376, presented at the Sixty-eightieth Annual Technical Conference, Houston, Texas, October 3–6.\nWalls, M. R., 1995, Integrating business strategy and capital allocation: an application of multi-objective decision making,” Engineering Economist, v. 40, n. 3, p. 247–266.",{"EN":591},"Petroleum exploration companies enter the twenty first century facing an increasingly competitive and risky environment. Under those circumstances, there is a growing need for better systematic decision-making that explicitly embodies the firm's desired goals and resource constraints. Computer-aided decision making, or decision support systems (DSS), provide an aid for those exploration management problems that are large, complex, unstructured, and involve management mudgment. Almost every present day DSS falls into one of two general classes. Vehicle DSSs such as linear\u002Fnonlinear programming models and other optimization routines, propose and impose specific methodologies to the decision-maker. On the other hand, toolbox DSSs, such as simulation programs, statistical functions, and graphical packages, are generally flexible in enabling their users to employ a variety of approaches and tools for their decision tasks but provide little guidance on both problem representation and investigation. This paper describes the development of a hybrid DSS model that combines the advantages of both the vehicle and toolbox systems components to provide a comprehensive approach to exploration planning from geological development through the capital allocation process. The Exploration Decision Support System (EDSS) preserves the flexibility of the toolbox system while enriching the problem-solving strategies available to the firm. The central objectives for developing an EDSS framework are: (1) better decisions about resource allocations; (2) more systematic understanding of the factors affecting exploration decisions; (3) improved communication about E&P performance objectives and constraints at all levels of decision-making; and (4) an explicit vehicle for continuous improvement of the petroleum exploration firm's decision-making process. The EDSS model can guide geological and exploration managers toward a more formal evaluation of projects, provide insight into the impact of competing choice alternatives, and significantly improve the quality of exploration decisions.",{"EN":593},"Developing an exploration decision support system (EDSS): A strategy for combining information and analytics",{"VOID":595},"10.1007\u002FBF02257661","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002FBF02257661",[598],{"id":599,"sortIndex":19,"researcher":18,"roles":600,"affiliations":601,"properties":610},"e9ad081c-1f19-49ca-a017-65991c6a15f1",[212],[602],{"id":18,"sortIndex":19,"affiliation":603,"properties":18},{"id":604,"createTime":605,"updateTime":605,"relativeEntities":606,"slug":18,"properties":607,"entityType":37,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"10178145-5bc6-4229-b8d5-715421ff4e5c","2024-01-23T08:56:12.821+00:00",[],{"title":608},{"VI":609},"Division of Economics and Business, Colorado School of Mines, Golden",{"title":611},{"VI":612},"Michael R. 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Radial Basis Function Link Networks (RBFLN) were used as a data-driven method for GIS-based predictive mapping of Proterozoic mineralization in this area. To generate the input data for RBFLN, the evidential maps comprising stratigraphic, structural, geophysical, and geochemical data were used. Fifty-eight deposits and 58 ‘nondeposits’ were used to train the network. The operations for the application of neural networks employed in this study involve both multiclass and binary representation of evidential maps. Running RBFLN on different input data showed that an increase in the number of evidential maps and classes leads to a larger classification sum of squared error (SSE). As a whole, an increase in the number of iterations resulted in the improvement of training SSE. The results of applying RBFLN showed that a successful classification depends on the existence of spatially well distributed deposits and nondeposits throughout the study area.",{"EN":660},"Application of Radial Basis Functional Link Networks to Exploration for Proterozoic Mineral Deposits in Central Iran",{"VOID":662},"10.1007\u002Fs11053-007-9036-7",[664],"EN","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11053-007-9036-7",[667],{"id":668,"sortIndex":19,"researcher":18,"roles":669,"affiliations":670,"properties":679},"84772b4f-2b19-4e56-8069-4effdb3e38e0",[],[671],{"id":18,"sortIndex":19,"affiliation":672,"properties":18},{"id":673,"createTime":674,"updateTime":674,"relativeEntities":675,"slug":18,"properties":676,"entityType":37,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"f2dd9fac-e0eb-4548-a374-7afc68764de1","2024-01-13T16:15:06.503+00:00",[],{"title":677},{"VI":678},"Geomatics Department, Geological Survey of Iran, Tehran, Iran",{"title":680,"email":682},{"EN":681},"Pouran Behnia",{"VOID":683},"pouranb@yahoo.com",{"url":18,"publisher":685,"properties":18},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":686,"slug":10,"properties":687,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":690,"manageAffiliations":691,"indexDatabases":692,"url":18,"thumbnailPath":18,"statistic":707,"gsStatistic":18,"type":18,"analyzePriority":18},[],{"issn":688,"title":689},{"VOID":13},{"EN":15},[],[],[693,700],{"id":53,"indexDatabase":694,"url":66,"indexYears":67,"academicFieldIds":699,"indexDatabaseRanking":70},{"id":55,"createTime":56,"updateTime":57,"relativeEntities":695,"label":696,"description":697,"key":63,"publicationTags":698,"standard":18},[],{"EN":60,"VI":60},{"EN":60,"VI":62},[65],[69],{"id":72,"indexDatabase":701,"url":87,"indexYears":18,"academicFieldIds":706,"indexDatabaseRanking":18},{"id":74,"createTime":75,"updateTime":76,"relativeEntities":702,"label":703,"description":704,"key":83,"publicationTags":705,"standard":18},[],{"EN":79,"VI":79},{"VI":81,"EN":82},[85,86],[89],{"impactFactor":19,"impactFactorByYear":708,"i10Index":103,"i10IndexLast5Year":104,"totalPublication":105,"totalPublicationByYear":709,"totalCitation":133,"totalCitationByYear":710,"totalCitationPerPublication":157,"totalCitationPerPublicationByYear":711,"hindexLast5Year":125,"hindex":125},{"2012":92,"2013":92,"2014":93,"2015":94,"2016":95,"2017":96,"2018":97,"2019":98,"2020":99,"2021":100,"2022":101,"2023":102},{"1992":107,"1993":108,"1994":109,"1995":110,"1996":111,"1997":112,"1998":113,"1999":114,"2000":115,"2001":116,"2002":115,"2003":117,"2004":118,"2005":119,"2006":120,"2007":121,"2008":110,"2009":122,"2010":123,"2011":124,"2012":111,"2013":125,"2014":119,"2015":122,"2016":113,"2017":126,"2018":127,"2019":128,"2020":129,"2021":130,"2022":131,"2023":132,"2024":109},{"1992":135,"1993":136,"1994":109,"1995":125,"1996":137,"1997":117,"1998":138,"2004":139,"2005":140,"2007":141,"2008":142,"2009":143,"2010":144,"2011":145,"2012":146,"2013":147,"2014":148,"2015":145,"2016":127,"2017":149,"2018":150,"2019":151,"2020":152,"2021":153,"2022":154,"2023":155,"2024":156},{"1992":159,"1993":160,"1994":161,"1995":162,"1996":163,"1997":164,"1998":165,"2004":166,"2005":167,"2007":168,"2008":169,"2009":170,"2010":171,"2011":172,"2012":173,"2013":174,"2014":175,"2015":176,"2016":177,"2017":178,"2018":179,"2019":180,"2020":181,"2021":182,"2022":183,"2023":98,"2024":184},"2007-05-16",2007,[715,717,719,721,723,725,727,729,731,733,735,737],{"id":18,"text":716,"url":18,"identifiers":18},"Behnia, P., 2004, Geospatial data modeling for mineral exploration in Saghand-Chadormalu area, Central Iran: unpubl. doctoral dissertation, Wuhan Univ., Wuhan, China, 184 p",{"id":18,"text":718,"url":18,"identifiers":18},"Bohman-Carter G. 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B61–B65",{"id":18,"text":726,"url":18,"identifiers":18},"ESRI, 1995, Understanding GIS: The ARC\u002FINFO Method: GeoInformation International, United Kingdom and John Wiley & Sons, New York, 551 p.",{"id":18,"text":728,"url":18,"identifiers":18},"ESRI, 1996, ArcView Spatial Analyst. http:\u002F\u002Fwww.esri.com\u002Fsoftware\u002Farcview\u002Fextensions\u002Fspatialanalyst\u002F",{"id":18,"text":730,"url":18,"identifiers":18},"Hitzman, M. W., Oreskes, N., and Einaudi, M. T., 1992, Geological characteristics and tectonic setting of Proterozoic iron oxide (Cu-U-Au-REE) deposits: Precambrian Research: v. 58, no. 1–4, p. 241–287",{"id":18,"text":732,"url":18,"identifiers":18},"Kemp, L. D., Bonham-Carter, G. F., Raines, G. L., and Looney, C. G., 2001, Arc-SDM: ArcView extension for spatial data modeling using weights of evidence, logistic regression, fuzzy logic and neural network analysis, http:\u002F\u002Fwww.ige.unicamp.br\u002Fsdm",{"id":18,"text":734,"url":18,"identifiers":18},"Looney C. (2002) Radial basis functional link nets and fuzzy reasoning. Neurocomputing 48:489–509",{"id":18,"text":736,"url":18,"identifiers":18},"Moody J. E., Darken C. J. (1989) Fast learning in networks of locally-tuned processing units. Neural Computation. 1(2):281–294",{"id":18,"text":738,"url":18,"identifiers":18},"Pan G. C., Harris D. P. (2000) Information synthesis for mineral exploration. Oxford Univ. Press, New York, p. 461",{"id":740,"createTime":741,"updateTime":742,"relativeEntities":743,"slug":744,"properties":745,"entityType":204,"verifyStatus":205,"verifyTime":742,"verifyNote":206,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":754,"fullTextUrl":18,"authors":755,"publicationType":262,"publisherRelationship":785,"citationCount":18,"citationInfo":18,"publishDate":818,"publishYear":819,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":18,"openAccess":18,"references":18,"isForceReanalyzing":298},"df341f3f-e791-4792-b258-9c7c93d9b309","2024-01-13T17:41:19.125+00:00","2025-02-21T23:51:06.600+00:00",[],"Dynamics-Behind-Cycles-and-Co-movements-in-Metal-Prices-An-Empirical-Study-Using-Band-Pass-Filters",{"references":746,"abstract":748,"title":750,"doi":752},{"VOID":747},"Alameer, Z., Elaziz, M. A., Ewees, A. A., Ye, H., & Jianhua, Z. (2019a). 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The real, real price of nonrenewable resources: Copper 1870–2000. World Development,34(3), 501–519.\nSvedberg, P., & Tilton, J. E. (2011). Long-term trends in the Real real prices of primary commodities: Inflation bias and the Prebisch–Singer hypothesis. Resources Policy,36, 91–93.\nTapia-Cortez, C. A., Saydam, S., Coulton, J., & Sammut, C. (2018). Alternative techniques for forecasting mineral commodity prices. International Journal of Mining Science and Technology,28(2), 309–322.\nTilton, J. E., & Guzmán, J. I. (2016). Mineral economics and policy. New York: RFF Press.\nTilton, J. E., Humphreys, D., & Radetzki, M. (2011). Investor demand and spot commodity prices. Resources Policy, 36(3), 187–195.\nTrotsky, L. (1923). The curve of capitalist development. Vestnik Sotsialisticheskoy Akademii,4, 11–12.\nU.S. Geological Survey. (2018). Mineral commodity summaries. https:\u002F\u002Fminerals.usgs.gov\u002Fminerals\u002Fpubs\u002Fcommodity\u002F. Accessed 1 Apr 2018.\nvan Gelderen, J. (1913). Springvloed: beschouwingen over industrieele ontwikkeling en prijsbeweging. De Nieuwe Tijd,18, 253–445.\nWeijermars, R. (2015). Natural resource wealth optimization: A review of fiscal regimes and equitable agreements for petroleum and mineral extraction projects. Natural Resources Research,24(4), 385–441.",{"EN":749},"After living one of the most intense metal price cycles, several ongoing macroeconomic phenomena with the potential of structurally redefining the long-run supply and demand for metals, and raising divergency regarding where the metal prices are trending, it is suitable to evaluate the dynamics in the metal prices, especially focus on the long cyclical components. This article studies in detail the cyclical components of the real prices of base metals, iron ore, and gold, applying band-pass filters and a novel decomposition over time series with length as far as 1800. The main findings are: (1) the long cyclical components in real prices are highly correlated among them and with the proposed long economic cycles, (2) short and medium cyclical components are more relevant in explaining the price deviations from their trend, but the long cyclical component is not negligible, (3) co-movement in base metals is strong for all the cyclical components, but decreasing as cyclical frequency increases, and (4) prices are either sideways or upward-trending depending on the assumptions for correction of the US Consumer Price Index, which suggests that the supply side of these industries, in the best case, only offset the cost increases by depletion.",{"EN":751},"Dynamics Behind Cycles and Co-movements in Metal Prices: An Empirical Study Using Band-Pass Filters",{"VOID":753},"10.1007\u002Fs11053-019-09535-z","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11053-019-09535-z",[756,773],{"id":757,"sortIndex":161,"researcher":18,"roles":758,"affiliations":759,"properties":770},"14e61b5f-8d9c-4715-af06-e09dcfeb45d5",[212],[760],{"id":18,"sortIndex":19,"affiliation":761,"properties":18},{"id":762,"createTime":763,"updateTime":764,"relativeEntities":765,"slug":766,"properties":767,"entityType":37,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"17567ff0-7a6a-4985-976a-f1c4b8c24e8c","2023-12-11T12:53:08.694+00:00","2024-10-03T09:11:12.249+00:00",[],"Department-of-Mining-and-Materials-Engineering-McGill-University-Montreal-Canada",{"title":768},{"VI":769},"Department of Mining and Materials Engineering, McGill University, Montreal, Canada",{"title":771},{"VI":772},"Mustafa 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A tight fit-Early Mesozoic Gondwana, a plate reconstruction perspective. Memoirs of National Institute of Polar Research, 53, 214–229.\nLowrance, J. D. (2007). Graphical manipulation of evidence in structured arguments: Oxford Journal of Law. Probability and Risk, 6, 225–240.\nMacdonald, D., Gomez-Perez, I., Franzese, J., Spalletti, L., Lawver, L., Gahagan, L., et al. (2003). Mesozoic break-up of SW Gondwana: Implications for regional hydrocarbon potential of the southern South Atlantic. Marine and Petroleum Geology, 20, 287–308.\nMagoon, L. B., & Dow, W. G. (2000). Mapping the petroleum system–an investigative technique to explore the hydrocarbon system. In M. R. Mello & B. J. Katz (Eds.), Petroleum systems of South Atlantic Margins (pp. 53–68), Tulsa, Oklahoma.\nMartinelli, G., Eidsvik, J., Hauge, R., & Forland, M. D. (2011). Bayesian networks for prospect analysis in the North Sea. AAPG Bulletin, 95, 1423–1442.\nMoulin, M., Aslanian, D., Olivet, J.-L., Contrucci, I., Matias, L., Geli, L., et al. (2005). Geological constraints on the evolution of the Angolan margin based on reflection and refraction seismic data (ZaïAngo project). Geophysical Journal International, 162, 793–810.\nMoulin, M., Aslanian, D., & Unternehr, P. (2009). A new starting point for the South and Equatorial Atlantic Ocean. Earth-Science Reviews, 97, 59–95.\nMutter, J. C., Talwani, M., & Stoffa, P. L. (1982). Origin of seaward-dipping reflectors in oceanic crust off the Norwegian margin by “subaerial sea-floor spreading”. Geol, 10, 353–357.\nNürnberg, D., & Müller, R. D. (1991). The tectonic evolution of the South Atlantic from Late Jurassic to present. Tectonophysics, 191, 27–53.\nPletsch, T., Erbacher, J., Holbourn, A. E. L., Kuhnt, W., Moullade, M., Oboh-Ikuenobede, F. E., et al. (2001). Creatceous separation of Africa and America: The view from the West African margin. Journal of South American Earth Sciences, 14, 147–174.\nRabinowitz, P. D., & Labrecque, J. L. (1979). The Mesozoic South Atlantic Ocean and evolution of its continental margins. Journal of Geophysical Research, 84, 5973–6002.\nRichards, P. C., & Hillier, B. V. (2000). Post-drilling analysis of the North Falkland Basin—part 1: Tectono-stratigraphic framework. Journal of Petroleum Geology, 23, 253–272.\nRoss, J. G., Pichin, J., Griffin, D. G., Dinkelmann, M. G., Turic, M. A., & Nevistic, V. A. (1996). Cuenca de Malvinas Norte. In V. A. Ramos & M. A. Turic (Eds.), Geologia y recursos naturales de la plataforma continental Argentina, relatario XIII Congreso Geologico Argentino y III Congreso de Exploracion de Hidrocarboros (pp. 253–271). Buenos Aires: Association Geologica Argentina and Instituto Argentino del Petroleo, Association Geologica Argentina and Instituto Argentino del Petroleo.\nSchmidt, S., Cramer, B., Gerling, P., Neben, S., & Littke, R. (2001). The hydrocarbon potential of the southern Atlantic conjugate continental margins of Argentina and Namibia\u002FSouth Africa. Schriftenreihe der Deutschen Geologischen Gesellschaft, 14, 184–185.\nSchuenemeyer, J. H. (2002). A Framework for expert judgement to assess oil and gas resources. Natural Resources Research, 11(2), 97–107.\nShafer, G. (1976). A mathematical theory of evidence (p. 297). Princeton, NJ: Princeton University Press.\nSzatmari, P. (2000). Habitat of petroleum along the South Atlantic margins. In M. R. Mello & B. J. Katz (Eds.), Petroleum systems of South Atlantic margins (pp. 69–75). Tulsa, OK: AAPG Memoir 73, ISBN 0-89181-354-3.\nTavella, G. F., & Wright, C. G. (1996). Cuenca del Salado. In V. A. Ramos & M. A. Turic (Eds.), Geologia y recursos naturales de la plataforma continental Argentina, relatario XIII° Congreso Geologico Argentino y III° Congreso de Exploracion de Hidrocarboros (pp. 95–116). Buenos Aires: Association Geologica Argentina and Instituto Argentino del Petroleo, Association Geologica Argentina and Instituto Argentino del Petroleo.\nUnternehr, P., Curie, D., Olivet, J. L., Goslin, J., & Beuzart, P. (1988). South Atlantic fits and intraplate boundaries in Africa and South America. Tectonophysics, 155, 169–179.\nWiencierz, A., & Arthur, P. (2009). Dempster’s generalized inference theory: Theoretical foundations, extensions and modern applications. Ludwig Maximilians Universität München, master thesis, 69 pp.",{"EN":830},"The underexplored deep Argentine continental margin may be a prospective area for hydrocarbon resources. This study quantifies the degree of certainty about a fundamental part in a petroleum system, the presence of source rocks. Probability theory and, in particular, Bayesian networks provide powerful assessment tools based on incomplete knowledge. However, they should not be applied uncritically in cases where necessary assumptions like independence are not fulfilled or prior probabilities are not known. We discuss the difference between a low probability and the ignorance of a hypothesis, and apply an alternative method to the assessment of source rock presence. The results of this probabilistic argumentation system coincide with the intuitive judgement, stating that there is a quantifiable evidence for a source rock, but no evidence against it.",{"EN":832},"How to Include Ignorance into Hydrocarbon-Resource Assessments? 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B., & Gillespie, A. R. (2006). Remote sensing of landscapes with spectral images, a physical modeling approach (1st ed.). New York: Cambridge University Press.\nAgar, B., & Coulter, D. (2007). Remote Sensing for mineral exploration. A decade perspective 1997–2007. In Proceedings of exploration 07: fifth decennial international conference on mineral exploration (pp. 109–136).\nAhmadirouhani, R., & Samiee, S. (2014). Mapping glauconite units with using remote sensing techniques in north east of Iran. The international archives of the photogrammetry, remote sensing and spatial information sciences, Volume XL-2\u002FW3. In: The 1st ISPRS international conference on geospatial information research, 15–17 November 2014, Tehran, Iran.\nAlimohammadi, M., Alirezaei, S., & Kontak, D. J. (2015). Application of ASTER data for exploration of porphyry copper deposits: a case study of Daraloo-Sarmeshk area, southern part of the Kerman copper belt, Iran. Ore Geology Reviews, 70, 290–304.\nAsiabanha, A., & Foden, J. (2012). Post-collisional transition from an extensional volcano-sedimentary basin to a continental arc in the Alborz Ranges, N-Iran. Lithos, 148, 98–111.\nAyoobi, I., & Tangestani, M. H. (2017). Evaluation of relative atmospheric correction methods on ASTER VNIR–SWIR data in playa environment. Carbonates and Evaporites, 32(4), 539–546.\nBazargani-Guilani, K., & Rezaei, S. (2008). Mineralogy and genesis of zeolitic succession of Sartakht area, SE—Semnan, north Central Iran. Tehran University Journal of Science, 2, 64–73. (in Persian).\nBerberian, F., & Berberian, M. (1981). Tectono–plutonic episodes in Iran. In: Geological Survey of Iran, Report 52, pp. 566–593.\nBerberian, F., Muir, I. D., Pankhurst, R. J., & Berberian, M. (1982). Late Cretaceous and early Miocene Andean-type plutonic activity in northern Makran and central Iran. Journal of the Geological Society, 139(5), 605–614.\nBertsch, L., & Habgood, H. W. (1963). An infrared spectroscopic study of the adsorption of water and carbon dioxide by Linde Molecular Sieve X. Journal of Physical Chemistry, 67, 1621–1628.\nBishop, J. L., Pieters, C. M., & Edwards, J. O. (1994). Infrared spectroscopic analyses on the nature of water in montmorillonite. Clays and Clay Minerals, 6, 702–716.\nCastro Godoy, S. E., Cozzi, G., Ubaldón, M. C., Donnari, E., & Wright, E. M. (2017). Detection of zeolite with ASTER in stone stop-Buitrera, middle Chubut river, province of Chubut [Detección de Zeolitas con ASTER en Piedra Parada - La Buitrera, río Chubut medio, provincia del Chubut]. Serie Correlacion Geologica, 33(1–2), 61–72.\nChang, C.-I. (1999). Spectral information divergence for hyperspectral image analysis. In Geoscience and remote sensing symposium, 1999. IGARSS ’99 Proceedings. IEEE 1999 International, 1 (pp. 509–511).\nClark, R. N., King, T. V. V., Klejwa, M., Swayze, G. A., & Vergo, N. (1990). High spectral resolution reflectance spectroscopy of minerals. Journal of Geophysical Research, 95, 12653–12680.\nClark, R. N., Swayze, G. A., Gallagher, A. J., Gorelick, N., & Kruse, F. (1991). Mapping with imaging spectrometer data using the complete band shape least-squares algorithm simultaneously fit to multiple spectral features from multiple materials. In R. O. Green (Ed.), Proceedings of the Third Airborne Visible\u002FInfrared Imaging Spectrometer (AVIRIS) Workshop, Jet Propulsion Laboratory Publication 91-28 (pp. 2–3).\nClark, R. N., Swayze, G. A., Wise, R., Livo, E., Hoefen, T., Kokaly, R., & Sutley, S. J. (2007). USGS digital spectral library splib06a: U.S. Geological Survey, Digital Data Series 231. http:\u002F\u002Fspeclab.cr.usgs.gov\u002Fspectral.lib06.\nCloutis, E. A., Asher, P. M., & Mertzm, S. A. (2002). Spectral reflectance properties of zeolites and remote sensing implications. Journal of Geophysical Research, 107(E9), 5067. https:\u002F\u002Fdoi.org\u002F10.1029\u002F2000JE001467.\nCongalton, R. (1991). A review of the assessing the accuracy of classification of remotely sensed data. Remote Sensing of Environment, 37, 35–46.\nCrosta, A. P., De Souza Filho, C. R., Azevedo, F., & Brodie, C. (2003). Targeting key alteration minerals in epithermal deposits in Patagonia, Argentina, using ASTER imagery and principal component analysis. International Journal of Remote Sensing, 24(21), 4233–4240.\nde Jong, S. M., & van der Meer, F. D. (2004). Remote sensing image analysis: including the spatial domain. Remote sensing and digital image processing (Vol. 5). Dordrecht: Kluwer Academic.\nEvans, A. H. (1993). Ore geology and industrial minerals (3rd ed.). Oxford: Blackwell Scientific.\nFujisada, H. (1995). Design and performance of the ASTER instrument. In Proceedings of SPIE. The International Society for Optical Engineering, 2583, pp. 16–25.\nGaffney, E. S., Singer, R. B., & Kunkie, T. D. (1984). Zeolites on mars: Prospects for remote sensing, in reports of the planetary geology and geophysics program 1984 (p. 397). Washington, D. C.: NASA.\nGottardi, G., & Galli, E. (1985). Natural zeolites. New York: Springer.\nHassanzadeh, J., Ghazi, A. M., Axen, G., & Guest, B. (2002). Oligomiocene mafic-alkaline magmatism north and northwest of Iran: Evidence for the separation of the Alborz from the Urumieh-Dokhtar magmatic arc. Geological Society of America Abstracts with Programs, 34(6), 331.\nHay, R. L. (1977). Geology of zeolites in sedimentary rocks. In F. A. Mumpton (Ed.), Mineralogy and geology of natural zeolites. Chelsea. Mineralogical Society of America, 4, pp. 53–63.\nHosseinjani Zadeh, M., Tangestani, M. H., Roldan, F. V., & Yusta, I. (2014). Spectral characteristics of minerals in alteration zones associated with porphyry copper deposits in the middle part of Kerman copper belt, SE Iran. Ore Geology Reviews, 66, 191–198.\nHunt, G. R. (1977). Spectral signatures of particulate minerals in the visible and near infrared. Geophysics, 42(3), 501–513.\nHunt, G. R., & Salisbury, J. W. (1970). Visible and near-infrared spectra of minerals and rocks. I. Silicate minerals. Modern Geology, 1, 283–300.\nIijima, A. (1980). Geology of natural zeolites and zeolitic rocks. In L. V. C. Rees (Ed.), Proceedings, 5th international conference on zeolites. Pure Applied Chemistry, 52, pp. 2115–2130.\nKazemian, H. (2002). Zeolite science in Iran: A brief review. In Zeolite ‘02, 6th international conference on the occurrence, properties and utilization of natural zeolites, Thessaloniki, Greece (pp. 162–163).\nKenea, N. H., & Haenisch, H. (1996). Principal component analyses for lithological and alteration mapping. Example from the Red sea Hills, Sudan. International Archive of Photogrammetry and Remote Sensing, XXXI, 271–275.\nKhalili, M., Makizadeh, M. A., & Taghipour, B. (2005). Evaporitic zeolites in Central Alborz, north of Iran. Carbonates and Evaporites, 20, 34–41. https:\u002F\u002Fdoi.org\u002F10.1007\u002FBF03175446.\nKruse, F. A. (1988). Use of Airborne imaging spectrometer data to map minerals associated with hydrothermally altered rocks in the Northern Grapevine Mountains, Nevada and California. Remote Sensing of Environment, 24, 31–51.\nLangella, A., Cappelletti, P., & de’ Gennaro, M. (2001). Zeolites in closed hydrologic systems. In D. L., Bish, & D. W. Ming (Eds.), Natural zeolites: Occurrence, properties, applications. In: Reviews in Mineralogy and Geochemistry, vol. 45. Mineralogical Society of America, 45 (pp. 235–260).\nMars, J. C., & Rowan, L. C. (2006). Regional mapping of phyllic- and argillic-altered rocks in the Zagros magmatic arc, Iran, using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data and logical operator algorithms. Geosphere, 2, 161–186.\nNoetstaller, R. (1988). Industrial minerals, a technical review (Vol. 76). Washington, D.C.: World Bank.\nOztan, N. S., & Suzen, M. L. (2011). Mapping evaporate minerals by ASTER. International Journal of Remote Sensing, 32(6), 1651–1673.\nRajendran, R., Al-Khirbash, S., Pracejus, B., Nasir, S., Al-Abri, A. H., Kusky, T. M., et al. (2012). ASTER detection of chromite bearing mineralized zones in Semail Ophiolite Massifs of the northern Oman Mountains: Exploration strategy. Ore Geology Reviews, 44, 121–135.\nRajendran, R., & Nasir, S. (2017). Characterization of ASTER spectral bands for mapping of alteration zones of volcanogenic massive sulphide deposits. Ore Geology Reviews, 88, 317–335.\nSabins, F. F. (1987). Remote sensing principles and interpretation. New York: W.H. Freeman and Company.\nSanjeevi, S. (2008). Targeting limestone and bauxite deposits in Southern India by spectral unmixing of hyperspectral image data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B8. Beijing 2008.\nSheppared, R. A., & Gude, A. J., III. (1968). Distribution and genesis of authigenic silicate minerals in tuffs of Pleistocene lake Tecopa, Inyo Country, California. US Geological Survey Professional Paper, 597, 38.\nSoltaninejad, A., Ranjbar, H., Honarmand, M., & Dargahi, S. (2018). Evaporite mineral mapping and determining their source rocks using remote sensing data in Sirjan playa, Kerman, Iran. Carbonates and Evaporites, 33(2), 255–274.\nTaghipour, B., & Mackizadeh, M. (2012). Geological environment of the zeolite origin in the Central Alborz. Neues Jahrbuch Fur Geologie und palaontologie, 256, 235–248.\nTangestani, M. H., & Moore, F. (2002). Porphyry copper alteration mapping at the Meiduk area, Iran. International Journal of Remote Sensing, 23(22), 4815–4825.\nVolesky, J. C., Stern, R. J., & Johnson, P. R. (2003). Geological control of massive sulfide mineralization in the Neoproterozoic Wadi Bidah shear zone, southwestern Saudi Arabia, inferences from orbital remote sensing and field studies. Precambrian Research, 123, 235–247. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fs0301-9268(03)00070-6.\nVural, A., Corumluoglu, O., & Asri, I. (2016). Exploring Gordes zeolite by feature oriented principle component analysis of LANDSAT images. Caspian Journal of Environmental Science, 14(4), 285–298.",{"EN":966},"Zeolites are hydrated alumino-silicates of alkali metals and alkaline earth cations which occur in sedimentary and volcano-sedimentary terrains. In this study, visible–near-infrared and shortwave infrared data of ASTER were evaluated in prospecting for zeolite in part of the green tuff belt of the Alborz Mountains, northern Iran. The study area is dominantly covered by sedimentary and volcano-sedimentary rocks, in which zeolite minerals occur only in the Late Eocene vitric tuff. Principal components (PC) analysis and spectral information divergence (SID) were used to discriminate and map the sedimentary and volcano-sedimentary units and the zeolite-rich areas, respectively. The X-ray diffraction and reflectance spectroscopy results indicated that clinoptilolite is the major type of zeolite mineral in this area. Comparing a color composite image, produced from PC images 1–3–5 as R–G–B, with the published geological map and the field investigations indicated that major sedimentary and volcano-sedimentary units as well as their alluvial deposits were discriminated efficiently. Results of the SID method, using an image-derived spectrum of clinoptilolite as a reference, showed good agreements with the field observations. The results of this study indicated that ASTER data are useful for discriminating various sedimentary and volcano-sedimentary units as well as clinoptilolite-type zeolite-rich areas in arid and semiarid terrains.",{"EN":968},"Prospecting for Clinoptilolite-Type Zeolite in a Volcano-Sedimentary Terrain Using ASTER Data: A Case Study from Alborz Mountains, Northern Iran",{"VOID":970},"10.1007\u002Fs11053-019-09452-1","http:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs11053-019-09452-1",[973,990],{"id":974,"sortIndex":19,"researcher":18,"roles":975,"affiliations":976,"properties":987},"d7fc03ec-274c-43b4-b58c-4f3175826ada",[212],[977],{"id":18,"sortIndex":19,"affiliation":978,"properties":18},{"id":979,"createTime":980,"updateTime":981,"relativeEntities":982,"slug":983,"properties":984,"entityType":37,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"0218bc45-567c-41da-990c-edaa1af8366f","2023-12-21T17:25:32.297+00:00","2025-06-11T15:31:34.735+00:00",[],"Department-of-Earth-Sciences-Faculty-of-Sciences-Shiraz-University-Shiraz-Iran",{"title":985},{"VI":986},"Department of Earth Sciences, Faculty of Sciences, Shiraz University, Shiraz, Iran",{"title":988},{"VI":989},"Khadijeh Validabadi Bozcheloei",{"id":991,"sortIndex":161,"researcher":18,"roles":992,"affiliations":993,"properties":999},"14e92b31-6360-4d9f-ba70-a55a6c45af4e",[212],[994],{"id":18,"sortIndex":19,"affiliation":995,"properties":18},{"id":979,"createTime":980,"updateTime":981,"relativeEntities":996,"slug":983,"properties":997,"entityType":37,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":998},{"VI":986},{"title":1000},{"VI":1001},"Majid H. 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