{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T04:12:23Z","timestamp":1750306343163,"version":"3.41.0"},"publisher-location":"New York, New York, USA","reference-count":37,"publisher":"ACM Press","license":[{"start":{"date-parts":[[2016,1,1]],"date-time":"2016-01-01T00:00:00Z","timestamp":1451606400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"European Regional Development Fund","award":["POCI-01-0145-FEDER-006961"],"award-info":[{"award-number":["POCI-01-0145-FEDER-006961"]}]},{"DOI":"10.13039\/501100001871","name":"Fundacao para a Ciencia e a Tecnologia","doi-asserted-by":"publisher","award":["UID\/EEA\/50014\/2013"],"award-info":[{"award-number":["UID\/EEA\/50014\/2013"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016]]},"DOI":"10.1145\/2948992.2949006","type":"proceedings-article","created":{"date-parts":[[2016,7,25]],"date-time":"2016-07-25T15:17:25Z","timestamp":1469459845000},"page":"110-114","source":"Crossref","is-referenced-by-count":1,"title":["An Overview of Evolutionary Computing for Interpretation in the Oil and Gas Industry"],"prefix":"10.1145","author":[{"given":"Rui L.","family":"Lopes","sequence":"first","affiliation":[{"name":"Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal"}]},{"given":"Hamed","family":"Nikhalat-Jahromi","sequence":"additional","affiliation":[{"name":"Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal"}]},{"given":"Al\u00edpio M.","family":"Jorge","sequence":"additional","affiliation":[{"name":"Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal and DCC - FCUP, Universidade do Porto, Portugal"}]}],"member":"320","reference":[{"key":"key-10.1145\/2948992.2949006-1","doi-asserted-by":"crossref","unstructured":"M.-A. Ahmadi, M. R. Ahmadi, S. M. Hosseini, and M. Ebadi. Connectionist model predicts the porosity and permeability of petroleum reservoirs by means of petro-physical logs: Application of artificial intelligence. Journal of Petroleum Science and Engineering, 123:183--200, 2014.","DOI":"10.1016\/j.petrol.2014.08.026"},{"key":"key-10.1145\/2948992.2949006-2","doi-asserted-by":"crossref","unstructured":"T. Aifa. Neural network applications to reservoirs: Physics-based models and data models. Journal of Petroleum Science and Engineering, 123:1--6, 2014.","DOI":"10.1016\/j.petrol.2014.10.015"},{"key":"key-10.1145\/2948992.2949006-3","doi-asserted-by":"crossref","unstructured":"B. D. Al-Anazi, A. Al-Quraishi, et al. New correlation for z-factor using genetic programming technique. In SPE Oil and Gas India Conference and Exhibition. Society of Petroleum Engineers, 2010.","DOI":"10.2118\/128878-MS"},{"key":"key-10.1145\/2948992.2949006-4","unstructured":"A. A. AlQuraishi, M. A. Jumma, et al. Determination of gas viscosity and density using genetic programing. In Offshore Mediterranean Conference and Exhibition. Offshore Mediterranean Conference, 2009."},{"key":"key-10.1145\/2948992.2949006-5","doi-asserted-by":"crossref","unstructured":"F. Aminzadeh. Applications of ai and soft computing for challenging problems in the oil industry. Journal of Petroleum Science and Engineering, 47(1):5--14, 2005.","DOI":"10.1016\/j.petrol.2004.11.011"},{"key":"key-10.1145\/2948992.2949006-6","unstructured":"R. Bertocco and V. Padmanabhan. Big data analyticsin oil and gas. Bain Brief, March 2014."},{"key":"key-10.1145\/2948992.2949006-7","unstructured":"P. P. Bonissone. Soft computing: the convergence of emerging reasoning technologies. Soft Computing-A Fusion of Foundations, Methodologies and Applications, 1(1):6--18, 1997."},{"key":"key-10.1145\/2948992.2949006-8","doi-asserted-by":"crossref","unstructured":"A. Brabazon, M. O'Neill, and S. McGarraghy. Natural Computing Algorithms. Natural Computing Series. Springer, 2015.","DOI":"10.1007\/978-3-662-43631-8"},{"key":"key-10.1145\/2948992.2949006-9","doi-asserted-by":"crossref","unstructured":"C. Cranganu and E. Bautu. Using gene expression programming to estimate sonic log distributions based on the natural gamma ray and deep resistivity logs: a case study from the anadarko basin, oklahoma. Journal of Petroleum Science and Engineering, 70(3):243--255, 2010.","DOI":"10.1016\/j.petrol.2009.11.017"},{"key":"key-10.1145\/2948992.2949006-10","unstructured":"K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan. A fast and elitist multiobjective genetic algorithm: Nsga-ii. Evolutionary Computation, IEEE Transactions on, 6(2):182--197, 2002."},{"key":"key-10.1145\/2948992.2949006-11","doi-asserted-by":"crossref","unstructured":"M. Dobr&#243;ka and N. P. Szab&#243;. Interval inversion of well-logging data for automatic determination of formation boundaries by using a float-encoded genetic algorithm. Journal of Petroleum Science and Engineering, 86:144--152, 2012.","DOI":"10.1016\/j.petrol.2012.03.028"},{"key":"key-10.1145\/2948992.2949006-12","doi-asserted-by":"crossref","unstructured":"C. Du and J. Cheng. Seismic reservoir fuzzy rules extraction based on ga-bp fnn. In Computational Sciences and Optimization, 2009. CSO 2009. International Joint Conference on, volume 2, pages 850--852. IEEE, 2009.","DOI":"10.1109\/CSO.2009.115"},{"key":"key-10.1145\/2948992.2949006-13","doi-asserted-by":"crossref","unstructured":"A. E. Eiben and J. E. Smith. Introduction to evolutionary computing, volume 53. Springer, 2003.","DOI":"10.1007\/978-3-662-05094-1"},{"key":"key-10.1145\/2948992.2949006-14","doi-asserted-by":"crossref","unstructured":"M. Emami Niri and D. E. Lumley. Simultaneous optimization of multiple objective functions for reservoir modeling. Geophysics, 80(5):M53--M67, 2015.","DOI":"10.1190\/geo2015-0006.1"},{"key":"key-10.1145\/2948992.2949006-15","doi-asserted-by":"crossref","unstructured":"C. Ferreira. Gene expression programming in problem solving. In Soft Computing and Industry, pages 635--653. Springer, 2002.","DOI":"10.1007\/978-1-4471-0123-9_54"},{"key":"key-10.1145\/2948992.2949006-16","doi-asserted-by":"crossref","unstructured":"R. Gholami, A. Moradzadeh, S. Maleki, S. Amiri, and J. Hanachi. Applications of artificial intelligence methods in prediction of permeability in hydrocarbon reservoirs. Journal of Petroleum Science and Engineering, 122:643--656, 2014.","DOI":"10.1016\/j.petrol.2014.09.007"},{"key":"key-10.1145\/2948992.2949006-17","unstructured":"M. S. Hajirahimova. Opportunities and challenges of big data in oil and gas industry. 2015."},{"key":"key-10.1145\/2948992.2949006-18","unstructured":"A. Hems, A. Soofi, and E. Perez. How innovative oil and gas companies are using big data to outmaneuver the competition, 2013."},{"key":"key-10.1145\/2948992.2949006-19","doi-asserted-by":"crossref","unstructured":"H. Kaydani, A. Mohebbi, and A. Baghaie. Permeability prediction based on reservoir zonation by a hybrid neural genetic algorithm in one of the iranian heterogeneous oil reservoirs. Journal of Petroleum Science and Engineering, 78(2):497--504, 2011.","DOI":"10.1016\/j.petrol.2011.07.017"},{"key":"key-10.1145\/2948992.2949006-20","doi-asserted-by":"crossref","unstructured":"H. Kaydani, A. Mohebbi, and M. Eftekhari. Permeability estimation in heterogeneous oil reservoirs by multi-gene genetic programming algorithm. Journal of Petroleum Science and Engineering, 123:201--206, 2014.","DOI":"10.1016\/j.petrol.2014.07.035"},{"key":"key-10.1145\/2948992.2949006-21","unstructured":"V. Kecman. Learning and soft computing: support vector machines, neural networks, and fuzzy logic models. MIT press, 2001."},{"key":"key-10.1145\/2948992.2949006-22","doi-asserted-by":"crossref","unstructured":"M. M. Labani, A. Kadkhodaie-Ilkhchi, and K. Salahshoor. Estimation of nmr log parameters from conventional well log data using a committee machine with intelligent systems: a case study from the iranian part of the south pars gas field, persian gulf basin. Journal of Petroleum Science and Engineering, 72(1):175--185, 2010.","DOI":"10.1016\/j.petrol.2010.03.015"},{"key":"key-10.1145\/2948992.2949006-23","unstructured":"J. Leber. Big oil goes mining for big data. MIT Technology Review, May 2012."},{"key":"key-10.1145\/2948992.2949006-24","doi-asserted-by":"crossref","unstructured":"H. Li, H. Guo, H. Guo, and Z. Meng. Data mining techniques for complex formation evaluation in petroleum exploration and production: A comparison of feature selection and classification methods. In Computational Intelligence and Industrial Application, 2008. PACIIA'08. Pacific-Asia Workshop on, volume 1, pages 37--43. IEEE, 2008.","DOI":"10.1109\/PACIIA.2008.241"},{"key":"key-10.1145\/2948992.2949006-25","doi-asserted-by":"crossref","unstructured":"X. Li and H. Li. A new method of identification of complex lithologies and reservoirs: task-driven data mining. Journal of Petroleum Science and Engineering, 109:241--249, 2013.","DOI":"10.1016\/j.petrol.2013.08.049"},{"key":"key-10.1145\/2948992.2949006-26","doi-asserted-by":"crossref","unstructured":"C. Maschio, A. Davolio, M. G. Correia, and D. J. Schiozer. A new framework for geostatistics-based history matching using genetic algorithm with adaptive bounds. Journal of Petroleum Science and Engineering, 127:387--397, 2015.","DOI":"10.1016\/j.petrol.2015.01.033"},{"key":"key-10.1145\/2948992.2949006-27","doi-asserted-by":"crossref","unstructured":"B. Min, J. M. Kang, S. Chung, C. Park, and I. Jang. Pareto-based multi-objective history matching with respect to individual production performance in a heterogeneous reservoir. Journal of Petroleum Science and Engineering, 122:551--566, 2014.","DOI":"10.1016\/j.petrol.2014.08.023"},{"key":"key-10.1145\/2948992.2949006-28","doi-asserted-by":"crossref","unstructured":"S. D. Mohaghegh. Reservoir simulation and modeling based on artificial intelligence and data mining (ai&dm). Journal of Natural Gas Science and Engineering, 3(6):697--705, 2011.","DOI":"10.1016\/j.jngse.2011.08.003"},{"key":"key-10.1145\/2948992.2949006-29","doi-asserted-by":"crossref","unstructured":"H. Parhizgar, M. R. Dehghani, and A. Eftekhari. Modeling of vaporization enthalpies of petroleum fractions and pure hydrocarbons using genetic programming. Journal of Petroleum Science and Engineering, 112:97--104, 2013.","DOI":"10.1016\/j.petrol.2013.10.012"},{"key":"key-10.1145\/2948992.2949006-30","doi-asserted-by":"crossref","unstructured":"H.-Y. Park, A. Datta-Gupta, and M. J. King. Handling conflicting multiple objectives using pareto-based evolutionary algorithm during history matching of reservoir performance. Journal of Petroleum Science and Engineering, 125:48--66, 2015.","DOI":"10.1016\/j.petrol.2014.11.006"},{"key":"key-10.1145\/2948992.2949006-31","doi-asserted-by":"crossref","unstructured":"M. Shaheen, M. Shahbaz, Z. ur Rehman, and A. Guergachi. Data mining applications in hydrocarbon exploration. Artificial Intelligence Review, 35(1):1--18, 2011.","DOI":"10.1007\/s10462-010-9180-z"},{"key":"key-10.1145\/2948992.2949006-32","doi-asserted-by":"crossref","unstructured":"E. M. E.-M. Shokir. Dewpoint pressure model for gas condensate reservoirs based on genetic programming. Energy & Fuels, 22(5):3194--3200, 2008.","DOI":"10.1021\/ef800225b"},{"key":"key-10.1145\/2948992.2949006-33","doi-asserted-by":"crossref","unstructured":"D. Xie, D. A. Wilkinson, T. Yu, et al. Permeability estimation using a hybrid genetic programming and fuzzy\/neural inference approach. In SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers, 2005.","DOI":"10.2118\/95167-MS"},{"key":"key-10.1145\/2948992.2949006-34","doi-asserted-by":"crossref","unstructured":"Y. Xue, L. Cheng, J. Mou, and W. Zhao. A new fracture prediction method by combining genetic algorithm with neural network in low-permeability reservoirs. Journal of Petroleum Science and Engineering, 121:159--166, 2014.","DOI":"10.1016\/j.petrol.2014.06.033"},{"key":"key-10.1145\/2948992.2949006-35","doi-asserted-by":"crossref","unstructured":"D. Yong-gang, C. Wei, F. Quantang, and H. Jixiang. Rate decline analysis of fractured gas well by genetic hybrid optimization. In Computational and Information Sciences (ICCIS), 2010 International Conference on, pages 857--860. IEEE, 2010.","DOI":"10.1109\/ICCIS.2010.213"},{"key":"key-10.1145\/2948992.2949006-36","doi-asserted-by":"crossref","unstructured":"L. A. Zadeh. Fuzzy logic, neural networks, and soft computing. Communications of the ACM, 37(3):77--85, 1994.","DOI":"10.1145\/175247.175255"},{"key":"key-10.1145\/2948992.2949006-37","doi-asserted-by":"crossref","unstructured":"T. Zhao, V. Jayaram, A. Roy, and K. J. Marfurt. A comparison of classification techniques for seismic facies recognition. Interpretation, 3(4):SAE29--SAE58, 2015.","DOI":"10.1190\/INT-2015-0044.1"}],"event":{"number":"9","sponsor":["BytePress","ISEP"],"acronym":"C3S2E '16","name":"the Ninth International C* Conference","start":{"date-parts":[[2016,7,20]]},"location":"Porto, Portugal","end":{"date-parts":[[2016,7,22]]}},"container-title":["Proceedings of the Ninth International C* Conference on Computer Science &amp; Software Engineering - C3S2E '16"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/2948992.2949006","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/dl.acm.org\/ft_gateway.cfm?id=2949006&amp;ftid=1766659&amp;dwn=1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:55:46Z","timestamp":1750222546000},"score":1,"resource":{"primary":{"URL":"http:\/\/dl.acm.org\/citation.cfm?doid=2948992.2949006"}},"subtitle":[],"proceedings-subject":"Computer Science & Software Engineering","short-title":[],"issued":{"date-parts":[[2016]]},"references-count":37,"URL":"https:\/\/doi.org\/10.1145\/2948992.2949006","relation":{},"subject":[],"published":{"date-parts":[[2016]]}}}