Abstract
The chapter, entitled “Genetic Programming Techniques with Applications in the Oil and Gas Industry”, consists of four parts. The first part presents theoretical features of the genetic programming algorithm, describing its main components, such as individual representation, initialization of the population, evaluation of the individuals, genetic operators, and selection scheme. The second part is concerned with a hybrid evolutionary algorithm—Gene Expression Programming, which combines features from genetic algorithms and genetic programming. In the third part, references towards software frameworks that implement GP are provided. This part then focuses on the use of the R package for genetic programming—RGP and provides a guide for the package, using two model problems to exemplify its usage. The last part reviews applications of genetic programming for petroleum engineering problems.
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Notes
- 1.
RGP can be freely downloaded from http://cran.r-project.org/web/packages/rgp/index.html.
References
Bautu E (2010) Intelligent techniques for data modeling problems. Ph.D Thesis, Lambert Academic Publishing, Department of Computer Science, Al. I. Cuza University
Bautu E, Bautu A (2009) Programare genetica Teorie si aplicatii. Al. I Cuza University Publishing House, Romania (In romanian)
Cramer NL (1985) A representation for the adaptive generation of simple sequential programs. In: Grefenstette JJ (ed) Proceedings of an international conference on genetic algorithms and the applications, Carnegie-Mellon University, USA, pp. 183–187
Cranganu C, Bautu E (2010) 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. J Petrol Sci Eng 70(3):243–255
David J (1995) Montana strongly typed genetic programming. Evol Comput 3(2):199–230
Dickmanns D, Schmidhuber J, Winklhofer A (1987) Der genetische algorithmus: Eine implementierung in Prolog. Technical report, Institut fur Informatik, Lehrstuhl Prof. Radig, Technische Universität München
Eissa M, Shokir El-M (2008) Dewpoint pressure model for gas condensate reservoirs based on genetic programming. Energy Fuels 22(5):3194–3200
Ferreira C (2001) Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst 13(2):87–129
Ferreira C (2010) What is gep? from genexprotools tutorials-a gepsoft web resource. http://www.gepsoft.com/. Accessed 14 Dec 2014
Flasch O, Mersmann O, Bartz-Beielstein T (2010) Rgp: an open source genetic programming system for the r environment. In: Proceedings of the 12th annual conference companion on genetic and evolutionary computation, ACM, 2010, pp 2071–2072
Foster JA (2001) Review: discipulus: a commercial genetic programming system. Genet Program Evolvable Mach 2(2):201–203
Garg A, Garg A, Tai K, Sreedeep S (2014a) Estimation of pore water pressure of soil using genetic programming. Geotech Geol Eng 34:765–772
Garg A, Garg A, Tai K (2014b) A multi-gene genetic programming model for estimating stress-dependent soil water retention curves. Comput Geosci 18(1):45–56
Güllü H (2012) Prediction of peak ground acceleration by genetic expression programming and regression: a comparison using likelihood-based measure. Eng Geol 141:92–113
Holland JH (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. The MIT Press, Massachusetts
Irani R, Nasimi R (2011) Evolving neural network using real coded genetic algorithm for permeability estimation of the reservoir. Expert Syst Appl 38(8):9862–9866
Johari A, Habibagahi G, Ghahramani A (2006) Prediction of soil–water characteristic curve using genetic programming. J Geotech Geoenviron Eng 132(5):661–665
Jurgawczynski M (2007) Predicting absolute and relative permeabilities of carbonate rocks using image analysis and effective medium theory. PhD thesis, Imperial College, Cambridge
Katz R (1995) Measurements on petroleum rock samples. https://stat.ethz.ch/R-manual/R-devel/library/datasets/html/rock.html. Data from BP research, image analysis by Katz R. Department of Statistics, University of Oxford. Accessed 14 Dec 2014
Kaydani H, Mohebbi A Eftekhari M (2014) Permeability estimation in heterogeneous oil reservoirs by multi-gene genetic programming algorithm. J Petrol Sci Eng 123:201
Keith MJ, Martin MC (1994) Genetic programming in C++: implementation issues. In: Kinnear KE (ed) Advances in genetic programming (Chap. 13), MIT Press, Cambridge, pp 285–310
Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge
Langdon WB, Poli R (2002) Foundations of genetic programming. Springer, Berlin
LI Q, Zhihua CAI, Zhu L, Zhao Y (2004) Application of gene expression programming in predicting the amount of gas emitted from coal face. J Basic Sci Eng 1:006
Luke S (2000a) Issues in scaling genetic programming: breeding strategies, tree generation, and code bloat. Ph.D. thesis, Department of Computer Science, University of Maryland, College Park, Maryland
Luke S (2000b) Two fast tree-creation algorithms for genetic programming. IEEE Trans Evol Comput 4(3):274–283
Luke S, Panait L, Balan G, Paus S, Skolicki Z, Bassett J, Hubley R, A Chircop (2006) ECJ: a java-based evolutionary computation research system. Downloadable versions and documentation can be found at the following url: http://cs.gmu.edu/eclab/projects/ecj
Poli R, Koza J (2014) Genetic programming. In: Burke EK, Kendall G (eds) Search methodologies, Springer US, pp 143–185
Poli R, Langdon WB, McPhee NF (2008) A field guide to genetic programming. http://www.gp-field-guide.org.uk (With contributions by J.R. Koza)
Qiong G, Zhi-hua CAI, Li Z, Bo H, Du J (2007) A novel gep algorithm based on pca and its application in predicting the amount of gas emitted from coalface. J Basic Sci Eng 4:018
RGP documentation. http://cran.r-project.org/web/packages/rgp/rgp.pdf. Accessed 14 Dec 2014
RGP introduction. http://cran.r-project.org/web/packages/rgp/vignettes/rgp_introduction.pdf. Accessed: 2014-12-14
Roy S, Ghosh A, Dos AK, Banerjee R (2014) A comparative study of GEP and an ANN strategy to model engine performance and emission characteristics of a CRDI assisted single cylinder diesel engine under CNG dual-fuel operation. J Nat Gas Sci Eng 21:814–828
Saemi M, Ahmadi M, Varjani AY (2007) Design of neural networks using genetic algorithm for the permeability estimation of the reservoir. J Petrol Sci Eng 59(1):97–105
Schmidhuber J (1987) Evolutionary principles in self-referential learning. (on learning how to learn: the meta-meta-… hook.). Technical report, Institut f. Informatik, Technische Universität München
Shahnazari H, Shahin MA, Tutunchian MA (2014) Evolutionary-based approaches for settlement prediction of shallow foundations on cohesionless soils. Geotech Eng 12(1):55–64
Silva S, Almeida J (2003) Gplab-a genetic programming toolbox for matlab. In: Proceedings of the Nordic MATLAB conference, Citeseer, pp 273–278
Veeramachaneni K, Vladislavleva K, O’Reilly U-M (2010) Feature extraction from optimization data via datamodeler’s ensemble symbolic regression. In: Learning and intelligent optimization, Springer, pp 251–265
Vladislavleva EJ, Smits GF, Hertog DD (2009) Order of nonlinearity as a complexity measure for models generated by symbolic regression via pareto genetic programming. Evol Comput IEEE Trans 13(2):333–349
Wilkinson DA, Yu T, Castellini A (2010) Method for forecasting the production of a petroleum reservoir utilizing genetic programming, 2 Feb 2010. US Patent 7,657,494
Yu T, Wilkinson D, Castellini A (2007) Applying genetic programming to reservoir history matching problem. In: Genetic programming theory and practice IV, Springer, Berlin, pp 187–201
Yu T, Wilkinson D, Castellini A (2008) Constructing reservoir flow simulator proxies using genetic programming for history matching and production forecast uncertainty analysis. J Artif Evol Appl 2008:2
Yu T, Wilkinson D, Clark J, Sullivan M (2011) Computational intelligence for deepwater reservoir depositional environments interpretation. J Nat Gas Sci Eng 3(6):716–728
Zhou C, Xiao W, Tirpak TM, Nelson PC (2003) Evolving accurate and compact classification rules with gene expression programming. IEEE Trans Evol Comput 7:519–531
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Luchian, H., Băutu, A., Băutu, E. (2015). Genetic Programming Techniques with Applications in the Oil and Gas Industry. In: Cranganu, C., Luchian, H., Breaban, M. (eds) Artificial Intelligent Approaches in Petroleum Geosciences. Springer, Cham. https://doi.org/10.1007/978-3-319-16531-8_3
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