Release:
2021. Vol. 7. № 4 (28)About the authors:
Anton Y. Yushkov, Cand. Sci. (Tech.), Associate Professor, Department of Development and Operation of Oil and Gas Fields, Tyumen Industrial University; General Manager, Tyumen Petroleum Research Center; ayyushkov@tnnc.rosneft.ru; ORCID: 0000-0002-6160-0689Abstract:
3D hydrodynamic modeling is the standard tool for predicting the development of hydrocarbon (HC) fields. The relevance of the work is associated with the need to introduce fast and affordable optimization algorithms into engineering practice, which will reduce the cost of computer time to justify the best and most effective development solutions. The authors have proposed a new express method for finding the optimal option for the development of deposits. The method works with discrete sets of possible variations of the required development parameters (for example, the number of wells, the type of completion, the rate of hydrocarbon reserves withdrawal etc.) and minimizes the number of launches of the reservoir simulation simulator per forecast required for the feasibility of various combinations study of parameters and finding both local and global optimal combinations.
Compared with other methods, its advantage is simplicity and realizability in the “manual” mode with a small number of variable parameters, which can be useful for practical problems. The method uses the principle of iterations and is tested on several examples, including the results of hydrodynamic modeling, a comparison is made with known optimization algorithms — in some problems the method allows finding the optimum faster. For example, in the problem of finding the optimal location of horizontal wells, the iterative search turned out to be faster than the “swarm of particles” method. On the other hand, the method does not allow one to reliably determine the optima of complex objective functions that have several local optima. Testing was carried out on the Himmelblau and Rosenbrock functions: in the first case, all five local optima were found, in the second case, seven out of twelve.
Keywords:
References:
Gmurman V. E. 2003. Theory of Probability and Mathematical Statistics. Moscow: Higher school. 479 p. [In Russian]
Zhulanov E. E. 2009. Economics of the Oil and Gas Industry. Perm: Perm State Technical University Publishing House. 154 p. [In Russian]
Klimov Yu.V. 2017. “Selecting a stable development system as a way to increase the asset profitability”. PRONEFT. Professionally about oil, no.1 (3), pp. 60-66. [In Russian]
Matrenin P. V., Grif M. G., Sekaev V. G. 2016. Stochastic optimization methods (educational book). Novosibirsk: NGTU. 67 p. [In Russian]
Rules for the preparation of technical projects for the development of hydrocarbon deposits: approved by Order of the Ministry of Natural Resources of Russia dated 20 September 2019 No. 639. Moscow: Ministry of Natural Resources and Environment of the Russian Federation. [In Russian]
Sergienko A. B. 2015. Test functions for global optimization. Krasnoyarsk: Publishing House of Siberian State Aerospace University named after M. F. Reshetnev. [In Russian]
Yushkov A. Yu., Romanov A. S., Filatov V. S. 2016. “Selection of the optimal option for the development of gas and gas condensate objects based on the theory of an enlarged well”. Collection of scientific papers of LLC “Tyumen Oil Research Center”, no. 2, pp. 140-149. [In Russian]
Kennedy J., Eberhart R. 1995. “Particle swarm optimization”. Proceedings of ICNN’95 — International Conference on Neural Networks, vol. 4, pp. 1942-1948. DOI: 10.1109/ICNN.1995.488968
Melanie M. 1998. An introduction to Genetic Algorithms. Cambridge; London: The MIT Press. Pp. 35-81.
Späth H. 2014. Mathematical Algorithms for Linear Regression. Elsevier Science.