iipp publishingJournal of Applied Engineering Science


DOI: 10.5937/jaes16-17218
This is an open access article distributed under the CC BY-NC-ND 4.0 terms and conditions. 
Creative Commons License

Volume 16 article 544 pages: 391 - 397

Vladimir Lushpeev
Saint Petersburg State University, Saint Petersburg, Russian Federation

Andrey Margarit
Gazpromneft NTC LLC, Saint Petersburg, Russian Federation

Process of oil-and-gas field development optimization under the conditions of a mineral raw material base dete­rioration and increase in a share of hard-to-recover reserves is the integral part of commercial production stage, especially in the last stage of development. Decisions regarding the optimization of the development system with contour water flooding under the conditions of a high water-cut of well production need to be made using additional instruments for the decision making, such as 1-D, 2-D and 3-D models. Using of simulation does not exclude a par­ticipation of experts in such work and imposes great responsibility on them in making decisions. Searching for optimal decisions under the oil-and-gas field development optimization based on physic-mathematical models together with the participation of recovery and development experts is the basis for managerial decision making in oil-and-gas production companies. This article shows the principles of the oil-and-gas field development optimization based on the existing forecast model and describes an industrial example of such optimization instrument usage together with the participation of the experts.

View article

Audet, C. and Dennis Jr., J.E. (2002) Analysis of generalized pattern searches. SIAM Journal on Op­timization, 13(3), 889–903.

Eberhart, R.C. and Kennedy, J. (1995) A new opti­mizer using particle swarm theory. Proceedings of the Sixth International Symposium on Micromachine and Human Science, 39–43.

Echeverría Ciaurri, D., Isebor, O.J. and Durlofsky, L.J. (2011) Application of derivative-free methodol­ogies to generally constrained oil production opti­mization problems. International Journal of Mathe­matical Modelling and Numerical Optimisation, 2(2), 134–161.

Integrated Modelling Approach as Estimation Tool for Well Regimes and Gathering Network Impact on Oil Rim Development (Russian), 182007-RU SPE Conference Paper – 2016.

Kennedy, J. and Eberhart, R.C. (1995) Particle swarm optimization. Proceedings of IEEE Interna­tional Joint Conference on Neural Networks, 1942– 1948.

Onwunalu, J. and Durlofsky, L.J. (2010) Application of a particle swarm optimization algorithm for deter­mining optimum well location and type. Computa­tional Geosciences, 14, 183–198.

Torczon, V. (1997) On the convergence of pattern search algorithms. SIAM Journal on Optimization, 7(1), 1–25.

Van Essen, G., Van den Hof, P.M.J. and Jansen, J.D. (2011) Hierarchical long-term and short-term produc­tion optimization.109 SPE Journal, 16(1), 191–199.

Lushpeev V.A., Tsiku Y.K., Sorokin P.M. (2014) Well-test during synchronous-separate operation. Life Sci­ence Journal 2014; 11(12s):351-353] (ISSN:1097- 8135). http://www.lifesciencesite.com. 73

J.R. Fanchi, (2000). Integrated approach. ANCO «Institute of computer researches».