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Development of a comprehensive methodology for the forecast of effectiveness of geological and technical measures based on machine learning algorithms

A.A. Kochnev, N.D. Kozyrev, O.E. Kochneva, S.V. Galkin

Original article

DOI https://doi.org/10.18599/grs.2020.3.79-86

79-86
rus.

open access

Under a Creative Commons license
The main part of hydrocarbon production in Russia is represented by old oil and gas producing regions. Such areas are characterized by a significant decrease in well productivity due to high water cut and faster production of the most productive facilities. An important role for such deposits is played by stabilization of production and increase of mobile reserves by improving the development system. This is facilitated by various geological and technical measures.

Today, an urgent problem is to increase the reliability of the forecast of technological and economic efficiency when planning various geological and technical measures. This is due to the difficulty in selecting candidate wells under the conditions of the old stock, the large volume of planned activities, the reduction in the profitability of measures, the lack of a comprehensive methodology for assessing the potential of wells for the short and long term.

Currently, there are several methods to evaluate the effectiveness of geological and technical measures: forecast based on geological and field analysis, statistical forecast, machine learning, hydrodynamic modeling. However, each of them has its own shortcomings and assumptions. The authors propose a methodology for predicting the effectiveness of geological and technical measures, which allows one to combine the main methods at different stages of evaluating the effectiveness and to predict the increase in fluid and oil production rates, additional production, changes in the dynamics of reservoir pressure and the rate of watering of well production.
 

geological and technical measures, efficiency forecast, machine learning, mathematical statistics, hydrodynamic modeling, geological and physical parameters

 

  • Andronov Yu.V. (2019). Methodology for the operational assessment of the prospectivity of wells for methods of stimulating oil inflow using neural networks and decision trees. Abstract. cand. sci. diss. Moscow, 24 p. (In Russ.)
  • Azbuhanov A.F., Kostrigin I.V., Bondarenko K.A., Semenova M.N., Sereda I.A., Yulmukhametov D.R. (2019). Selection of candidate wells for hydraulic fracturing based on mathematical modeling using machine learning methods. Neftyanoe khozyaystvo = Oil industry, 11, pp. 38–42. (In Russ.) https://doi.org/10.24887/0028-2448-2019-11-38-42
  • Galkin S.V., Kochnev A.A., Zotikov V.I. (2019). Estimate of Radial Drilling Technology Efficiency for the Bashkir Operational Oilfields Objects of Perm Krai. Zapiski gornogo instituta = Journal of Mining Institute, 238, pp. 410–414. (In Russ.).  https://doi.org/10.31897/pmi.2019.4.410  
  • Ilyushin P.Y., Rakhimzyanov R.M., Solovyov D.Y., Kolychev I.Y. (2015). Analysis of geological and technical measures to increase the productivity of producing wells in the oil fields of the Perm region. Vestnik Permskogo natsional’nogo issledovatel’skogo politekhnicheskogo universiteta. Geologiya. Neftegazovoe i gornoe delo = Perm Journal of Petroleum and Mining Engineering, 14(15), pp. 81–89. (In Russ.)
  • Kochnev A.A., Zotikov V.I., Galkin S.V. (2018). Analysis of the influence of geological and technological parameters on the effectiveness of radial drilling technology on the example of operational objects in perm region. Izvestiya Tomskogo Politekhnicheskogo Universiteta Inziniring Georesursov = Bulletin of the Tomsk Polytechnic University. Geo Assets Engineering, 329(12), pp. 20–29. (In Russ.). https://doi.org/10.18799/24131830/2018/12/16
  • Kolbikov S., Kuznetsova Y., Smirnov A. (2018). Method of anisotropy modeling and its application to hydrodynamic simulation. SPE Russian Petroleum Technology Conference. https://doi.org/10.2118/191622-18RPTC-MS
  • Koroteev D., Dinariev O., Evseev N., Klemin D., Nadeev A., Safonov S., Gurpinar O., Berg S., Kruijsdijk C., Armstrong R., Myers M. T., Hathon L., Jong H. (2014). Direct hydrodynamic simulation of multiphase flow in porous rock. Petrophysics, 55(04), pp. 294–303.
  • Kravchenko M.N., Dieva N.N., Lischuk A.N., Muradov A.V., Vershinin V.E. (2018). Hydrodynamic modeling of thermochemical effects on low-permeability kerogen-containing reservoirs. Georesursy = Georesources, 20(3), pp. 178–185. https://doi.org/10.18599/grs.2018.3.178-185
  • Lyu S., Zhang W., Du J., Gong F. (2014). A Coupled Model for the Hydrodynamics Simulation of the Pearl River Networks and its Estuary. The Twenty-fourth International Ocean and Polar Engineering Conference. https://doi.org/10.1155/2014/798579
  • Olenchikov D., Kruglikova L. (2008). Hydrodynamic Simulation Of Predicted Options Of Field Development. SPE Russian Oil and Gas Technical Conference and Exhibition. https://doi.org/10.2118/117408-MS
  • Pichugin O.N., Prokofieva Yu.Z., Aleksandrov D.M. (2013). Decision trees as an effective method of analysis and forecasting. Neftepromyslovoe delo, 11, pp. 69–75. (In Russ.)
  • Polukeev D.I., Gabdrakhmanova R.R., Lesnoy A.N., Kryukov M.A., Pimenova N.A., Pimenova M.I. (2018). Methodology of technical and economic evaluation of the effectiveness of geological and technical measures. LUKOIL-Engineering LLC. (In Russ.)
  • Putilov I., Krivoshchekov S., Vyatkin K., Kochnev A., & Ravelev K. (2020). Methods of predicting the effectiveness of hydrochloric acid treatment using hydrodynamic simulation. Applied Sciences (Switzerland), 10(14),  4828. https://doi.org/10.3390/app10144828
  • Repina, V.A., Galkin, V.I., Galkin, S.V. Complex petrophysical correction in the adaptation of geological hydrodynamic models (On the example of visean pool of Gondyrev oil field). Zapiski gornogo instituta = Journal of Mining Institute, 231, pp. 268–274. (In Russ.). https://doi.org/10.25515/pmi.2018.3.268
  • Sayfutdinov M.A., Khakimzyanov I.N., Petrov V.N., Sheshdirov R.I., Mironova L.M. (2018). Studies on the presence of a hydrodynamic connection between the terrigenous Bobrikovsky and carbonate Tournaisian objects based on the geological and technological model of the field site. Georesursy = Georesources, 20(1), pp. 2–8. https://doi.org/10.18599/grs.2018.1.2-8
  • Tsaregorodtsev V.G. (2008). A constructive algorithm for synthesizing the structure of a multilayer perceptron. Vychislitelnye tekhnlogii = Computational technologies, 13, pp. 308–315. (In Russ.)
  • Voronovsky G.K., Makhotilo K.V., Petrashev S.N., Sergeev S.A. (1997). Genetic algorithms, artificial neural networks, and virtual reality problems. 112 p. (In Russ.)
  •  
Alexander A. Kochnev
Perm National Research Polytechnic University
29, Komsomolsky av., Perm, 614990, Russian Federation
 
Nikita D. Kozyrev
Perm National Research Polytechnic University; Branch of LLC «LUKOIL-Engineering» «PermNIPIneft» in Perm
29, Soviet Army st., Perm, 614066, Russian Federation
 
Olga E. Kochneva
Saint-Petersburg Mining University
2, 21st lines, Vasilyevsky Island, St. Petersburg, 199106, Russian Federation
 
Sergey V. Galkin
Perm National Research Polytechnic University
29, Komsomolsky av., Perm, 614990, Russian Federation
 

For citation:

Kochnev A.A., Kozyrev N.D., Kochneva O.E., Galkin S.V. (2020). Development of a comprehensive methodology for the forecast of effectiveness of geological and technical measures based on machine learning algorithms. Georesursy = Georesources, 22(3), pp. 79–86. DOI: https://doi.org/10.18599/grs.2020.3.79-86