The concept of multilevel modeling as the basis of a decision-making support system for the development of mature oil fields

UDK: 622.276.1/.4.001.57
DOI: 10.24887/0028-2448-2023-12-112-117
Key words: mathematical modeling, hierarchy of models, multiscale modeling, field development, decision making
Authors: S.V. Stepanov (Tyumen Petroleum Research Center LLC, RF, Tyumen; University of Tyumen, RF, Tyumen), I.N. Glukhikh (University of Tyumen, RF, Tyumen), A.V. Arzhilovskiy (Tyumen Petroleum Research Center LLC, RF, Tyumen)

The article discusses a new concept of multilevel mathematical modeling as the basis of a decision support system for the development of oil deposits at a late stage. The motivation for creating a new concept is the enormous uncertainty of the modeling object – oil reservoir. The article considers various reasons for such uncertainty, in particular, the ambiguity of the rescaling procedure and numerical effects in solving the equations of multiphase filtration in the reservoir. The proposed concept of multilevel modeling consists of a stage of multiscale modeling (modeling of nested objects of different scales: core fragment – core – borehole neighborhood - formation) and a stage of hierarchical modeling (reservoir modeling using models of varying complexity). In this case, the principle of optimal complexity models is used. The scheme of multilevel modeling is substantiated, the implementation of which should allow to level the problem of colossal uncertainty and make operational decisions on the development of deposits. Such a scheme assumes the construction of a three-dimensional hydrodynamic model (as the vertices of the hierarchy of models) based on the finite element method, excluding its total dependence on the geological model, numerical effects and the problem of rescaling. The proposed concept is used in the developed architecture of the decision-making system, in relation to which five requirements are formulated: 1) taking into account the peculiarities of the approach to mathematical modeling; 2) the ability of the decision-maker to access his level of the spatial and temporal hierarchy of managerial decisions; 3) the possibility of using a variety of approaches to working with information (in particular, the case-based approach); 4) consideration of the surface development of the field; 5) consideration of the iterative business planning process based on the economic model. It is noted that it is advisable to use an ontological knowledge base to create an integrated model.

References

1. Stepanov S.V., Sokolov S.V., Ruchkin A.A. et al., Considerations on mathematical modeling of producer-injector interference (In Russ.), Vestnik Tyumenskogo gosudarstvennogo universiteta. Fiziko-matematicheskoe modelirovanie. Neft', gaz, energetika = Tyumen State University Herald. Physical and Mathematical Modeling. Oil, Gas, Energy, 2018, V. 4, no. 3, pp. 146–164, DOI: https://doi.org/10.21684/2411-7978-2018-4-3-146-164

2. Samarskiy A.A., Vabishchevich P.N., Chislennye metody resheniya obratnykh zadach matematicheskoy fiziki (Numerical methods for solving inverse problems of mathematical physics), Moscow: Editorial URSS Publ., 2004, 480 p.

3. Gladkov E.A., Geologicheskoe i gidrodinamicheskoe modelirovanie mestorozhdeniy nefti i gaza (Geological and hydrodynamic modeling of oil and gas fields), Tomsk: Publ. of TPU, 2012, 99 p.

4. Persova M.G., Soloveichik Yu.G., Vagin D.V. et al., The design of high-viscosity oil reservoir model based on the inverse problem solution, Journal of Petroleum Science and Engineering, 2021, V. 199, Article No. 108245, DOI: https://doi.org/10.1016/j.petrol.2020.108245

5. Soloveichik Yu.G., Persova M.G., Grif A.M. et al., A method of FE modeling multiphase compressible flow in hydrocarbon reservoirs, Computer methods in applied mechanics and engineering, 2022, V. 390, Article No. 114468, DOI: https://doi.org/10.1016/j.cma.2021.114468

6. Akin'shin A.V., Rodivilov D.B., Yatsenko V.M. et al., Detailed study of lithological and petrophysical properties of texturally heterogeneous terrigenous reservoirs of Western Siberia (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2023, no. 6, pp. 16-19, DOI: https://doi.org/10.24887/0028-2448-2023-6-16-19

7. Belyakov E.O., Petrofizicheskoe modelirovanie fil'tratsionno-emkostnykh svoystv neftenosnykh kollektorov v kontseptsii svyazannosti porovogo prostranstva (na primere traditsionnykh terrigennykh kollektorov Zapadnoy Sibiri) (Petrophysical modeling of filtration and reservoir properties of oil-bearing reservoirs in the concept of pore space connectivity (using the example of traditional terrigenous reservoirs of Western Siberia)), Moscow – Izhevsk: Publ. of Institute for Computer Research, 2021, 288 p.

8. Kadet V.V., Khurgin Ya.I., Sovremennye veroyatnostnye podkhody pri reshenii zadach mikro- i makrourovnya v neftegazovoy otrasli (Modern probabilistic approaches to solving micro- and macro-level problems in the oil and gas industry), Moscow – Izhevsk: Publ. of Institute for Computer Research, 2006, 240 p.

9. Eremin N.A., Modelirovanie mestorozhdeniy uglevodorodov metodami nechetkoy logiki (Modeling of hydrocarbon deposits by methods of fuzzy logic), Moscow: Nauka Publ., 1994, 462 p.

10. Altunin A.E., Semukhin M.V., Stepanov S.V., Using the material balance and the fuzzy sets theory to solve the problems of recovery separation at simultaneous development of several reservoirs (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2012, no. 5, pp. 56-60.

11. Galiullin M.M., Zimin P.V., Vasil'ev V.V., Methodology selection of wells for stimulation of the production usage mathematical tools fuzzy logic (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2011, no. 6, pp. 120–123.

12. Stepanov S.V., Arzhilovskiy A.V., On the issue of improving the quality of mathematical modeling in solving problems of oil field development support (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2023, no. 4, pp. 56-60, DOI: https://doi.org/10.24887/0028-2448-2023-4-56-60

13. 13. Stepanov S.V., Tyrsin A.N., Ruchkin A.A., Pospelova T.A., Using entropy modeling to analyze the effectiveness of the waterflooding system (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2020, No. 6, pp. 62-67, DOI: https://doi.org/10.24887/0028-2448-2023-12- 62-67

14. Stepanov S.V., Bekman A.D., Ruchkin A.A., Pospelova T.A., Soprovozhdenie razrabotki neftyanykh mestorozhdeniy s ispol'zovaniem modeley CRM (Support for oil field development using CRM models), Tyumen: Ekspress Publ., 2021, 300 p.

15. Bekman A.D., Zelenin D.V., Application of advanced CRMP for reservoir pressure mapping (In Russ.), Vestnik Tyumenskogo gosudarstvennogo universiteta. Fiziko-matematicheskoe modelirovanie. Neft', gaz, energetika, 2021, V. 7, no. 4(28), pp. 163–180, DOI: https://doi.org/10.21684/2411-7978-2021-7-4-163-180

16. Betelin V.B., Yudin V.A., Afanaskin I.V., Sozdanie otechestvennogo termogidrosimulyatora – neobkhodimyy etap osvoeniya netraditsionnykh zalezhey uglevodorodov Rossii (The creation of a domestic thermohydrosimulator is a necessary stage in the development of unconventional hydrocarbon deposits in Russia), Moscow: Publ. of Research Institute for System Studies of the RAS, 2015, 206 p.

17. Bashlykov A.A., Precedent theory methods applyed in the systems of decision-making when managing pipeline systems (In Russ.), Avtomatizatsiya, telemekhanizatsiya i svyaz' v neftyanoy promyshlennosti, 2016, no. 1, pp. 23-32.

18. Glukhikh I.N., P'yankov V.N., Zabolotnov A.R., Situational models in corporate knowledge bases of geological and technological activities (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2002, no. 6, pp. 45-48.

19. Khasanov M.M., Glukhikh I.N., Shevelev T.G. et al., Ontology-based approach to designing intelligent support systems for oil and gas engineering (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2022, no. 12, pp. 7-13, DOI: https://doi.org/10.24887/0028-2448-2022-12-7-13



Attention!
To buy the complete text of article (Russian version a format - PDF) or to read the material which is in open access only the authorized visitors of the website can. .