The role and significance of geological heterogeneity in the formation of limestone productivity in the Famennian stage of the South Tatar arch

The purpose of the research is to identify the degree and nature of the geological heterogeneity impact on deposit productivity in order to use the obtained patterns in solving fundamental problems of oil field development considering different density of geological and field information and drastically uneven data of well operation. The object of the study is the Famennian stage deposits of the South Tatar arch located within the Volga-Ural oil and gas province. The use of the elements of multidimensional regression modeling allowed to obtain the dependences of deposit productivity on the number of geological and field parameters resulting in the establishment of the following features: the degree of parameters influence on productivity varies in the conditions of selected groups of identical objects; there are some cases featuring the different nature of parameter influence, which proves the relevance and necessity of implementing differentiation procedures when making a forecast in order to reduce the risks of making ineffective management decisions. The presented results can be used for reasonable estimation of deposit capacity at the stage of their commissioning. Since the error of the presented models is a quarter higher than the one of the models including the indicators of geological heterogeneity, it is quite reasonable to use them for drafting the first project documents as they allow subsoil users to successfully formulate an optimal approach to the implementation of an effective field development system.

1. Shcherbakov A.A., Khizhnyak G.P., Galkin V.I. Prediction of sidetrack wells productivity index (on example of the Unvinskoe field). Bulletin of the Tomsk Polytechnic University. Geo Assets Engineering. 2019;330(5):93-99. (In Russ.). https://doi.org/10.18799/24131830/2019/5/272. EDN: KFTCJV.

2. Tiab J., Donaldson E.Ch. Petrophysics: theory and practice of measuring reservoir rock and fluid transport properties; 2004, 880 p. (Russ. ed.: Petrofizika: teoriya i praktika izucheniya kollektorskikh svoistv gornykh porod i dvizheniya plastovykh flyuidov. Moscow: Premium Engineering; 2009, 868 p.).

3. Mukhametshin V.V., Andreev V.E. Increasing the efficiency of assessing the performance of techniques aimed at expanding the use of resource potential of oilfields with hard-to-recover reserves. Bulletin of the Tomsk Polytechnic University. Geo Assets Engineering. 2018;329(8):30-36. (In Russ.). EDN: XWCJOP.

4. Poplygin V.V., Mordvinov V.A. Assessing production well flow factors variation when bottom-hole pressure is below saturation pressure. Perm Journal of Petroleum and Mining Engineering. 2009;8(4):53-58. (In Russ.). EDN: SIGBJR.

5. Mordvinov V.A., Martyushev D.A., Ladeishchikova T.S., Gorlanov N.P. Assessing the effect of natural reservoir fracturing on the flow dynamics of the Ozernoye field production wells. Perm Journal of Petroleum and Mining Engineering. 2015;14(14):32-38. (In Russ.). https://doi.org/10.15593/2224-9923/2015.14.4. EDN: TMZMJR.

6. Nazarova L.N., Kazetov S.I., Ganiev A.L., Urazakov K.R. The method of calculation of well productivity in heterogeneous permeability reservoirs. Neft’. Gaz. Novatsii. 2018;4:51-55. (In Russ.). EDN: XNRQKT.

7. Sultanov V.F., Sultanov F.F. A tool for prompt calculation of well startup parameters based on the results of geophysical survey data interpretation. In: Novye tekhnologii v gazovoi promyshlennosti: gaz, neft’, ehnergetika: XIV Vseros. konf. molodykh uchenykh, spetsialistov i studentov: tezisy dokladov = New technologies in the gas industry: gas, oil, energy: the 14th All-Russian conference of young scientists, specialists and students: report abstracts. 14–18 November 2022, Moscow. Moscow; 2022, p. 428-429. (In Russ.). EDN: TTCCAX.

8. Gilyazetdinov R.A., Kuleshova L.S., Mukhametshin V.V., Yakupov R.F., Grishchenko V.A. Clarification of the results of solving the problems of developing deposits of the Volga-Ural oil and gas province using methods of ranking geological and statistical models. Earth Sciences and subsoil use. 2023;46(4):402-412. (In Russ.). https://doi.org/10.21285/26869993-2023-46-4-402-412. EDN: KLCTFQ.

9. Kuleshova L.S., Gilyazetdinov R.A., Mukhametshin V.Sh. Identification of deposits is the basis for the creation of artificial intelligence in the development of oil fields in the Ural-Volga region. Oil. Gas. Innovations. 2024;1:14-19. (In Russ.). EDN: VDTTNV.

10. Ladeishchikova T.S., Volkov V.A., Sobyanin N.N., Mitroshin A.V. Indirect methods for estimating current formation pressure in a well to be applied when building integrated models of fields. Oilfield engineering. 2021;7:39-45. (In Russ.). https://doi.org/10.33285/0207-2351-2021-7(631)-39-45. EDN: QOGVIS.

11. Gilyazetdinov R.A., Kuleshova L.S., Mukhametshin V.S. Risk reduction in the development of hard-to-recover liquid hydrocarbon reserves using a comprehensive analysis of geological and field data. News of the Ural State Mining University. 2024;1:106-113. (In Russ.). https://doi.org/10.21440/2307-2091-2024-1-106-113. EDN: ZVUVHQ.

12. Mwakipunda G.C., Yang Z., Guo C. Infill drilling optimization for enhanced oil recovery by waterflooding: a simulation study. Journal of Energy Engineering. 2023;149(1):4022053. https://doi.org/10.1061/(ASCE)EY.19437897.0000860.

13. Ramiro-Ramirez S., Bhandari A.R., Reed R.M., Flemings P.B. Permeability of upper Wolfcamp lithofacies in the Delaware Basin: the role of stratigraphic heterogeneity in the production of unconventional reservoirs. AAPG Bulletin. 2024;108(2):293 -326. https://doi.org/10.1306/12202222033.

14. Tavana M., Soltanifar M., Santos-Arteaga F.J. Analytical hierarchy process: revolution and evolution. Annals of Operations Research. 2023;326(2):879-907. https://doi.org/10.1007/s10479-021-04432-2.

15. Mukhametshin V.Sh., Tyncherov K.T., Rakhimov N.R. Geological and statistical modeling of oil recovery of carbonate formations. Journal of Physics: Conference Series. 2021;1753:12080. https://doi.org/10.1088/1742-6596/1753/1/012080.

16. Mukhametshin V.Sh. Calculation and forecast of current and final oil recovery from wells during depletion. Journal of Physics: Conference Series. 2021;2032:12047. https://doi.org/10.1088/1742-6596/2032/1/012047.

17. Dorfman M.B., Tufanova O.P. I nfluence of the created drawdowns during wells operation on the productivity coefficient change in carbonate reservoirs. Equipment and technologies for oil and gas complex. 2019;4:52-57. (In Russ.). https://doi.org/10.33285/1999-6934-2019-4(112)-52-57. EDN: MEJBVN.

18. Imqam A., Bai B., Wei M., Elue H., Muhammed F.A. Use of hydrochloric acid to remove filter-cake damage from preformed particle gel during conformance-control treatments. SPE Production & Operations. 2016;31(3):11. https://doi.org/10.2118/172352-PA.

19. Martyushev D.A., Ponomareva I.N. Researching features of development of fractured porous reservoirs reserves using well-test data. Oil Industry Journal. 2017;10:102-104. (In Russ.). https://doi.org/10.24887/0028-2448-2017-10-102104. EDN: ZPDYKR.

20. Kozubovsky A.G., Kuzmina T.V. The impact of development of reservoir on well productivity. PROneft. Professionally about Oil. 2022;7(2):32-40. (In Russ.). https://doi.org/10.51890/2587-7399-2022-7-2-32-40. EDN: OELFQN.