Selection of ranking parameters during prioritization of planned well drilling using Epsilon software to improve profitability in specific economic environment

UDK: 622.24.002.2

DOI: 10.24887/0028-2448-2025-7-18-21

Key words: candidate well ranking, prioritization of well drilling, priority of putting wells on stream, Epsilon software package, field development scenarios

Authors: М.I. Маnnapov (Tatneft-Dobycha JV, RF, Almetyevsk); V.V. Yemelyanov (Tatneft-Dobycha JV, RF, Almetyevsk); A.V. Nasybullin (Almetyevsk State Technological University «Petroleum Higher School», RF, Almetyevsk); R.Z. Sattarov TatNIPIneft, RF, Almetyevsk); М.А. Sharifullina TatNIPIneft, RF, Almetyevsk); М.F. Latifullina TatNIPIneft, RF, Almetyevsk

The paper presents key aspects of a drilling sequence planning considering the need to put the most promising wells on stream on a first-priority basis. The authors analyze the ranking parameters for candidate wells considered for well interventions including drilling, and identify several groups of criteria that can be used to evaluate their efficiency. To minimize the risk of improper candidate well selection, it is more efficient to consider complex parameters that were formed using several geological and technological criteria. A weighting factor can be assigned to each criterion which will define its priority. Candidate well ranking can be performed based on the predicted efficiency parameters obtained from machine learning models. The paper presents data on the existing algorithms developed by the authors under the Epsilon project, which optimize the process of selecting candidate wells for drilling. A comparative analysis of two methods of ranking the planned well clusters was carried out: based on integral absolute parameters (residual oil saturation, predicted oil production rate, net present value (NPV)) and based on specific parameters (discounted profitability index, cumulative production per well, NPV per Ruble of investment). It is shown that ranking of single planned wells can be based on absolute parameters, while ranking of well clusters should be based on relative (or specific) parameters. The proposed methods are aimed at improving the accuracy and efficiency of planning well interventions, including drilling operations.

References

1. Tyul’kov A.T., Permyakov A.V., Shakirov R.R., Methodology for ranking candidate wells for conducting geological and technical measures at a gas condensate field with significant reserves for commissioning from long-term conservation (In Russ.), Sfera. Neft’ i gaz, 2021, no. 3, pp. 30-34.

2. Sinitsyna T.I., Galeev A.A., Methodology of automated selection of well candidates for hydraulic fracturing at Kharampurneftegaz fields (In Russ.), Neftyanaya provintsiya, 2022, no. 4, pp. 239–251, DOI: https://doi.org/10.25689/NP.2022.4.239-251

3. Sinitsyna T.I., Gorbunov A.N., Automation of workover candidate ranking processes at Krasnoleninskoye oil and gas condensate field (In Russ.), PROneft’. Professional’no o nefti, 2021, V. 6, no. 4, pp. 116–122, DOI: https://doi.org/10.51890/2587-7399-2021-6-4-116-122

4. Mashkantseva T.I., Knyazev A.V., Olyunina A.G., Kanaykin C.P., Integrated approach to selection of candidate wells for interventions (based on example of Talinskaya license area, Krasnoleninskoye oil-gas-condensate field) (In Russ.), Nauchno-tekhnicheskiy vestnik OAO “NK “Rosneft’”, 2016, no. 1, pp. 34–37.

5. Kochnev A.A., Kozyrev N.D., Kochneva O.E., Galkin S.V., Development of a comprehensive methodology for the forecast of effectiveness of geological and technical measures based on machine learning algorithms (In Russ.), Georesursy, 2020, V. 22, no. 3, pp. 79–86, DOI: https://doi.org/10.18599/grs.2020.3.79-86

6. Azbukhanov A.F., Kostrigin I.V., Bondarenko K.A. et al., Selection of wells for hydraulic fracturing based on mathematical modeling using machine learning methods

(In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2019, no. 11, pp. 38–42, DOI: https://doi.org/10.24887/0028-2448-2019-11-38-42

7. Andronov Yu.V., Metodika operativnoy otsenki perspektivnosti skvazhin dlya metodov intensifikatsii pritoka nefti s primeneniem neyronnykh setey i derev’ev resheniy (Methodology for operational assessment of well prospects for oil flow stimulation methods using neural networks and decision trees): thesis of candidate of technical science, Moscow, 2019.

8. Certificate of state registration of a computer program no. 2020665887 RF. Programmnyy kompleks podderzhki prinyatiya resheniy po formirovaniyu mnozhestva predpochtitel’nykh variantov geologo-tekhnicheskikh meropriyatiy (vvoda skvazhin v ekspluatatsiyu) pri razrabotke neftyanogo mestorozhdeniya (Software package for decision support for the formation of a set of preferred options for geological and technical measures (commissioning of wells) during the development of an oil field), Authors: Katasev A.S., Kataseva D.V., Anikin I.V. et al.

9. Khisamov R.S., GanievB.G., Galimov I.F. et al., Computer-aided generation of development scenarios for mature oil field (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2020, no. 7, pp. 22–25, DOI: https://doi.org/10.24887/0028-2448-2020-7-22-25

10. Certificate of state registration of a computer program no. 2020616438 RF. Programmnyy kompleks avtomaticheskogo kustovaniya skvazhin (Software package for automatic well clustering), Authors: Akhmetov N.A., Boyarov F.G., Vasyutin V.A. et al.