Petroleum reservoir management involves generation of multiple field development scenarios and portfolio of the best production enhancement technologies of an oil company given production and capital cost restrictions. Decision support systems based on fuzzy set methods and expert systems have gained wide acceptance for selection of production enhancement operations. The paper describes one of the approaches to long-term strategic planning based on approximate estimates of the performance of various simulation cases for all fields operated by a company using statistical proxy models. This enables assessment of company expenses and long-term economic performance. In Tatneft Company, this approach is implemented in Epsilon software package. Epsilon integrates hierarchical proxy models of Lazurit workstation. The models fully reproduce the production history and generate remaining oil reserve maps showing unswept areas and bypassed oil pockets, being potential candidates for infill drilling. The workflow allows for automatic, step-wise introduction of planned operations with gradual increase of well grid spacing. Each stage provides for evaluation of geological risks and profitability of each well. Those wells that do not meet specified conditions are excluded. At every simulation time step, remaining oil reserves are automatically redistributed according to extent of their depletion. Automatic execution of the algorithm yields a nonuniform, dense grid of planned production enhancement operations that meet geological, operational, and economic constraints. The paper describes optimization problem solution for different objective functions and constraints. To estimate the production and economic performance of Tatneft’s production assets and to optimize the investments in the selected development scenarios for each target, we applied two taxation systems. Analysis of the key performance indicators and selection of the best development scenario for the Company’s fields made it possible to decide on the most appropriate taxation system for a particular field.cie
References
1. Gharbi R., Application of an expert system to optimize reservoir performance, Journal of Petroleum Science and Engineering, 2005, V. 49, no. 3-4, pp. 261–273, DOI: http://dx.doi.org/10.1016/j.petrol.2005.05.008
2. Nageh M., Abu M., Sayed E., Sayyouh H., Application of using fuzzy logic as an artificial intelligence technique in the screening criteria of the EOR technologies, SPE-175883-MS, 2015, DOI: https://doi.org/10.2118/175883-MS
3. Bing Han, Xiaoqiang Bian, A hybrid PSO-SVM-based model for determination of oil recovery factor in the low-permeability reservoir, Petroleum, 2018, V. 4, no. 1, pp. 43–49, DOI: https://doi.org/10.1016/j.petlm.2017.06.001
4. Aldhaheri M., Wei M., Bai B., Alsaba M., Development of machine learning methodology for polymer gels screening for injection wells, Journal of petroleum science & engineering, 2017, V. 151, pp. 77-93, DOI: https://doi.org/10.1016/j.petrol.2016.12.038
5. Tarrahi M., Afra S., Surovets I., A novel automated and probabilistic EOR screening method to integrate theoretical screening criteria and real field EOR practices using machine learning algorithms, SPE-176725-MS, 2015, DOI: https://doi.org/10.2118/176725-MS
6. Khazalia N., Sharifia M., Ahmadi M.A., Application of fuzzy decision tree in EOR screening assessment, Journal of Petroleum Science and Engineering, 2019, V. 177, pp. 167–180, DOI: https://doi.org/10.1016/j.petrol.2019.02.001
7. Ramos G.A.R., Akanji L., Application of artificial intelligence for technical screening of enhanced oil recovery methods, Journal of Oil, Gas and Petrochemical Sciences, 2017, V. 0, pp. 6–16, DOI: https://doi.org/10.30881/jogps.00002
8. AhmadiM.A., Soleimani R., Lee M. et al., Determination of oil well production performance using artificial neural network (ANN) linked to the particle swarm optimization (PSO) tool, Petroleum, 2015, V. 1, no. 2, pp. 118–132, DOI: https://doi.org/10.1016/j.petlm.2015.06.004
9. Panja P., Velasco R., Pathak M., Deo M., Application of artificial intelligence to forecast hydrocarbon production from shales, Petroleum, 2018, V. 4, no. 1, pp. 75–89, DOI: https://doi.org/10.1016/j.petlm.2017.11.003
10. Wang Sh., Chen Z., Chen Sh., Applicability of deep neural networks on production forecasting in Bakken shale reservoirs, Journal of Petroleum Science and Engineering, 2019, V. 179, pp. 112–125, DOI: https://doi.org/10.1016/j.petrol.2019.04.016
11. Ruiz M., Alzate-Espinosa G., Obando A., Alvares H., Combined artificial intelligence modeling for production forecast in an oil field, CT&F - Ciencia, Tecnologia y Futuro, 2019, V. 9, no. 1, pp. 27–35, DOI: https://doi.org/10.29047/issn.0122-5383
12. Krasnov F., Glavnov N., Sitnikov A., A machine learning approach to enhanced oil recovery prediction, Analysis of Images, Social Networks and Texts: 6th International Conference, AIST 2017, Moscow, Russia, 2017 July 27–29, Cham: Springer, 2018, pp. 164–171.
13. Suleimanov B.A., Ismayilov F.S., Dyshin O.A., Veliyev E.F., Selection methodology for screening evaluation of EOR methods, Petroleum Science and Technology, 2016, V. 34, no. 10, pp. 961-970, DOI: https://doi.org/10.1080/10916466.2015.1107849
14. 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.
15. Nasybullin A.V., Sattarov Ram.Z., LatifullinF.M. et al., Creation of software tool for long-term investment planning with a view to the effective development of oil fields (In Russ.), Neftjanoe hozjajstvo = Oil Industry, 2019, no. 12, pp. 128–131, DOI: https://doi.org/10.24887/0028-2448-2019-12-128-131.
16. Zvezdin E.Ju., MannapovM.I., Nasybullin A.V. et al., Stage-wise optimization of project well pattern using oil reserves evaluation program module (In Russ.), Neftjanoe hozjajstvo = Oil Industry, 2019, no. 7, pp. 28–31, DOI: https://doi.org/10.24887/0028-2448-2019-7-28-31.
17. Ganiev B.G., Nasybullin A.V., Sattarov Ram.Z. et al., Application of machine learning method for planning of well drilling in producing oil formations (In Russ.), Neftjanoe hozjajstvo = Oil Industry, 2021, no. 7, pp. 23–27, DOI: https://doi.org/10.24887/0028-2448-2021-7-23-27.
18. Poirriez V., Yanev N., Andonov R., A hybrid algorithm for the unbounded knapsack problem, Discrete Optimization, 2009, V. 6, no. 1, pp. 110–124, DOI: https://doi.org/10.1016/j.disopt.2008.09.004
