Analysis of the reservoir energy state and optimization of well production based on the results of proxy-modeling

UDK: 622.276.1/.4.001.57
DOI: 10.24887/0028-2448-2021-12-44-50
Key words: proxy-model, pore pressure map, steady filtration, production forecast, well performance optimization, adaptation to the well test data, bottomhole pressure, reservoir pressure, permeability
Authors: E.V. Yudin (Gazpromneft STC LLC, RF, Saint-Petersburg), N.S. Markov (Peter the Great Saint-Petersburg Polytechnic University, RF, Saint-Petersburg), V.S. Kotezhekov (Gazpromneft STC LLC, RF, Saint-Petersburg), A.V. Makhnov (Peter the Great Saint-Petersburg Polytechnic University, RF, Saint-Petersburg), S.O. Kraeva (Peter the Great Saint-Petersburg Polytechnic University, RF, Saint-Petersburg), E.R. Gadelshina (Peter the Great Saint-Petersburg Polytechnic University, RF, Saint-Petersburg), L.A. Gorbushin (Peter the Great Saint-Petersburg Polytechnic University, RF, Saint-Petersburg), N.P. Trubnikov (Peter the Great Saint-Petersburg Polytechnic University, RF, Saint-Petersburg)

The use of physically meaningful models as a basis for building pore pressure maps and analyzing well productivity is necessary to improve the efficiency of monitoring of the reservoir energy state and planning of geological activities. Therefore, the development of tools for the automatic construction of pore pressure maps based on the results of physically meaningful calculations is an urgent and demanded task. The main requirement for the model is a high speed of calculations, which is critical for timely updating of data on the state of the reservoir. Taking this aspect into account, proxy modeling based on the use of algorithms that provide a high and acceptable for practical application speed of calculation is a more suitable approach than full-scale 3D modeling.

This article is devoted to the development of a proxy model based on a two-dimensional diffusion equation, which is solved using the boundary element method. The developed tool allows to adapt the parameters of the proxy model in such a way that the resulting pore pressure field satisfies the conditions that can be specified based on the average reservoir pressures according to well test data. This adaptation of the model to the technological regime data is carried out automatically, which is of great importance for practical engineering tasks. The developed proxy model makes it possible to build a pore pressure map in a fairly short computation time. The deviation of the estimated reservoir pressures from well test data when using a proxy model is (on average) 2 times lower than when using traditional methods for constructing isobars. Due to the fact that the pore pressure map is built on a physically meaningful model, it is also possible to optimize well performance based on the parameters determined at the stage of mapping. This option was implemented in addition to the basic proxy model algorithm. Another one extension of the basic algorithm is adaptation of the local pore pressure to piezometric measurements. As well as the adaptation to well test data, this adaptation is carried out automatically. The developed proxy model was tested both on synthetic data and on five real fields. The obtained pore pressure maps were analyzed; conclusions were drawn about the limitations of the current version of the proxy model. Based on these conclusions, further directions of its development and expansion of the area of applicability were defined.

 References

1. Sharifov A.R., Perets D.S., Zhdanov I.A. et al., Tool for operational well stock management and forecasting, SPE-201927-MS, 2020, DOI: https://doi.org/10.2118/201927-MS

2. Zhao H., Kang Z., Zhang X. et al., INSIM: A data-driven model for history matching and prediction for waterflooding monitoring and management with a field application, SPE-173213-MS, 2015, DOI: https://doi.org/SPE-173213-MS

3. Jamali A., Ettehadtavakkol A., Application of capacitance resistance models to determining interwell connectivity of large-scale mature oil fields, Petroleum Exploration and Development, 2017, no. 44(1), pp. 132–138, DOI:10.1016/S1876-3804(17)30017-4

4. Sayarpour M., Kabir C.S., Lake L.W., Field applications of capacitance-resistance models in waterfloods, SPE-114983-PA, 2009, DOI: https://doi.org/10.2118/114983-PA

5. Simonov M.V., Penigin A.V., Margarit A.S. et al., Methodology of surrogate models (metamodels) and their prospects for solving petroleum engineering challenges (In Russ.), PROneft'. Professional'no o nefti, 2019, no. 2, pp. 48–53, DOI:10.24887/2587-7399-2019-2-48-53

6. Temirchev P., Simonov M., Kostoev R. et al., Deep neural networks predicting oil movement in a development unit (In Russ.), Journal of Petroleum Science and Engineering, 2020, V. 184, DOI: 10.1016/j.petrol.2019.106513.

7. Yudin E.V., Gubanova A.E., Krasnov V.A., The method of express estimation of pore pressure map distribution in reservoirs with faults and wedging zones (In Russ.), SPE-191582-18RPTC-MS, 2018, DOI: https://doi.org/10.2118/191582-18RPTC-MS

8. Yudin E.V., Gubanova A.E., Krasnov V.A., Method for estimating the wells interference using field performance data (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2018, no. 8, pp. 64–69, DOI: 10.24887/0028-2448-2018-8-64-69

9. Banerjee P.K., The boundary element methods in engineering, London: McGraw-Hill, 1994, 494 p.

10. Brown M. et al., Practical solutions for pressure-transient responses of fractured horizontal wells in unconventional shale reservoirs, SPE-125043-PA, DOI: https://doi.org/10.2118/125043-PA



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