Abstract

Deterministic methods are typically used for reservoir and production engineering or well intervention planning. Deterministic reservoir simulation models or design equations that are usually used provide only a single estimate of reserves and production. However, distribution of reservoir properties is random due to their significant areal and vertical heterogeneity, as well as uncertainty of their measurement. Therefore, accurate forecast of oil production rate is impossible. It is only possible to define a degree of possibility for a certain well flow rate.

For this reason, stochastic methods of reserve estimation based, for example, on Monte-Carlo technique or 3D geologic models have been successfully used for quite a long time. These methods provide a probabilistic estimate of oil reserves. However, when proceeding to field development analysis and planning, the entire group of geologic models turns into the same group of reservoir simulation models differing by geological structure representation. Labor-consuming nature of their generation and history matching makes it impossible to obtain a real-time probabilistic estimate of well flow rates.

This paper discusses the development of technique for well flow rate probabilistic forecasting based on stochastic approach to geologic reservoir description. This technique is quite simple and does not require significant time spending and computation effort. The main concept of the technique is estimating parameters in a certain point of a reservoir, obtaining parameter density function and evaluating production rate of a new well in a certain point by Monte-Carlo method.

Key words:

oil production forecast, geostatistics, geostochastics, Mote-Carlo method, variogram, interpolation, declustering

References

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Authors

For citation:

M.N. Khanipov, B.G. Ganiev, A.V. Nasybullin, R.Z. Sattarov Razrabotka metodiki verojatnostnogo prognozirovanija dobychi nefti [Development of oil production probabilistic forecasting technique]. Neftyanaya Provintsiya, No. 2(22), 2020. pp.73-94. DOI https://doi.org/10.25689/NP.2020.2.73-94 (in Russian)