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Probabilistic-statistical estimation of reserves and resources in «Uncertainty_analysis» module of ROXAR RMS software

UDK: 553.98.048
DOI: 10.24887/0028-2448-2018-7-8-11
Key words: risk, probabilistic-statistical estimate, Monte-Carlo method, classification of oil and combustible gases reserves and resources, hydrocarbon reserves and resources management system (SPE-PRMS), comparison between domestic and international reserves estimation techniques
Authors: R.S. Khisamov (Tatneft PJSC, RF, Almetyevsk), A.F. Safarov (TatNIPIneft, RF, Bugulma), A.M. Kalimullin (TatNIPIneft, RF, Bugulma), A.A. Dryagalkina (TatNIPIneft, RF, Bugulma), A.V. Ramadanov (TatNIPIneft, RF, Bugulma)

Today, a large variety of hydrocarbon reserves and resources classifications exist in the oil industry, and each of them has its benefits and drawbacks. This paper presents analysis, comparison, and correlation of the results obtained from otherwise different methods of hydrocarbon reserves and resources estimation.

The objective of this paper is to discuss details of reserves estimation by different methods and examine the possibility and practicability of application of probabilistic approach to reserves estimate. Oil reserves have been estimated by volumetric method based on the geologic model generated by IRAP RMS software. Variation of volumetric parameters was assigned in Uncertainty module which makes it possible to build a geologic model with equally probable implementations with limited data on key reservoir characteristics. In estimating the uncertainty, variations were assigned for the following parameters: oil-water level, correction factor, porosity and water saturation. After calculations and search of possible implementations, the software generated the result in three parameters: P10 (possible), P50 (probable), and P90 (proved). To compare the results of reserves estimation, generated net pay maps were used that allow analyzing distribution of in-situ reserves.

The research suggests that input variables and different methods of 3D geological modelling affect the results in distribution of reservoir properties and key parameters for volumetric estimation of reserves. Multi-variant distribution of volumetric parameters in the geological environment provides consistent estimates of reserves (resources).

References

1. Kelliher C.F., Mahoney L.S., Using Monte Carlo simulation to improve long-term investment decision, The Appraisal Journal, 2000, no. 1, pp. 44–56.

2. Khisamov R.S., Safarov A.F., Kalimullin A.M., Primenenie litologo-fatsial'nogo analiza pri postroenii geologicheskoy modeli bobrikovskogo gorizonta Sirenevskogo mestorozhdeniya (In Russ.), Ekspozitsiya Neft' Gaz, 2017, no. 6, pp. 11–15.

3. Petroleum Resources Management System, URL: http://www.spe.org/industry/docs/Petroleum_Resources_Management_System_2007.pdf.

4. Gert A.A. et al., Stoimostnaya otsenka neftegazovykh mestorozhdeniy i uchastkov nedr s uchetom neopredelennosti i riskov (Valuation of oil and gas fields and subsoil blocks taking into account uncertainty and risks), Novosibirsk: Publ. of SNIIGGiMS, 2009, 227 p.В  В В 

Today, a large variety of hydrocarbon reserves and resources classifications exist in the oil industry, and each of them has its benefits and drawbacks. This paper presents analysis, comparison, and correlation of the results obtained from otherwise different methods of hydrocarbon reserves and resources estimation.

The objective of this paper is to discuss details of reserves estimation by different methods and examine the possibility and practicability of application of probabilistic approach to reserves estimate. Oil reserves have been estimated by volumetric method based on the geologic model generated by IRAP RMS software. Variation of volumetric parameters was assigned in Uncertainty module which makes it possible to build a geologic model with equally probable implementations with limited data on key reservoir characteristics. In estimating the uncertainty, variations were assigned for the following parameters: oil-water level, correction factor, porosity and water saturation. After calculations and search of possible implementations, the software generated the result in three parameters: P10 (possible), P50 (probable), and P90 (proved). To compare the results of reserves estimation, generated net pay maps were used that allow analyzing distribution of in-situ reserves.

The research suggests that input variables and different methods of 3D geological modelling affect the results in distribution of reservoir properties and key parameters for volumetric estimation of reserves. Multi-variant distribution of volumetric parameters in the geological environment provides consistent estimates of reserves (resources).

References

1. Kelliher C.F., Mahoney L.S., Using Monte Carlo simulation to improve long-term investment decision, The Appraisal Journal, 2000, no. 1, pp. 44–56.

2. Khisamov R.S., Safarov A.F., Kalimullin A.M., Primenenie litologo-fatsial'nogo analiza pri postroenii geologicheskoy modeli bobrikovskogo gorizonta Sirenevskogo mestorozhdeniya (In Russ.), Ekspozitsiya Neft' Gaz, 2017, no. 6, pp. 11–15.

3. Petroleum Resources Management System, URL: http://www.spe.org/industry/docs/Petroleum_Resources_Management_System_2007.pdf.

4. Gert A.A. et al., Stoimostnaya otsenka neftegazovykh mestorozhdeniy i uchastkov nedr s uchetom neopredelennosti i riskov (Valuation of oil and gas fields and subsoil blocks taking into account uncertainty and risks), Novosibirsk: Publ. of SNIIGGiMS, 2009, 227 p.В  В В 



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