Methodological assessment of uncertainty in forecasting hydrocarbon reserves of the North Varieganskoye field (Western Siberia)

ISSN 2307-2091 (Print) 

ISSN 2500-2414 (Online)

 

I. A. Lebedeva, S. G. Panyak / News of the Ural State Mining University. 2021. Issue 1(61), pp. 46-54

https://doi.org/10.21440/2307-2091-2021-1-46-54

 

Relevance. The assessment of uncertainties for the calculation of hydrocarbon reserves is a timely topic. The quality
of hydrocarbon reserves is declining as old oil fields are depleted. Rates of discovery are in decline since most of
the territories prospectively rich in hydrocarbon resources have already been explored. Newly discovered fields are
classified as small and medium in terms of the amount of hydrocarbon reserves and difficult in terms of the quality
of reserves.
Purpose of the research is to determine important reservoir properties that have the greatest impact on the variability
of the initial hydrocarbon reserves using sensitivity analysis and conducting a probabilistic hydrocarbon reserves
assessment by the Monte Carlo method.
Methods of research. The probabilistic hydrocarbon reserves assessment was carried out by the Monte Carlo method
with the corresponding frequency curves of the probability distribution of volumetric parameters, which were
performed using the Oracle Crystal Ball application. The sensitivity analysis of volumetric parameters that have the
greatest impact on the amount of the initial hydrocarbons reserves was performed using the Oracle Crystal Ball
application as well.
Results and their application. An assessment of uncertainties and risks is necessary both for newly discovered fields
in order to negate the risk of drilling unproductive wells and mature fields for a targeted program of geological and
technological measures (GTM).
Conclusions. The formation of bedded sand bodies of the Vasyugan (SE11, SE12, SE13) and Tyumen (SE2) suites took
place in various sedimentation conditions, which could not but affect the quality of the reservoir of the studied
formations. The analysis of the sensitivity of parameters has shown that different parameters affect the amount of
hydrocarbon reserves in places SE11, SE12, SE13 and place SE2 .

Keywords: mature deposits, genesis of sediments, forecast uncertainties, risks, probabilistic hydrocarbon reserves
assessment (Monte Carlo method), parameters sensitivity analysis.

 

REFERENCES

1. Panyak S. G., German V. I. 2010, New methodology for prospecting small and medium hydrocarbon deposits. Izvestiya vysshikh uchebnykh
zavedeniy. Neft’ i gaz [Oil and Gas Studies], no. 3, pp. 4—8. (In Russ.)
2. Polozov M. B., Al-Rumaima D. M. 2018, Improving the efficiency of developing residual reserves at the later stages of development. Upravleniye
tekhnosferoy [Management of the Technosphere], vol. 1, issue 3, pp. 275—286. (In Russ.) URL: http://f-ing.udsu.ru/technosphere
3. Panyak S. G., Lebedeva I. А. 2018, Pribrezhno-morskiye usloviya formirovaniya verkhneyurskikh otlozheniy na osnove fatsial'nogo analiza
(Zapadnaya Sibir') [Coastal-marine conditions for the formation of Upper Jurassic deposits based on facies analysis (Western Siberia)]. Ural
Mining School for Regions: International research and practice conference (Ekaterinburg, April 9—18, 2018). Ekaterinburg, pp. 63—64.
4. Christian P. Robert. 2011, Simulation in statistics. Proceedings of the 2011 Winter Simulation Conference S. Jain, R. R. Creasey, J. Himmelspach,
K. P. White, and M. Fu, eds., 12 р.
5. Robert P. C., Casella G. Monte Carlo statistical methods. N. Y.: Springer-Verlag, 1999. 507 р.
6. Rose P. R. 2001, Risk analysis and management of petroleum exploration ventures. American Association of Petroleum Geologists, vol. 12,
pp. 17–48.
7. Mirzadzhanzade A. Kh., Khasanov M. M., Bakhtizin R. N. 2005, Modelirovaniye protsessov neftegazodobychi [Modeling of oil and gas
production processes]. Moscow, 368 p.
8. Mirzadzhanzade A. Kh. 1977, Matematicheskaya teoriya eksperimenta v dobyche nefti i gaza [Mathematical theory of experiment in oil and
gas production]. Moscow, 233 p.
9. Collins R. E., Jordan J. K. 1961, Porosity And Permeability Distribution Of Sedimentary Rocks. SPE 212-MS.
10. Altunin A. E., Semukhin M. V., Yadryshnikova O. A. 2017, Probabilistic and fuzzy models for assessing uncertainties and risks when calculating
hydrocarbon reserves. Information technologies. Vestnik Tyumenskogo gosudarstvennogo universiteta [Tyumen State University Herald],
Physical and mathematical modeling. Oil, gas, and energy, vol. 3, no. 2, pp. 85–99. (In Russ.) https://doi.org/10.21684/2411-7978-2017-3-2-85-99
11. Dorogobed A. N., Kuntsev V. E., Kozhevnikova P. V. 2019, Using the Monte Carlo method to control the assessment of the reliability of
geological models. Sovremennyye naukoyemkiye tekhnologii [Modern high technologies], no. 9, pp. 80—84. (In Russ.)
geological models. Sovremennyye naukoyemkiye tekhnologii [Modern high technologies], no. 9, pp. 80—84. (In Russ.)
12. Bilinchuk A. V., Sitnikov A. N., Asmandiyarov R.N., Pustovskikh A. A., Zulkarniev R. Z., Cherevko S. A. 2015, Formation of a geological rating
for well drilling is the basis for planning a complex asset development project. Neftyanoye khozyaystvo [ Oil industry], no. 12, pp. 10—12. (In
Russ.)
13. Shatrov S. V. 2013, Probabilistic evaluation of oil resources on block 12, Iraq. Neftyanoye khozyaystvo [Oil industry], no. 4, pp. 86—89. (In
Russ.)
14. Sitnikov A. N., Pustovskikh A. A., Margarit A. S., Belonogov E. V.,. Zulkarniev R. S, Korovin A. Yu. 2016, Methodology of drilling targets
selection under geological uncertainty. Neftyanoye khozyaystvo [Oil industry], no. 12, pp. 44—47. (In Russ.)
15. Shatrov S. V. 2012, Probabilistic evaluation of oil and gas exploration assets. Neftyanoye khozyaystvo [Oil industry], no. 4, pp. 13—17. (In
Russ.)

Лицензия Creative Commons
All articles posted on the site are available under the Creative Commons Attribution 4.0 Global License.