Neftyanaya Provintsiya Journal

No.3(19),2019

MODELING OF SATURATION BEHAVIOR BASED ON SEISMIC FORECAST OF PETROPHYSICAL PARAMETERS (on the example of Achimov deposits of a field in YANAO)

Kalashnikova M.P., Yanevits R.B., Natchuk N.Yu., Sitdikov R.R.

DOI https://doi.org/10.25689/NP.2019.3.115-128

PP.115-128

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Abstract

The paper considers a problem of water saturation determination in the interwell space based on comprehensive analysis of well logging and seismic data, as well as identification of reservoirs with high potential of water breakthrough. The authors analyze relations between elastic parameters and petrophysical properties and saturation, and present procedure for generation of a resistivity cube using a neural network algorithm. Quality assessment has been performed, as well as analysis of production operations effect on input data used for resistivity cube forecasting. The paper presents actual drilling data confirming water saturation forecast made from seismic data. It also demonstrates how the results obtained affect the final geologic model and reserves estimation.

Key words:

sedimentary deposits, gas field, water producing interval, seismic forecast, neural network

References

 

1. V.V. Kruglov, V.V. Borisov Gibridnye nejronnye seti [Hybrid neural networks]. Smolensk, Rusich, 2001, 224 p. (in Russian)

2. R.B. Yanevits, O.A. Sokolovskaya, L.V. Lapina, N.V. Kholmanskikh Ispol'zovanie nejrosetevyh algoritmov pri prognoze petrofizicheskih svojstv tonkosloistogo razreza po dannym sejsmorazvedki i GIS (na primere achimovskih otlozhenij mestorozhdeniya v YANAO) [Use of neural network algorithms to forecast petrophysical properties of thinlayer structures from seismic survey data and well logging data]. Geologiya, geofizika i razrabotka neftyanyh i gazovyh mestorozhdenij [Oil and gas field geology, geophysics and development], No.7, 2017 (in Russian)

3. Masters T. Advanced algorithms for neural networks.- John Wiley & Sons, Inc. - 1995.

4. Specht Donald. A general regression neural network. IEEE Transactions on Neural Networks. - № 2(6). - 1991. - P. 568-576.

5. Specht Donald. Probabilistic neural networks. Neural Networks. № 3. 1990. P.109-118.

6. Hampson D.P., J.S. Schuelke, and J.A. Quirein, 2011, Use of multiattribute transforms to predict log properties from seismic data. Geophysics, Vol. 66, No. 1, p. 220-239.

Authors

Kalashnikova M.P., LLC «Tyumen Petroleum Research Center», Tyumen, Russian Federation E-mail: mpkalashnikova@tnnc.rosneft.ru

Yanevits R.B., LLC «Tyumen Petroleum Research Center», Tyumen, Russian Federation E-mail: rbyanevits@tnnc.rosneft.ru

Natchuk N.Yu, LLC «Tyumen Petroleum Research Center», Tyumen, Russian Federation E-mail: nynatchuk@tnnc.rosneft.ru

Sitdikov R.R., AO Rospan International, Novy Urengoy, Russian Federation E-mail: rrsitdikov2@rspn.rosneft.ru

For citation:

M.P. Kalashnikova, R.B. Yanevits, N.Yu. Natchuk, R.R. Sitdikov Modelirovanie haraktera nasyshhenija na osnove sejsmicheskogo prognoza petrofizicheskih parametrov (na primere achimovskih otlozhenij mestorozhdenija v JaNAO) [Modeling of saturation behavior based on seismic forecast of petrophysical parameters (on the example of achimov deposits of a field in yanao)]. Neftyanaya Provintsiya, No. 3(19), 2019. pp. 115-128. https://doi.org/10.25689/NP.2019.3.115-128 (in Russian)