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Automation of fluid contact surfaces construction in geology modeling

UDK: 550.8
DOI: 10.24887/0028-2448-2020-1-42-45
Key words: geological model, declustering methods, oil-water contact, surface construction
Authors: K.E. Zakrevskiy (Rosneft Oil Company, RF, Moscow), M.I. Saakyan (Rosneft Oil Company, RF, Moscow), A.E. Lepilin (RN-BashNIPIneft LLC, RF, Ufa), Ch.R. Akhmetov (RN-BashNIPIneft LLC, RF, Ufa)

The article is devoted to some aspects of optimizing the process of digital geological modeling of oil and gas fields. The algorithm for constructing fluid contact surfaces is proposed, which allows to automate the stage of geometrization of oil and gas deposits, speed up the modeling process, and reduce the number of errors at the same time. The algorithm contains the following computing units:

– analyzing and pre-processing the initial data, namely, geophysical “collector – non-collector” interpretation, “gas – oil – water” interpretation and zone markers;

– calculating the surface of fluid contacts by minimizing the deviation of known initial data relative to a certain surface;

– data declustering allowing to reduce the influence of tightly located data groups;

– minimizing discrepancies between the calculation result and the initial data, which allows “to hook” the obtained contact surface onto the markers.

Obtained results have been illustrated by examples of automated construction of contact surfaces on deposits of the West Siberian and Volga-Urals oil and gas provinces. The proposed algorithm can be used manually and automatically for updating and adaptation of fluid contact surfaces to changed input data at the stage of reservoir geometrization. The algorithm most effectively manifests itself in large oil and gas fields with a large number of wells. In addition to constructing fluid contact surfaces, the application of the algorithm allows the geologist-designer to analyze the well data for the presence of wells with significant inclinometry errors.

References

1. Saakyan M.I., Zakrevskiy K.E., Gazizov R.K. et al., The prospects of corporate geological modeling software creation (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2019, no. 11, pp. 50–54.

2. Journel A.G., Nonparametric estimation of spatial distributions, Mathematical Geology, 1983, V. 15, no. 3, pp. 445–468.

3. Pyrcz M.J., Deutsch C.V., Declustering and debiasing, 2007, URL: http://www.gaa.org.au/pdf/DeclusterDebias-CCG.pdf

4. Vestnik Roxar, 2015, no. 8 (22), pp. 3–11, URL: http://roxar.ru/wp-content/uploads/2017/04/%D0%92%D0%B5%D1%81%D1%82%D0%BD%D0%B8%D0%BA-Roxar-%D0%9E%D...

5. Manchuk J., Neufeld C., Deutsch C.V., Petrel plugins for declustering and debiasing, URL: https://www.ccgalberta.com/ccgresources/report09/2007-403_petrel_plugin_for_declustering.pdf

6. Baykov V.Ya., Bakirov N.K., Yakovlev A.A., Matematicheskaya geologiya (Mathematical geology), Part 1. Vvedenie v geostatistiku (Introduction to geostatistics), Moscow – Izhevsk: Publ. of Institute of Computer Science, 2012, 228 p.

The article is devoted to some aspects of optimizing the process of digital geological modeling of oil and gas fields. The algorithm for constructing fluid contact surfaces is proposed, which allows to automate the stage of geometrization of oil and gas deposits, speed up the modeling process, and reduce the number of errors at the same time. The algorithm contains the following computing units:

– analyzing and pre-processing the initial data, namely, geophysical “collector – non-collector” interpretation, “gas – oil – water” interpretation and zone markers;

– calculating the surface of fluid contacts by minimizing the deviation of known initial data relative to a certain surface;

– data declustering allowing to reduce the influence of tightly located data groups;

– minimizing discrepancies between the calculation result and the initial data, which allows “to hook” the obtained contact surface onto the markers.

Obtained results have been illustrated by examples of automated construction of contact surfaces on deposits of the West Siberian and Volga-Urals oil and gas provinces. The proposed algorithm can be used manually and automatically for updating and adaptation of fluid contact surfaces to changed input data at the stage of reservoir geometrization. The algorithm most effectively manifests itself in large oil and gas fields with a large number of wells. In addition to constructing fluid contact surfaces, the application of the algorithm allows the geologist-designer to analyze the well data for the presence of wells with significant inclinometry errors.

References

1. Saakyan M.I., Zakrevskiy K.E., Gazizov R.K. et al., The prospects of corporate geological modeling software creation (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2019, no. 11, pp. 50–54.

2. Journel A.G., Nonparametric estimation of spatial distributions, Mathematical Geology, 1983, V. 15, no. 3, pp. 445–468.

3. Pyrcz M.J., Deutsch C.V., Declustering and debiasing, 2007, URL: http://www.gaa.org.au/pdf/DeclusterDebias-CCG.pdf

4. Vestnik Roxar, 2015, no. 8 (22), pp. 3–11, URL: http://roxar.ru/wp-content/uploads/2017/04/%D0%92%D0%B5%D1%81%D1%82%D0%BD%D0%B8%D0%BA-Roxar-%D0%9E%D...

5. Manchuk J., Neufeld C., Deutsch C.V., Petrel plugins for declustering and debiasing, URL: https://www.ccgalberta.com/ccgresources/report09/2007-403_petrel_plugin_for_declustering.pdf

6. Baykov V.Ya., Bakirov N.K., Yakovlev A.A., Matematicheskaya geologiya (Mathematical geology), Part 1. Vvedenie v geostatistiku (Introduction to geostatistics), Moscow – Izhevsk: Publ. of Institute of Computer Science, 2012, 228 p.


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