The prospects of corporate geological modeling software creation

UDK: 519.868:55
DOI: 10.24887/0028-2448-2019-11-50-54
Key words: geological modeling software, digital geological model, geostatistics, permanent geological-technological model
Authors: M.I. Saakyan (Rosneft Oil Company, RF, Moscow), K.E. Zakrevskiy (Rosneft Oil Company, RF, Moscow), R.K. Gazizov (RN-BashNIPIneft LLC, RF, Ufa), A.E. Lepilin (RN-BashNIPIneft LLC, RF, Ufa), E.A. Ryzhikov (RN-BashNIPIneft LLC, RF, Ufa)

Current state and main trends in development of corporate software for geological modeling of oil and gas fields are reviewed as well as main factors affecting the functional content and architectural features of a corporate software product in the near future are identified. Influence of modern information technologies on software developed, such as: non-relational databases, cloud storages and applications, multi-core computing, knowledge management systems, are included. Particular attention is given to the trends of great current interest in the oil and gas field modeling software development. It is noted the necessity in integration of professional oil and gas field modeling software into universal software packages covering the entire cycle of geological and technological design (from seismic processing to filtration calculations and economic risks assessment). Automation of the modeling process, including memorizing process sequence and stages parameters, creating “templates” for modeling, ensuring calculations repeatability is considered. Providing the possibility of multi-user modeling, both in the modes of phased data processing by various profiles specialists, and in the mode of parallel processing of one dataset by single-profile specialists is discussed. It is shown the advantages of transition from parallelized to distributed computing, allowing to remove restrictions on computing power, and transition to intelligent software support systems within the framework of corporate knowledge management systems. The emphasis is placed on the development, in accordance with the indicated priorities, of the corporate line of software products for modeling oil and gas fields of Rosneft Oil Company and, in particular, the RN-GEOSIM geological modeling software package.

References

1. Yakovlev V.V., Khasanov M.M., Prokofʹev D.O., Shushkov A.V., Technological development in Upstream Division of Gazprom Neft PJSC (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2016, no. 2, pp. 6–10.

2. Zakrevskiy K.E., Geologicheskoe 3D modelirovanie (3D geological modeling), Moscow: Publ of IPTs Maska, 2009, 376 p.

3. Veyber V., Kudinov A., Markov N., Model-driven platform for oil and gas enterprise data integration, International Journal of Computer Applications, 2012, V. 49, pp. 14–19.

4. Egorov D.V., Bukhanov N.V., Osmonalieva O.T. et al., Incorporation of experts'' experience into machine learning models using well logs analysis for Priobskoye and Muravlenkovskoye brownfields (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2017, no. 12, pp. 28–31.

5. Minikeeva L.R., Nadezhdin O.V., Nugumanov E.R. et al., Development of methods for automation of multi-well logging data interpretation and core analysis (in Russ.), Neftyanoe khozyaystvo = Oil Industry, 2018, no. 6, pp. 54–57.

6. Bilinchuk A.V., Gorev K.V., Koryabkin V.V. et al., Automating the process of petrophysical interpretation as an element of an efficient geosteering process (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2016, no. 12, pp. 36–39.

7. Kumar S., Wen X.H., He J. et al., Integrated static and dynamic uncertainties modeling big-loop workflow enhances performance prediction and optimization, SPE-182711-MS, 2017, DOI:10.2118/182711-ms

8. Moniruzzaman A.B.M., Hossain S., NoSQL database: New era of databases for big data analytics – Classification, characteristics and comparison, International Journal of Database Theory and Application, 2013, V. 6, no. 4.

9. Perrons R.K., Hems A., Cloud computing in the upstream oil & gas industry: A proposed way forward, Energy Policy, 2013, V. 56, pp. 732–737.

10. Jin H., Jespersen D.C., Mehrotra P. et al., High performance computing using MPI and OpenMP on multi-core parallel systems, Parallel Computing, 2011, V. 37, pp. 562–575.

11. URL: https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html

12. Halsey T., Computational sciences in the upstream oil and gas industry, Phil. Trans. R. Soc., 2016, v. 374, no. 2078, http://dx.doi.org/10.1098/rsta.2015.0429

13. Bakhtiy N.S., Aristov A.A., Khodanovich D.A. et al., Reservoir simulation of major oil fields in Surgutneftegas OJsC using high performance computing (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2017, no. 5, pp. 64–67.

14. Mikryukova A., iKnow: a single point of access to technical knowledge (In Russ.), Upravlenie proizvodstvom, 2014, URL: http://www.up-pro.ru/print/library/information_systems/management/iknow-bz.html.


15. Badpa A., Salim J., Yahaya J. et al., The effect of KMS usage on organizational performance in oil and gas industry: An empirical study in the context of developing economy, International Journal of Trend in Research and Development (IJTRD), 2018, V. 5, http://www.ijtrd.com/ViewFullText.aspx?Id=15819.

Current state and main trends in development of corporate software for geological modeling of oil and gas fields are reviewed as well as main factors affecting the functional content and architectural features of a corporate software product in the near future are identified. Influence of modern information technologies on software developed, such as: non-relational databases, cloud storages and applications, multi-core computing, knowledge management systems, are included. Particular attention is given to the trends of great current interest in the oil and gas field modeling software development. It is noted the necessity in integration of professional oil and gas field modeling software into universal software packages covering the entire cycle of geological and technological design (from seismic processing to filtration calculations and economic risks assessment). Automation of the modeling process, including memorizing process sequence and stages parameters, creating “templates” for modeling, ensuring calculations repeatability is considered. Providing the possibility of multi-user modeling, both in the modes of phased data processing by various profiles specialists, and in the mode of parallel processing of one dataset by single-profile specialists is discussed. It is shown the advantages of transition from parallelized to distributed computing, allowing to remove restrictions on computing power, and transition to intelligent software support systems within the framework of corporate knowledge management systems. The emphasis is placed on the development, in accordance with the indicated priorities, of the corporate line of software products for modeling oil and gas fields of Rosneft Oil Company and, in particular, the RN-GEOSIM geological modeling software package.

References

1. Yakovlev V.V., Khasanov M.M., Prokofʹev D.O., Shushkov A.V., Technological development in Upstream Division of Gazprom Neft PJSC (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2016, no. 2, pp. 6–10.

2. Zakrevskiy K.E., Geologicheskoe 3D modelirovanie (3D geological modeling), Moscow: Publ of IPTs Maska, 2009, 376 p.

3. Veyber V., Kudinov A., Markov N., Model-driven platform for oil and gas enterprise data integration, International Journal of Computer Applications, 2012, V. 49, pp. 14–19.

4. Egorov D.V., Bukhanov N.V., Osmonalieva O.T. et al., Incorporation of experts'' experience into machine learning models using well logs analysis for Priobskoye and Muravlenkovskoye brownfields (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2017, no. 12, pp. 28–31.

5. Minikeeva L.R., Nadezhdin O.V., Nugumanov E.R. et al., Development of methods for automation of multi-well logging data interpretation and core analysis (in Russ.), Neftyanoe khozyaystvo = Oil Industry, 2018, no. 6, pp. 54–57.

6. Bilinchuk A.V., Gorev K.V., Koryabkin V.V. et al., Automating the process of petrophysical interpretation as an element of an efficient geosteering process (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2016, no. 12, pp. 36–39.

7. Kumar S., Wen X.H., He J. et al., Integrated static and dynamic uncertainties modeling big-loop workflow enhances performance prediction and optimization, SPE-182711-MS, 2017, DOI:10.2118/182711-ms

8. Moniruzzaman A.B.M., Hossain S., NoSQL database: New era of databases for big data analytics – Classification, characteristics and comparison, International Journal of Database Theory and Application, 2013, V. 6, no. 4.

9. Perrons R.K., Hems A., Cloud computing in the upstream oil & gas industry: A proposed way forward, Energy Policy, 2013, V. 56, pp. 732–737.

10. Jin H., Jespersen D.C., Mehrotra P. et al., High performance computing using MPI and OpenMP on multi-core parallel systems, Parallel Computing, 2011, V. 37, pp. 562–575.

11. URL: https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html

12. Halsey T., Computational sciences in the upstream oil and gas industry, Phil. Trans. R. Soc., 2016, v. 374, no. 2078, http://dx.doi.org/10.1098/rsta.2015.0429

13. Bakhtiy N.S., Aristov A.A., Khodanovich D.A. et al., Reservoir simulation of major oil fields in Surgutneftegas OJsC using high performance computing (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2017, no. 5, pp. 64–67.

14. Mikryukova A., iKnow: a single point of access to technical knowledge (In Russ.), Upravlenie proizvodstvom, 2014, URL: http://www.up-pro.ru/print/library/information_systems/management/iknow-bz.html.


15. Badpa A., Salim J., Yahaya J. et al., The effect of KMS usage on organizational performance in oil and gas industry: An empirical study in the context of developing economy, International Journal of Trend in Research and Development (IJTRD), 2018, V. 5, http://www.ijtrd.com/ViewFullText.aspx?Id=15819.


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