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From digital to mathematical models: a new look at geological and hydrodynamic modeling of oil and gas fields by means of artificial intelligence

UDK: 004.032.26:622.276
DOI: 10.24887/0028-2448-2019-12-144-148
Key words: oil and gas fields, geological and reservoir modeling, artificial intelligence, machine learning, fuzzy-logical matrices, computer forecasting
Authors: A.Z. Zakharian (Cervart Ltd., RF, Moscow), S.O. Ursegov (Skolkovo Institute of Science and Technology, RF, Moscow)

The article shows that the traditional version of geological and hydrodynamic models of oil and gas fields based on a computer approach is not the only possible one and it prevents the development of modeling as a whole, since it is not truly mathematical. Considering that computers do not work with images, but with numbers, a new methodology for construction of geological and hydrodynamic models of oil and gas fields is presented, which have an unusual appearance and are not intended for visual analysis, but they are more effective for computer forecasting. New mathematical geological and hydrodynamic models are cascades of fuzzy-logical matrices. The matrices of the geological model are formed from spatial coordinates and geological parameters; the time coordinate is additionally included in the matrices of the hydrodynamic model. The number of fuzzy-logical matrices can reach several thousands. Using the obtained matrices, one can construct membership functions and predict the values of evaluated parameters, for example, the efficiency of new drilling, the distribution of remaining reserves, and the levels of hydrocarbon production.The proposed approach for geological and hydrodynamic modeling from a set of matrix cascades may seem complex. However, the calculation of these cascades is carried out completely automatically, and no one should control it. The matrix cascades are mathematical functions, not illustrations of the geological structure of the studied objects and they are directly used for forecasting calculations. The matrix cascades are a new form of machine learning. To do this, it is advisable to use the big amount of data. The new machine learning method based on the matrix cascades opens up new possibilities for the application of artificial intelligence in the geological and hydrodynamic modeling of oil and gas fields.

References

1. Khalimov E.M., Detailed geological models and three-dimensional simulation (In Russ.), Neftegazovaya geologiya. Teoriya i praktika, 2012, V. 7, no. 3, рр. 1–10, URL: http://www.ngtp.ru/rub/11/41_2012.pdf.

2. Mohaghegh S.D., Modavi A., Hafez H. et al., Development of a Surrogate Reservoir Model (SRM) for fast track analysis of a complex reservoir, International Journal of Oil, Gas, and Coal Technology, 2009, no. 1 (2), pp. 2–23.

3. Taraskin E.N., Gutman I.S., Rudnev S.A. et al., New adaptive approach to geological and hydrodynamic modeling of fields and reservoirs with long production history (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2017, no. 6, pp. 78–83.

4. Mikhaylovskiy A.A., Application of simplified gas-hydrodynamic proxy models for real-time technological calculations aimed at gas fields and underground gas storages (In Russ.), Vesti gazovoy nauki: Aktual'nye problemy dobychi gaza, 2018, no. 1(33), pp. 193–202.

5. Hudson J., Technology focus: Intelligent fields technology, Journal of Petroleum Technology, 2016, V. 69, no. 5, https://doi.org/10.2118/0517-0060-JPT.

6. Khasanov M.M., Prokof'ev D.O., Ushmaev O.S. et al., Promising Big Data technologies in petroleum engineering: the experience of the Gazprom Neft PJSC (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2016, no. 12, pp. 76–79.

7. Dogru A.H., Fung L.S.K., Al-Shaalan T.M. et al., From mega cell to giga cell reservoir simulation, SPE-116675-MS, 2008.

8. Chetverushkin B.N., Dorodnitsyn L.V., Kinetically consistent schemes in gas dynamics (In Russ.), Matematicheskoe modelirovanie, 1999, no. 5, pp. 84–100.

9. Lantz B., Machine Learning with R, Packt Publishing, 2015, 452 p.

10. Bezrukov A.V., Mukharlyamov A.R., Baykov V.A., Savichev V.I., Multivariant modelling support system: uncertainty space analysis (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2007, no. 11, pp. 14 –16.

The article shows that the traditional version of geological and hydrodynamic models of oil and gas fields based on a computer approach is not the only possible one and it prevents the development of modeling as a whole, since it is not truly mathematical. Considering that computers do not work with images, but with numbers, a new methodology for construction of geological and hydrodynamic models of oil and gas fields is presented, which have an unusual appearance and are not intended for visual analysis, but they are more effective for computer forecasting. New mathematical geological and hydrodynamic models are cascades of fuzzy-logical matrices. The matrices of the geological model are formed from spatial coordinates and geological parameters; the time coordinate is additionally included in the matrices of the hydrodynamic model. The number of fuzzy-logical matrices can reach several thousands. Using the obtained matrices, one can construct membership functions and predict the values of evaluated parameters, for example, the efficiency of new drilling, the distribution of remaining reserves, and the levels of hydrocarbon production.The proposed approach for geological and hydrodynamic modeling from a set of matrix cascades may seem complex. However, the calculation of these cascades is carried out completely automatically, and no one should control it. The matrix cascades are mathematical functions, not illustrations of the geological structure of the studied objects and they are directly used for forecasting calculations. The matrix cascades are a new form of machine learning. To do this, it is advisable to use the big amount of data. The new machine learning method based on the matrix cascades opens up new possibilities for the application of artificial intelligence in the geological and hydrodynamic modeling of oil and gas fields.

References

1. Khalimov E.M., Detailed geological models and three-dimensional simulation (In Russ.), Neftegazovaya geologiya. Teoriya i praktika, 2012, V. 7, no. 3, рр. 1–10, URL: http://www.ngtp.ru/rub/11/41_2012.pdf.

2. Mohaghegh S.D., Modavi A., Hafez H. et al., Development of a Surrogate Reservoir Model (SRM) for fast track analysis of a complex reservoir, International Journal of Oil, Gas, and Coal Technology, 2009, no. 1 (2), pp. 2–23.

3. Taraskin E.N., Gutman I.S., Rudnev S.A. et al., New adaptive approach to geological and hydrodynamic modeling of fields and reservoirs with long production history (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2017, no. 6, pp. 78–83.

4. Mikhaylovskiy A.A., Application of simplified gas-hydrodynamic proxy models for real-time technological calculations aimed at gas fields and underground gas storages (In Russ.), Vesti gazovoy nauki: Aktual'nye problemy dobychi gaza, 2018, no. 1(33), pp. 193–202.

5. Hudson J., Technology focus: Intelligent fields technology, Journal of Petroleum Technology, 2016, V. 69, no. 5, https://doi.org/10.2118/0517-0060-JPT.

6. Khasanov M.M., Prokof'ev D.O., Ushmaev O.S. et al., Promising Big Data technologies in petroleum engineering: the experience of the Gazprom Neft PJSC (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2016, no. 12, pp. 76–79.

7. Dogru A.H., Fung L.S.K., Al-Shaalan T.M. et al., From mega cell to giga cell reservoir simulation, SPE-116675-MS, 2008.

8. Chetverushkin B.N., Dorodnitsyn L.V., Kinetically consistent schemes in gas dynamics (In Russ.), Matematicheskoe modelirovanie, 1999, no. 5, pp. 84–100.

9. Lantz B., Machine Learning with R, Packt Publishing, 2015, 452 p.

10. Bezrukov A.V., Mukharlyamov A.R., Baykov V.A., Savichev V.I., Multivariant modelling support system: uncertainty space analysis (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2007, no. 11, pp. 14 –16.


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