Reservoir Characterization. Группа авторов

Чтение книги онлайн.

Figure 1.10 Integrated reservoir modeling, fluid simulation update and reiteration by incorporating geophysical monitoring data. http://www.co2care.org/Sections.aspx?section=538.5.

In Part 7 of this volume, we will discuss Artificial Intelligence (AI) and Data Analytic (DA) can help address some of the remaining complexities associated with reservoir characterization results. For example, Nikravesh and Aminzadeh [12] reported on the past, present and future of AI in reservoir characterization. Twenty years later Aminzadeh [3], discussed how human and machine intelligence can be combined to improve characterization results. It is firmly believed that AI- and DA offer hope solve the issues related to the SURE Challenge discussed earlier.

1.7 Conclusion

      Reservoir Characterization Is an important step in the entire life cycle of the reservoir. Reservoir Characterization is aimed at assessing reservoir properties and its condition, using the available data from different sources such as core samples, log data, seismic surveys (3D and 4D) and production data. This is done in different stages of the E&P process from high grading reservoirs in exploration to their delineation, for their development, as well as their description for optimum production to assessing their evolution in their stimulation for enhance oil/gas recovery to extend their economic life. An integrated approach for reservoir characterization bridges the traditional disciplinary divides, leading to better handling of uncertainties and improvement of the reservoir model for field development. Among the main difficulties in reservoir characterization is what I call “SURE” Challenge. The display here demonstrates the complications involved in integrating different data types with different Scale, Uncertainty, Resolution and Environment.

References

      1. Aminzadeh, F., 2005, Meta-Attributes: A new concept detecting geologic features and predicting reservoir properties, Second International Congress on Geosciences Merida, Mexico September 2005

      2. Aminzadeh, F. and Dasgupta, S., 2013 Geophysics for Petroleum Engineers, Elsevier.

3. Aminzadeh, F., 2021, Reservoir Characterization: Combining Machine Intelligence with Human Intelligence, E&P Plus, April 2021, Vol. 96 Issue 4, E&P Plus, Hart Energy.

      4. Castagna, J., Han, D., Batzle, M.L., 1995, Issues in rock physics and implications for DHI interpretation, The Leading Edge, August 1995.

      5. Dvorkin, J., & Nur,A., 1993, Dynamic poroelasticity: A unified model with the squirt and the Biot mechanisms, Geophysics 58, 524-533.

      6. Fornel, A. and Estublier, A. 2013. To A Dynamic Update of The Sleipner CO2 Storage Geological Model Using 4D Seismic Data. Energy Procedia. 37. 4902-4909. 10.1016/j.egypro.2013.06.401.

      7. Kosco, K. & Schiøtt, C.R. & Vejbaek, Ole & Herwanger, Jorg & Wold, Rune & Koutsabeloulis, N., 2010, Integrating time-lapse seismic, Reservoir Simulation and Geomechanics. 231. 61-66.

      8. Ma, Y. Z., Phillips, D. Gomez, E., 2020 Synergistic Integration of Seismic and Geologic Data for Modeling Petrophysical Properties, The Leading Edge, March 2020.

      9. Maity, D., Aminzadeh, F., 2015. Novel Fracture Zone Identifier Attribute Using Geophysical and Well Log Data for Unconventional Reservoirs, Interpretation Journal, Vol.3, No. 3, P.T155-T167.

      10. Maleki, M., 2018, Integration of 3D and 4D seismic impedance into the simulation model to improve reservoir characterization. PhD Dissertation, University of Compinas.

      11. Meadows, M., 2012, Time-lapse seismic data for reservoir monitoring and characterization Course notes on Advanced Oil Field Operations with Remote Visualization, Guest Lecturer for F. Aminzadeh’s course, USC PTE 587.

      12. Nikravesh, N. and Aminzadeh, F., 2001, “Past, present and future intelligent reservoir characterization trends,” Journal of Petroleum Science and Engineering, vol. 31, no. 2, pp. 67–79, 2001.