Reservoir Characterization. Группа авторов

performed using a cellular facies model. Rock properties are assigned to model cells according to the defined facies. The geological models describe the flow layers that account for fluid and displacement phenomenon in the reservoir. It models the inter-well connectivity and continuity of flow units in the rock facies present within the reservoir architecture.

      Reservoir fluid simulation is the quantification of fluid flow over time in the 3D reservoir model. The numerical model simulation and forecasts of reservoir performance is based on the geo-cellular static model. Reservoir simulation is performed to infer fluid flow behavior from a mathematical model. The forecast of reservoir performance is improved with increased accuracy in the geological model. Major decisions regarding the development and production plans for the reservoirs e.g., location and spacing of production and injector wells, depletion strategy, maximum production rates are based on the reservoir simulation. As hydrocarbons remaining in place become more difficult to recover, fluid movement in the reservoir needs to be more closely monitored. The location of remaining hydrocarbons must be known to plan injection schemes. Also, the manner in which injected fluids move and make contact with the target oil must be known in order to evaluate and, if necessary, correct the recovery project.

Static reservoir model provides a representation of the structure, thickness, lithology, porosity, initial fluids in the reservoir. As discussed in Section 1.5 on DRC, a dynamic reservoir model is a representation of the changes in fluid flow in the reservoir model that needs to be validated with reservoir performance data-pressure changes, production and injection rates. Rock properties defined in the reservoir rocks from 3D seismic interpretation include: Lithology, Porosity, Net pay thickness (or porosity volume), Fluid type and the respective fluid saturation, as well as the reservoir pressure. The heterogeneity within a petroleum reservoir has a profound influence on its production performance. Structural deformations, fractures, lithological variations, and diagenetic alternations all contribute to the creation or destruction of conduits and barriers to fluid flow through the reservoir matrix. Rock physics is a key component of analyzing the reservoir properties. It is important to monitor changes in the fluid flow or its composition during the producing life of the field. Figure 1.9 illustrates different components of reservoir modeling.

These integrated reservoir models are critical for forecasting, monitoring, and optimizing reservoir performance over the life cycle of the reservoir, from exploration, development, primary production and secondary/tertiary production. They will enable reservoir engineers to more accurately perform flow simulation studies, identify permeability flow-paths and barriers, map bypassed oil, and monitor pressure and saturation fronts in the reservoir. All of these are essential for effective reservoir management. Figure 1.10 shows how the original (static) geological or reservoir model based on the integration of geophysical data could be used to for reservoir simulation which in turn it can be used for reservoir monitoring and reservoir model updating.

Figure 1.9 Reservoir modeling process workflow. The process takes control of the data within its modeling framework and integrates the various types of data attributes. Courtesy: Roxar-Emerson.

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.

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