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of the reservoir rock and the production-generated changes in the incompressibility of the pore fluids (∆K_fluids).

      The reservoir rocks must therefore be sufficiently compressible so that there is a prominent and measurable contribution from the pore fluids. Soft compressible rocks like unconsolidated sands (younger in geologic times) are ideal for time lapse seismic while rigid or incompressible reservoir rocks such as carbonates do not lend themselves for effective application of this technology.

      The acquisition and processing parameters for different vintage 3D seismic should either be the same or necessary calibration should be applied to make them consistent. That is the seismic response should be identical when no changes in the geologic formation due to injection of production has taken place. Some of these difficulties may be mitigated by using permanent sensors in wells and recording time lapse data.

      1.5.2 Microseismic Data for DRC

      The permanent sensors also can record micro seismic or microearthquake (MEQ) data in passive mode without any seismic source. They detect the seismic events that are induced by hydrocarbon production due to change in the reservoir stress with pressure changes. Many examples of MEQ data applications in DRC and/or monitoring hydraulic fracturing or other well stimulation processes have been reported.

The results of seismic monitoring (4D or MEQ or combination of the two) along with the well log and production data can be used to constrain the flow simulation model in the reservoir history matching and fluid flow prediction process, as well as characterizing the fracture regime. As an example, see Maity and Aminzadeh [8]. Figure 1.7 shows how integration of conventional seismic, well log data and MEQ data have been able to create a 3-D fracture distribution, using a neural network approach through integrating “Fracture Zone Identifier” or FZI attributes.

Figure 1.7 Use of conventional seismic, well log data and MEQ data to create a 3-D fracture distribution volume, using a neural network by integrating “Fracture Zone Identifier” or FZI attributes, Maity and Aminzadeh [9].

1.6 More on Reservoir Characterization and Reservoir Modeling for Reservoir Simulation

      No discussion on reservoir characterization is complete without understanding rock properties and the corresponding rock physics. Furthermore, reservoir modeling could be considered as the last step for reservoir characterization during different stages of the life of the reservoir. Indeed, ideally, any reservoir simulation and could use reservoir models based on static and dynamic reservoir characterization to improve the process. 4D seismic data can help with the reservoir model updating process, thus enabling creation of a dynamic reservoir model.

Figure 1.8 shows how a static reservoir model with the associated structural earth model and corresponding geologic and reservoir properties such as facies, porosities, and vshale among other parameters can be a starting point (top left). Through reservoir simulation a flow model can be created at different time points, with the respective information about pressure, saturation and temperature. Output of the reservoir simulation model and the earth model could be used to generate (invert for) the reservoir rock physics properties such as density, compressional and shear wave velocities at different time periods. Such information can then be used to generate the synthetic 4D seismic data. Comparison of the synthetic and field 4D seismic data will then lead to an updated Earth model. The process continuous until we establish a good match between real and simulated seismic and reservoir flow models leading to an acceptable dynamic reservoir characterization results from the accurately derived rock physics properties as well as other reservoir properties such as porosity, permeability, oil and gas saturation, and pressure among other properties.

Figure 1.8 The entire process of reservoir model updating through 4D seismic modeling and reservoir simulation, (from Meadows, [11]).

      In what follows we describe rock physics and reservoir modeling briefly.

      1.6.1 Rock Physics

      Rock physics investigates reservoir rocks properties that affect transmission of seismic waves through the rocks. These physical properties are rigidity, compressibility, and porosity. This provides a connection between elastic properties measured at the surface of the earth, within the borehole environment or in the laboratory with the intrinsic properties of rocks, such as mineralogy, porosity, pore shapes, pore fluids, pore pressures, permeability, viscosity, stresses and overall architecture such as laminations and fractures.

Description of rock and fluid properties between the well control points requires understanding of the linkage of bulk and seismic properties to each other and their changes with geologic age, burial depth, and location. This connection allows us to understand and model the petrophysical and geometrical properties which give rise to the seismic signal. Rock physics requires a knowledge and understanding of geophysics, petrophysics, geomechanics and the causes of distribution of fluids in the subsurface reservoir between wells. From seismic fluid monitoring we can obtain valuable information about reservoir fluid movements and geologic reservoir heterogeneities. The results can also resolve seal integrity issues and guide the optimum placement of wells in complex reservoirs.

      Rock physics uses sonic, density and dipole sonic logs to establish a relationship between the geophysical data and the petrophysical properties. In ‘80s and ‘90s many oil companies had their own rock physics laboratories. Because of the longer-range objectives and the need to assemble large databases, today such laboratories are found primarily within five or six universities and a few service companies. The focus of rock physics analysis started with estimating porosity and permeability of sandstones and carbonates. Today, much of the research is focused on unconventional reservoirs and on estimating rock strength or “fracability” and the presence of total organic carbon. For some detailed discussion on the value of rock physics analysis in various aspects of reservoir characterization and reservoir property estimation see Dvorkin and Nur [5] and Castagna et al. [4].

      Integration of 3D seismic interpretation with well measurements provides a powerful tool for characterization a reservoir for the 3D distribution of rock properties and the geometric framework of the reservoir. While the cores, wireline logs and outcrops provide the vertical resolution it is only geophysical data like 3D seismic data that can provide detailed spatial information between the wells for the geological model. Since 3D seismic is a measurement made at the surface of the earth, the subsurface interpretation using seismic data can be done only after proper calibration with available well information. Seismic reflection data provide the gross acoustic properties within a volume of rock and do not have the vertical resolution of wireline logs.

      1.6.2 Reservoir Modeling

Quantification of rock properties and the fluids in three dimensions is the process of reservoir modeling. The goal of reservoir modeling and fluid simulation is increased hydrocarbon fluid production with an increased rate of return. The 3D quantification is performed in a geo-cellular model that consists or reservoir geometry, lithology, porosity, permeability and initial fluid saturation. Integration of information from seismic data, cores, wireline logs and outcrops provide the quantification of the static reservoir model of the reservoir. A geological reservoir characterization

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