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Fluid flow in a reservoir is controlled by bed continuity, the presence of baffles to flow, and the [[permeability]] distribution (see “Fundamentals of Fluid Row”). Reservoir heterogeneities influencing fluid flow range from large scale faults and discontinuities down to thin shale intercalations, sedimentary structures, and even pore scale features (Figure 1).

Fluid flow in a reservoir is controlled by bed continuity, the presence of baffles to flow, and the [[permeability]] distribution (see “Fundamentals of Fluid Row”). Reservoir heterogeneities influencing fluid flow range from large scale faults and discontinuities down to thin shale intercalations, sedimentary structures, and even pore scale features ([[:file:reservoir-modeling-for-simulation-purposes_fig1.png|Figure 1]]).

[[file:reservoir-modeling-for-simulation-purposes_fig1.png|thumb|{{figure number|1}}Classification of reservoir heterogeneity types.]]

[[file:reservoir-modeling-for-simulation-purposes_fig1.png|thumb|{{figure number|1}}Classification of reservoir heterogeneity types.]]

The effect of large scale features such as faults can be estimated on the basis of geological experience and modeling<ref name=pt10r33>Weber, K. J., Mandl, G., Pilaar, W. F., Lehner, F., Precious, R. G., 1978, The role of faults in hydrocarbon migration and trapping in Nigerian growth fault structures: Offshore Technical Conference, Houston, OTC 3356.</ref> or it must be evaluated by pressure measurements or fluid level differences.

The effect of large scale features such as faults can be estimated on the basis of geological experience and modeling<ref name=pt10r33>Weber, K. J., Mandl, G., Pilaar, W. F., Lehner, F., Precious, R. G., 1978, The role of faults in hydrocarbon migration and trapping in Nigerian growth fault structures: Offshore Technical Conference, Houston, OTC 3356.</ref> or it must be evaluated by pressure measurements or fluid level differences.

==Rock typing and permeability estimation==

==Rock typing and permeability estimation==

The first priority in describing the reservoir rock is the determination of the environment of deposition and the range of lithofacies that occur within the reservoir (see “Lithofacies and Environmental Analysis of Clastic Depositional Systems” and “Carbonate Reservoir Models: Facies, Diagenesis, and Flow Characterization”). Regional stratigraphic information, cores, and sidewall samples are used for this purpose. Of particular interest is the rock typing through a study of porosity, permeability, petrography, and capillary properties (Figure 2) (see Part 5 on Laboratory Methods). For simulation purposes, permeability is a major parameter, and estimating the permeability profile in noncored wells is of prime importance<ref name=pt10r38>Wolf, M., Pelissier-Combescure, J., 1982, Faciolog-automatic electrofacies determination: SPWLA Third Annual Logging Symposium Transactions, July.</ref>. The basis for these techniques is multivariate analysis of the combined logging data. Discriminant analysis of log response using a core calibrated system usually leads to the best results. In general, one has to combine several rock types into an electrofacies class mainly because of the poor vertical resolution of the nuclear logs if run in standard fashion. If the porosity and permeability relationships of combined rock classes differ little, this is an acceptable simplification (Figure 3).

[[file:reservoir-modeling-for-simulation-purposes_fig3.png|left|thumb|{{figure number|3}}Log-facies calibration and determination of facies-related rock characteristics.]]

[[file:reservoir-modeling-for-simulation-purposes_fig2.png|thumb|{{figure number|2}}Analysis of core data for facies identification and rock quality assessment.]]

The first priority in describing the reservoir rock is the determination of the environment of deposition and the range of lithofacies that occur within the reservoir (see “Lithofacies and Environmental Analysis of Clastic Depositional Systems” and “Carbonate Reservoir Models: Facies, Diagenesis, and Flow Characterization”). Regional stratigraphic information, cores, and sidewall samples are used for this purpose. Of particular interest is the rock typing through a study of porosity, permeability, petrography, and capillary properties ([[:file:reservoir-modeling-for-simulation-purposes_fig2.png|Figure 2]]) (see Part 5 on Laboratory Methods). For simulation purposes, permeability is a major parameter, and estimating the permeability profile in noncored wells is of prime importance<ref name=pt10r38>Wolf, M., Pelissier-Combescure, J., 1982, Faciolog-automatic electrofacies determination: SPWLA Third Annual Logging Symposium Transactions, July.</ref>. The basis for these techniques is multivariate analysis of the combined logging data. Discriminant analysis of log response using a core calibrated system usually leads to the best results. In general, one has to combine several rock types into an electrofacies class mainly because of the poor vertical resolution of the nuclear logs if run in standard fashion. If the porosity and permeability relationships of combined rock classes differ little, this is an acceptable simplification ([[:file:reservoir-modeling-for-simulation-purposes_fig3.png|Figure 3]]).

[[file:reservoir-modeling-for-simulation-purposes_fig3.png|thumb|{{figure number|3}}Log-facies calibration and determination of facies-related rock characteristics.]]

[[file:reservoir-modeling-for-simulation-purposes_fig4.png|thumb|{{figure number|4}}Correlation of reservoir units and subdivision of reservoir In flow units.]]

==Well correlation==

==Well correlation==

The next step in reservoir modeling is correlating the reservoir intervals from well to well (Figure 4). This procedure is strongly dependent on facies and well spacing. A requirement is a sound database of genetic unit geometry, for example, width to thickness ratio of a specific sand body type. When no deterministic correlation can be made of reservoir units, it may be necessary to use probabilistic modeling techniques, but in such cases only prototype simulations are usually carried out<ref name=pt10r13>Haldorsen, H. H., Brand, P. J., Macdonald, C. J., 1987, Review of the stochastic nature of reservoirs: Proceedings of the Seminar on Mathematics of Oil Production, Cambridge, U., K.</ref>.

The next step in reservoir modeling is correlating the reservoir intervals from well to well ([[:file:reservoir-modeling-for-simulation-purposes_fig4.png|Figure 4]]). This procedure is strongly dependent on facies and well spacing. A requirement is a sound database of genetic unit geometry, for example, width to thickness ratio of a specific sand body type. When no deterministic correlation can be made of reservoir units, it may be necessary to use probabilistic modeling techniques, but in such cases only prototype simulations are usually carried out<ref name=pt10r13>Haldorsen, H. H., Brand, P. J., Macdonald, C. J., 1987, Review of the stochastic nature of reservoirs: Proceedings of the Seminar on Mathematics of Oil Production, Cambridge, U., K.</ref>.

 

The framework for constructing simulation models is controlled by facies distribution, major permeability contrasts, and impermeable layers<ref name=pt10r35>Weber, K. J., Van Geuns, L. C., 1990, Framework for constructing clastic reservoir simulation models: Journal of Petroleum Technology, Oct., p. 1248–1297.</ref>. Again, maximum use should be made of reservoir engineering data with emphasis on wireline formation pressures. Geological predictions of sealing surfaces are often unreliable because of the presence of small scale faults or local erosion.

The framework for constructing simulation models is controlled by facies distribution, major permeability contrasts, and impermeable layers<ref name=pt10r35>Weber, K. J., Van Geuns, L. C., 1990, Framework for constructing clastic reservoir simulation models: Journal of Petroleum Technology, Oct., p. 1248–1297.</ref>. Again, maximum use should be made of reservoir engineering data with emphasis on wireline formation pressures. Geological predictions of sealing surfaces are often unreliable because of the presence of small scale faults or local erosion.

Practical grid block sizes usually imply the amalgamation of a few geological features. Thus, an averaging procedure is required to obtain realistic overall flow characteristics for the blocks as a whole. The influence of discontinuous shale breaks on vertical permeability, for example, can be estimated using a statistic approach<ref name=pt10r1>Begg, S. H., Chang, D. M., Haldorsen, H. H., 1985, A simple statistical method for calculating the effective vertical permeability of a reservoir containing discontinuous shales: Society of Petroleum Engineers Symposium on Reservoir Simulation, Dallas, TX, Feb. 10–13, SPE 14271.</ref>. Averaging horizontal and vertical permeability over grid block size units is a difficult task. In practice, the effective horizontal permeability usually ranges from the arithmetic to the geometric average of the permeability profile of the block. The more continuous the sublayers of the flow unit, the closer the average Ues to the arithmetic average. The more random the permeability, the closer it gets to the geometric average. Geostatistical methods have become popular to tackle these problems<ref name=pt10r17>Journel, A. G., Alabert, F. G., 1990, New method for reservoir mapping: Journal of Petroleum Technology, Feb., p. 212–218.</ref>. Vertical permeabilities are difficult to measure, and the values used are often based either on experience for a given facies or on vertical pulse tests or other pressure data evaluation.

Practical grid block sizes usually imply the amalgamation of a few geological features. Thus, an averaging procedure is required to obtain realistic overall flow characteristics for the blocks as a whole. The influence of discontinuous shale breaks on vertical permeability, for example, can be estimated using a statistic approach<ref name=pt10r1>Begg, S. H., Chang, D. M., Haldorsen, H. H., 1985, A simple statistical method for calculating the effective vertical permeability of a reservoir containing discontinuous shales: Society of Petroleum Engineers Symposium on Reservoir Simulation, Dallas, TX, Feb. 10–13, SPE 14271.</ref>. Averaging horizontal and vertical permeability over grid block size units is a difficult task. In practice, the effective horizontal permeability usually ranges from the arithmetic to the geometric average of the permeability profile of the block. The more continuous the sublayers of the flow unit, the closer the average Ues to the arithmetic average. The more random the permeability, the closer it gets to the geometric average. Geostatistical methods have become popular to tackle these problems<ref name=pt10r17>Journel, A. G., Alabert, F. G., 1990, New method for reservoir mapping: Journal of Petroleum Technology, Feb., p. 212–218.</ref>. Vertical permeabilities are difficult to measure, and the values used are often based either on experience for a given facies or on vertical pulse tests or other pressure data evaluation.

==Mapping of reservoir properties==

==Mapping of reservoir properties==

The geological input to three-dimensional reservoir simulation must consist of structural maps and property maps. Typically, maps must be prepared for every simulation model layer, specifying the distribution of net/gross, isochores, porosity, horizontal and vertical permeability, [[capillary pressure]] curve characteristics, and water saturation (Figure 5). Also, the geologist and the reservoir engineer have to cooperate to define pseudo-[[relative permeability]] curves for different internal grid block heterogeneity types. The matching phase of the simulation study requires similar cooperation to arrive at a final model with realistic properties.

The geological input to three-dimensional reservoir simulation must consist of structural maps and property maps. Typically, maps must be prepared for every simulation model layer, specifying the distribution of net/gross, isochores, porosity, horizontal and vertical permeability, [[capillary pressure]] curve characteristics, and water saturation ([[:file:reservoir-modeling-for-simulation-purposes_fig5.png|Figure 5]]). Also, the geologist and the reservoir engineer have to cooperate to define pseudo-[[relative permeability]] curves for different internal grid block heterogeneity types. The matching phase of the simulation study requires similar cooperation to arrive at a final model with realistic properties.

 

==Modeling carbonate reservoirs==

==Modeling carbonate reservoirs==