Changes

| Sealing fault || • || • || || ||

| Sealing fault || • || • || || ||

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| Semi-sealing fault

| Semi-sealing fault || × || • || • || ||

| ×

| •

| •

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| Nonsealing fault

| Nonsealing fault || × || • || • || ||

| ×

| •

| •

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| Boundaries as genetic units

| Boundaries as genetic units || • || • || • || ||

| •

| •

| •

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| [[Permeability]] zonation

| [[Permeability]] zonation

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==

 

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

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

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

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 [[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]]).

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 [[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]]).