Integrated Study of a Faulted and Fractured Reservoir
Abstract: This paper presents a case study drawn from an integrated reservoir study on a faulted and fractured carbonate reservoir offshore Abu Dhabi.Particular emphasis is put on demonstrating how the results of 3D seismic interpretation impacted the structural understanding. The paper also explains how the seismic interpretation, well-bore observations and production data were integrated to provide an improved reservoir model'.
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Minimizing uncertainty associated with predicted log water saturation can be best achieved through the integration of log analysis (formation evaluation) and capillarity (core measured high pressure mercury and oil/water drainage capillary pressure data). Further reduction of uncertainty in predicted formation water saturation requires; first the most probable log porosity solution interpretation; second the most probable log porosity/permeability relationship (match core measured values); and third a quantitative capillary pressure model that represents measured high pressure mercury and oil/water drainage capillary pressure data. In this paper, the classical Leverett-J method (Leverett 1941) was used in the integration of capillary pressure data with routine core analysis and petrophysical rock types to generate a robust model for predicting formation water saturation profiles. The classical interpolation method that relates capillary pressure (derived from Leverett J function) to water saturation has been used predict the wetting phase saturation. The assignment of capillary pressure curves to their corresponding rock types was accomplished by grouping rock types according to discrete ranges of the interval speed which is defined as the square root of core measured permeability divided by porosity () data. Each range of the interval speed will have a corresponding Leverett J function from which water saturations will be interpolated according to its respective height above the Free Water Level (FWL) (using the log predicted permeability-porosity ratios). In the absence of clear fluid contact, a trial and error method is used to adjust the FWL until a good agreement between the log predicted water saturation and the predicted water saturation from the capillary pressure correlation is reached. The error in averaging was minimized by increasing the number of rock types (minimizing the ranges of interval speed) using a large number of measured high pressure mercury and oil-water drainage capillary pressure data. Furthermore, this paper presents a comprehensive comparison of the log derived and model predicted water saturations. Introduction Reservoir characterization is a central element in field development planning. It is with no doubt that the amount of oil left behind will be reduced with improvements in reservoir description. This can be achieved with better understanding of pore throat structure and distribution in reservoir rocks. Many reservoir description programs, though detailed, have not included well-defined descriptions at the pore-throat scale. Yet, pore-throat attributes control initial/residual hydrocarbon distribution and fluid flow. Efficient field development requires a full knowledge of reservoir rock and fluid properties and their interactions. Rock petrophysical study in combination with Geology and High Pressure Mercury Injection data (HPMI) are the most viable tools to assess reservoir quality, to characterize its lithology and rock types, and ultimately to determine flow units, which are of utmost importance in reservoir modeling. The proper reservoir characterization in combination with a flow simulator which incorporates the dynamical data, such as relative permeability, can then be used to properly assess and manage the reservoir. We recognize many talented investigators have worked in this area during the past 60 years (leverett 1941, O'Meara 1985, Coalson 1985, Flolo 1998, Gunter, Smart, Miller and Finneran 1999, and Shahin and Gunter 2000). The emphasis we have in this paper is on the discrimination between rock types and their relationship to geology and dynamics. Moreover, with acceptable level of accuracy, one can still use the simple water saturation prediction model (leverett J) to initialize the simulation model.
Minimizing uncertainty associated with predicted log water saturation can be best achieved through the integration of log analysis (formation evaluation) and capillarity (core measured high pressure mercury and oil/water drainage capillary pressure data). Further reduction of uncertainty in predicted formation water saturation requires; first the most probable log porosity solution interpretation; second the most probable log porosity/permeability relationship (match core measured values); and third a quantitative capillary pressure model that represents measured high pressure mercury and oil/water drainage capillary pressure data. In this paper, the classical Leverett-J method (Leverett 1941) was used in the integration of capillary pressure data with routine core analysis and petrophysical rock types to generate a robust model for predicting formation water saturation profiles. The classical interpolation method that relates capillary pressure (derived from Leverett J function) to water saturation has been used predict the wetting phase saturation. The assignment of capillary pressure curves to their corresponding rock types was accomplished by grouping rock types according to discrete ranges of the interval speed which is defined as the square root of core measured permeability divided by porosity () data. Each range of the interval speed will have a corresponding Leverett J function from which water saturations will be interpolated according to its respective height above the Free Water Level (FWL) (using the log predicted permeability-porosity ratios). In the absence of clear fluid contact, a trial and error method is used to adjust the FWL until a good agreement between the log predicted water saturation and the predicted water saturation from the capillary pressure correlation is reached. The error in averaging was minimized by increasing the number of rock types (minimizing the ranges of interval speed) using a large number of measured high pressure mercury and oil-water drainage capillary pressure data. Furthermore, this paper presents a comprehensive comparison of the log derived and model predicted water saturations. Introduction Reservoir characterization is a central element in field development planning. It is with no doubt that the amount of oil left behind will be reduced with improvements in reservoir description. This can be achieved with better understanding of pore throat structure and distribution in reservoir rocks. Many reservoir description programs, though detailed, have not included well-defined descriptions at the pore-throat scale. Yet, pore-throat attributes control initial/residual hydrocarbon distribution and fluid flow. Efficient field development requires a full knowledge of reservoir rock and fluid properties and their interactions. Rock petrophysical study in combination with Geology and High Pressure Mercury Injection data (HPMI) are the most viable tools to assess reservoir quality, to characterize its lithology and rock types, and ultimately to determine flow units, which are of utmost importance in reservoir modeling. The proper reservoir characterization in combination with a flow simulator which incorporates the dynamical data, such as relative permeability, can then be used to properly assess and manage the reservoir. We recognize many talented investigators have worked in this area during the past 60 years (leverett 1941, O'Meara 1985, Coalson 1985, Flolo 1998, Gunter, Smart, Miller and Finneran 1999, and Shahin and Gunter 2000). The emphasis we have in this paper is on the discrimination between rock types and their relationship to geology and dynamics. Moreover, with acceptable level of accuracy, one can still use the simple water saturation prediction model (leverett J) to initialize the simulation model.
Minimizing uncertainty associated with predicted log water saturation can be best achieved through the integration of log analysis (formation evaluation) and capillarity (core measured high pressure mercury and oil/water drainage capillary pressure data). Further reduction of uncertainty in predicted formation water saturation requires; first the most probable log porosity solution interpretation; second the most probable log porosity/permeability relationship (match core measured values); and third a quantitative capillary pressure model that represents measured high pressure mercury and oil/water drainage capillary pressure data. This paper is a continuation of previous work done using the classical Leverett-J method (Leverett 1941) in integrating capillary pressure data with routine core analysis and petrophysical rock types to generate a robust model for predicting formation water saturation profiles. Two datasets were used in the application of this method in two phases. Phase 1 was accomplished by using the first dataset, which is for a crestal well, in characterizing reservoir rock types by the assignment of capillary pressure curves to their corresponding rock types. This process was accomplished by grouping rock types according to discrete ranges of the interval speed which is defined as the square root of core measured permeability divided by porosity (k / f) data. Each range of the interval speed will have a corresponding Leverett J function from which water saturations will be interpolated according to its respective height above the Free Water Level (FWL) (using the log predicted permeability-porosity ratios). In phase 2, the second dataset, which is from a flank well, was used to quality check the rock typing interpretation and to aerially map the change in reservoir rock types when going from crest to flank. One of the main findings from this work is the importance of quantifying the cementation process that took place in rocks' post-deposition. In the absence of clear fluid contact, a trial and error method was followed to adjust the FWL until a good agreement between the log predicted water saturation and the predicted water saturation from the capillary pressure correlation was reached. The error in averaging was minimized by increasing the number of rock types (minimizing the ranges of interval speed) using a large number of measured high pressure mercury and oil-water drainage capillary pressure data. Furthermore, this paper presents a comprehensive comparison of the log derived and model predicted water saturations. Introduction Reservoir characterization is a central element in field development planning. It is with no doubt that the amount of oil left behind will be reduced with improvements in reservoir description. This can be achieved with better understanding of pore throat structure and distribution in reservoir rocks. Many reservoir description programs, though detailed, have not included well-defined descriptions at the pore-throat scale. Yet, pore-throat attributes control initial/residual hydrocarbon distribution and fluid flow. Efficient field development requires a full knowledge of reservoir rock and fluid properties and their interactions. Rock petrophysical study in combination with Geology and High Pressure Mercury Injection data (HPMI) are the most viable tools to assess reservoir quality, to characterize its lithology and rock types, and ultimately to determine flow units, which are of utmost importance in reservoir modeling. The proper reservoir characterization in combination with a flow simulator which incorporates the dynamical data, such as relative permeability, can then be used to properly assess and manage the reservoir.
This paper illustrates a detailed workflow to build a compositional simulation model for a large gas-bearing with a sizable oil-rim carbonate reservoir in the Middle East. The workflow is emphasising on the importance of data gathering, validation, analysis and integration in building a sound simulation model from geological and dynamical perspective. One of the main achievements of this work was the concept behind matching the variable producing Gas-to-Oil ratio without jeoporadizing the match of other performance parameters. The key resolution to this problem is the careful setup of equilibration and fluid models.
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