SPE 161427 Water Saturation Modeling in Khafji Carbonate Reservoir PDF

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SPE 161427 Water Saturation Modeling in Khafji Carbonate Reservoir Mohammad H. Al-Otaibi, SPE, Al-Khafji Joint Operations; Rafael Khamatdinov, SPE, Al-Khafji Joint Operations; Nasser Al-Khaldi, SPE, Al-Khafji Joint Operations; Rabei Abdelrahim, SPE, Schlumberger; Mohamed Bouaouaja, SPE, Schlumberger...

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SPE 161427 Water Saturation Modeling in Khafji Carbonate Reservoir Mohammad H. Al-Otaibi, SPE, Al-Khafji Joint Operations; Rafael Khamatdinov, SPE, Al-Khafji Joint Operations; Nasser Al-Khaldi, SPE, Al-Khafji Joint Operations; Rabei Abdelrahim, SPE, Schlumberger; Mohamed Bouaouaja, SPE, Schlumberger

Copyright 2012, Society of Petroleum Engineers This paper was prepared for presentation at the Abu Dhabi International Petroleum Exhibition & Conference held in Abu Dhabi, UAE, 11–14 November 2012. This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.

Abstract Within an oil reservoir the water saturation height functions can vary strongly, especially for carbonate rocks. These variations can be significant and difficult to estimate. The amount of hydrocarbons in place, the prediction of recoverable oil, the recovery process and the future plans of developing such reservoirs depend on many factors, one of which is the accurate modeling of water saturation. The Khafji carbonate reservoir is a heterogeneous reservoir with two different types of oil: light oil in the top of the reservoir and heavy oil in the bottom of the reservoir. The challenge of water saturation modeling is primarily in the heavy oil zone, where conventional height function techniques produces poor match against measured water saturation logs. Alternative methods were utilized in order to obtain good match in both light oil and heavy oil columns. A workflow has been created in order to overcome these challenges. Laboratory derived capillary pressure curves were used to establish water saturation height relationships as a function of rock type (RT). Additionally, a Flow Zone Indicators (FZI) analysis was used as a basis for rock typing. Then a J-function derived from capillary pressure data for each rock type or hydraulic flow unit (HFU) was used to generate saturation height function for each RT. The generated saturation undergone via several iterations to match the large span of openhole electric water saturation logs above the free-water level (FWL). The saturation profile generated by this workflow shows a good match to the measured Sw electric logs, and the calculated fluid volumes are in agreement with company’s approved reserves estimation. Introduction The Khafji carbonate reservoir was discovered in 1960’s and was put into production by the end of 1968. It is a heterogeneous carbonate reservoir with two different types of oil: light oil in the top of the reservoir and heavy oil in the bottom of the reservoir. There is a substantial amount of interpreted openhole electric water saturation logs available from about 40 wells, covering majority of the wells in the field. There are also around 90 measurements of laboratory-derived capillary pressure data, which are covering about 24% of the wells in the field, and were used to define water saturation-height relationships as a function of rock type and reservoir / fluid conditions. After validation and processing of laboratory-derived capillary pressure data, and correcting it for reservoir condition, capillary pressure curves were translated into height above FWL. The first attempt was done with the popular Leverett J-function approach to generate universal, normalized saturation curves for the field based on the porosity derived RT; however, the results show poor match against the measured electrical water saturation logs due to heteroginity of the reservoir, as well as different fluid properties. To overcome the above limitations, HFU approach using modified FZI was developed, taking into account the flow behaviour of rocks. Saturation profiles were created for the Khafji carbonate field and their validity were checked against saturation profiles interpreted from petrophysical logs. This workflow characterizing the water saturation in the field is an integrated work where core data, perophysical log interpretation and geological model were utilized together.

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Defining Hydraulic Flow Units based on the FZI Flow Zone Indicator (FZI) was calculated as a property and was assigned to each grid block in the model, derived from the already known parameters of porosity and permeability:

FZI = 0.0314 K φ

1−φ

φ

Where K is Permeability, mD, φ is Porosity, fraction. The cumulative density of FZI was used to initially split the reservoir into five rock types that have probability range of FZI values (Figure 1).

Figure 1. FZI cumulative density and the split into 5 FZI ranges to initially define the rock types

This approach is preliminary classification that should help to group samples from the reservoir that would have similar rock quality, the FZI indicator is being a direct re-formulation of the Rock quality indicator (RQI):

RQI = 0.0314 K φ FZI = RQI

1−φ

φ

It is assumed that the modeling of the water saturation is in direct relation with the quality of the reservoir. The hypothesis is that the reservoir regions with bad characterestics in term of porosity and permeability, thus in term of RQI and FZI, would have higher water saturation either by having higher connate water saturation or higher trasition zone. A plot of permeability vs. porosity sorted by the calculated FZI ranges was used to quality check the equal coverage of initial FZI goups (Figure 2).

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Figure 2. Permeability vs. Porosity sorted by the 5 initially identified ranges of FZI

The five identified ranges of FZI were used to group the drainage capillary pressure laboratory data for Khafji carbonate reservoir. All the Pc lab measured data was first corrected for laboratory conditions and then corrected to reservoir conditions by using the following equation:

 (σ cos θ ) res  Pc(res) = Pc(lab)    (σ cos θ ) lab  Where Pc is Capillary pressure, psi, σ is Interfacial tension between the fluids, dyn/cm θ is Contact angle relating wetability and rock fluids, degrees. The corrected to reservoir conditions Pc data was then loaded into SCAL software (Figure 3) and grouped based on the FZI ranges. The objective was to subgroup Pc curves within each FZI group, so that all curves in the same subgroup will have similar shapes, and thus will be representing the same RT. A number of differentiating criteria was set in order to make the grouping process.

Figure 3. All Pc curves corrected to the reservoir conditions

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At initial iteration, Pc curves were grouped according to the initially defined FZI ranges. The FZI range split was not producing groups of Pc curves with similar shapes and amplitude. Sub-grouping with additional criteria such as permeability and/or porosity was required to subdivide into more HFU to achieve better grouping of Pc curves. Following the above procedure 11 HFU were created. Each HFU is represented by number of Pc curves with similar shape. The example of the subgrouping into several HFU is presented in Figure 4.

Figure 4. An FZI based Pc group divided into further two Pc subgroups based on other criteria (Pereability and/or porosity)

The same HFU criteria were used in the 3D model to calculate HFU for each cell, having distribution of 3D properties of the FZI, permeability and porosity. Based on the above procedure each grid block was assigned to one of the 11 RT based on the HFU value. Water Saturation Modeling As mentioned above, the Leverett J-function approach was utilized for initial generation of the one universal saturation curve, but that method was not suitable for heterogeneous rocks in Khafji carbonate field. As shown in Figure 5 the saturation curves produced by this method does not match saturation profiles interpreted from electrical logs.

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Figure 5. Saturation curves produced from conventional Leverette J-Function against water saturation curves interpreted from electrical logs for some wells of Khafji carbonate field

To overcome this problem, it is suggested to calculate two J-functions: one - as function of water saturation, and the other as Leverett J-function based on the capillary pressure data, permeability and porosity. The capillary pressure can be obtained from the directly measured laboratory data; as well it can be derived from the Height function based on the fluid densities and the hight above the fluid contact. For each Pc curve a J-function was calculated (having porosity, permeability and wettability data of each sample) using the following formulae:

J=

0.217 Pc σ cos θ

K

φ

End points can vary for Pc curves within the same HFU group, therefore normalization was applied. All J-function curves within each HFU were plotted together and resulting power trend line was created. Example of normalized J-function with resulting trend line for one of the 11 HFU is shown in Figure 6. The resulting trend line for each HFU can be represented with the following equation:

J n = aS wn Where Jn is Normalized J-function, Swn is Normalized water saturation, fraction, a, b are Coefficients.

b

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Figure 6. Normalized J-function curves generated from Pc curves of HFU-1 with resulting power trendline

Thus, for each HFU parameters “a” and “b” were defined and then utilized in the 3D model in the following steps. The 3D model has structural parameters of the grid, contacts, and a distribution of porosity and permeability. A new property of Pc was created in the 3D model for each grid block with Height function and utilizing the 3D model properties:

Pc =

H (ρ w − ρo ) 144

Where H is Hight above FWL, feet, ρw is Water density, lb/ft3, ρo is Oil density, lb/ft3. Utilizing the newly obtained Pc, the J values for each cell can be calculated using the same Leverette J-function formulae:

J=

0.217 Pc σ cos θ

K

φ

Having J value for each grid cell in the 3D model, and utilizing the averge normalized J function from the SCAL analysis per each HFU, the normalized water saturation can be calculated using the following equation:

S wn = ( J / a )1 / b Where J is J-value calculated for each grid cell a, b are Coefficients from the SCAL exercise for each HFU Finally, the normalized water saturation for each cell was denormalized using the following formulae:

S w = S wi + S wn (1 − S wi ) Where Sw is Denormalized water saturation, Swi is Connate water saturation for each hydraulic unit. If 3D model Sw match against water saturation electrical logs are still not achieved, adjustment of parameters “a” and “b” might be required for certain HFU.

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Results Using the above workflow a good match of Sw against the log derived water saturation was obtained in the Light oil zone. For the heavy oil zone several iterations on “a” and “b” were required. Figure 7 shows some of the Khafji carbonate field wells, where calculated 3D model saturations show very good match against saturation profiles from electrical logs by utilizing this workflow.

Figure 7. Calculated water saturation of the 3D model vs. water saturation profiles interpreted from logs for some wells of Khafji carbonate field

Summary and Conclusion: • Improved HFU identification based on modified FZI has been developed. Based on that a new workflow for rock typing and water saturation modeling has been created. This workflow involes integrated approach that include data from various domains: geology, petrophysics and reservoir engineering. •

The water saturation calculation is done utilizing combination of traditional methods, but in different manner. The capillary pressure is measured in the lab and transferred to the grid via J-function calibrated to match the water saturation against water saturation derived from the logs.

•

The saturation profile generated by this workflow shows a good match against saturation profiles interpreted from petrophysical data, and the calculated fluid volumes are in agreement with company’s approved reserves estimation.

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Abbreviation RT: Rock Type FZI: Flow Zone Indicator FWL: Free Water Level RQI: Rock Quality Indicator HFU: Hydraulic flow units References: 1. S. Biniwale, SPE, Australian School of Petroleum, The U. of Adelaide “An Integrated Method for Modeling Fluid Saturation Profiles and Characterising Geological Environments Using a Modified FZI Approach: Australian Fields Case Study”, paper SPE-99285. 2. Kassem Ghorayeb, Leaong Hooi Tan, Manoch Limsukhon, SPE, Schlumberger, and Rafi Mohammad Aziz, SPE, Kuwait Oil Company, “Ensuring Water Saturation Consistency Between Static (Fine-Grid) and Dynamic (Upscaled) Models - A case Study of the North Kuwait Jurassic Complex”, paper SPE-125568. 3. Shamsuddin H. Shenawi, Jerry P. White, Emad A. Elrafie, Khaled A. Kilany, SPE, Saudi Aramco, “Permeability and water saturation Distribution by Lithologic Facies and Hydraulic Units: A Reservoir Simulation Case Study”, paper SPE-105273. 4. Tawfic A. Obeida, Yousef S. Al-Mehairi and Karri Suryanarayana, SPE, Abu Dhabi Company for Onshore Oil Operations (ADCO), “Calculation of Fluid Saturations From Log-Derived J-Functions in Giant Complex Middle-East Carbonate Reservoir”, paper SPE-95169....