Systematic Rock Typing in an Iranian Oil Reservoir

Mohammad Reza Rasaei 1 , Shobeir Nabavi 2

1 :Asistant Professor, IPE,Tehran University 2 : M.Sc., IPE,Tehran University

Abstract

The degree of success in many production activities and secondary recovery processes depends on the accuracy of the models used in the reservoir description. Reservoir rock typing is one of the most essential parts of proper reservoir characterization. The goal in optimum rock typing is to overcome the extreme heterogeneity of the reservoir. This is to decrease the effects of heterogeneity for normalizing/averaging capillary pressure curves and estimation of permeability. Rock typing and hydraulic flow unit identification are elaborated to integrate both geological and petroleum engineering data. Flow unit is defined as a group of reservoir rocks with similar properties that affects fluid flow. Geological/petrophysical characterization incorporated the analysis of the complex variations in pore and pore throat geometry that control initial and residual fluid distribution. An under­saturated oil field reservoir in southwest of the Zagros belt in Iran was considered in this study. Asmari formation in this reservoir compromised of two main Carbonate and Sandstone bodies. Four lithotypes of Shale, Limestone, Dolomite, and Sandstone have been determined from geological and petrophysical studies. In this study, conventional porosity and permeability, mercury injection, capillary pressure, relative permeability and mineralogical data were used to characterize the reservoir pore systems into rock types having similar flow and storage capacity. Water Saturation, all of which is considered immobile, was found to be dependent on rock type, with pore throat being the dominant control on the flow characteristics of the reservoirs. Also, a different flow unit definition of FZI/RQI concept was applied on this field. Good consistency was observed between lithotypes and rock types to compromise the views of both geologists and reservoir engineers.

Keywords: Rock type­ Litho type­Hydraulic Flow Unit (HFU)­ Pore Size Distribution (PSD)­Leverett J­Function­mercury injection­ RQI/FZI

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Introduction

The field of interest in this study is situated in southwest of the Zagros belt and extends from northwest to southeast. This oil bearing reservoir is 180 km northwest of the Ahwaz field, at the edge of Dezful embayment. The field is 28.5 km long and 4.5 km wide at the top of the Asmari formation. The first well on this structure was drilled in 1967. Of the total 9 wells drilled so far, 2 of them are relatively new and were drilled in 2004. Geological and sedimentological studies showed that Asmari formation in this reservoir compromised of two main Carbonate and sandstone bodies. Although 30 years from discovery of this field has elapsed, unfortunately no in­depth study of this field has been carried out. As a result, even a preliminary dynamic model of this field does not exist. In the course of developing a proper dynamic model, building valid and accurate static model, to the possible extend, is of prime importance. In this phase, proper characterization and rock typing of different geological features cannot be overwhelming. For the determination of the reservoir rock type and HFUs, different factors or parameters can be used. For example, reservoir rocks that exhibit similar or even close properties such as water saturations, porosities or permeabilities can be used in the grouping of the rock types. In general, determination of the reservoir rocks can be categorized according to their lithology, petrophysical properties, and/or their geological features. Yet in another way, they can be classified according to their dynamic physical characteristics related to permeability. The first approach is usually elaborated by geologists. The second method, preferred by reservoir engineers, takes into considerations such factors as their ability to transmit fluids (permeability). Reservoir engineers prefer to use the latter method in the coarse dynamic model building. Since the dynamic behavior of rocks is the outcome of minute behavior of fluids in relation to pores, either methods or approaches should yield close outcomes. In the following sections, the strategy applied to use all available information to get to a sound rock typing is presented. Efforts have been made to use all data at different scales and nature such as thin sections, log data, geological and sedimentological studies, cores and routine and SCAL tests.

Rock Typing Based on Geological Considerations

As said before, Asmari formation in this reservoir has 7 members that compromised of two main Carbonate and Sandstone bodies.4 first members of As­1a, As­2a, As­1b and As­2b are carbonates and 3 second members of As­3, As­4 and As­5 are sandstones. In determining reservoir rock types, based on extensive petrophysical, sedimentology and sequence stratigraphy, four types (lithotypes) have been determined and taken into account: Shale, Limestone, Dolomite, and Sandstone. Anhydrites are another element exist in the area which is the main constitute of cap rock. This rock has no reservoir

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properties and detected only as small traces in parts of As_1a adjacent to cap rock. So, there is no gain of considering anhydrites as individual rock type in reservoir rock typing. Mean porosity values of these lithologies are 7%, 10% and 24% for Limestone, Dolomite, and Sandstone rocks respectively. This put the limestone as the worst and sandstone as the best reservoir rocks in this reservoir in terms of their average porosity values. Irreducible water saturation and porosity Relation Rock type analysis based on the relation of Swirr and porosity in the tested cores is another routine which can be applied. Figure 1 shows distribution of Sw vs. porosity in three main rock types mentioned above. It is clear that sandstones have minimum water saturation while limestones have the maximum value. This again confirms the rock typing done based on lithology of the cores.

0

10

20

30

40

50

60

70

80

90

0 5 10 15 20 25 30

Phi(%)

Swirr(%)

Dolomite Lim

Sand

Figure 1­ Swirr vs. porosity from SCAL tests for different rock types

Mercury Injection Data for Rock Typing In addition to the above methods in the classification of reservoir rock types, mercury injection method can also be used. Mercury injection method shows the distribution of throat sizes within the porous rock sample. Although the distribution of throat sizes possesses systematic and proven discrepancies, it can form a relative basis upon which different samples can be compared. In this reservoir, mercury injection test have been carried out on 58 plugs taken from the cores of one well and the distribution of porosity have been obtained. Based on the distribution of bottlenecks, reported by the lab, average quantities of throat sizes (BRC) and their apparent radius, after injection 35% mercury (R35%) have been calculated. In order to articulate the relation between the permeability of samples and their throat size in each core, the average thickness of the throat sizes according to the permeability is drawn on the logarithmic chart. The somewhat large scatter of points indicated lack of correlation between permeability and thickness of throat sizes does not support

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theoretical considerations. Additional considerations in this regard, showed that the majority of mercury injection tests do not have the required validity and they should not be taken into account. The rule of thumb presented by Bruno Grainer was applied which postulates that the average thickness of throat size of the sample should be equal or less than the apparent thickness of throat size after the injection of 35% mercury. After the elimination of doubtful lab results, mentioned above, only 24 cores were selected to examine their throat sizes. Change in throat size vs. permeability is plotted again and the trend of change demonstrates an acceptable relationship between the two as shown in figure 2. To further determine the throat size for each litho type, in petrophysical studies, the type is shown next to the appropriate throat size and then sorted on that basis. The result of this is shown in table 1. Cores with less than 12 micrometer throat size are limestone and those with 12­18 micrometer are dolomites. Sandstones show values more than 18 micrometer. Thus, there is a good relationship between rock types and throat sizes (at least in the samples of one well).

Figure 2­ Averaged pore­throat size vs. permeability for all 50 plugs (Left) and for 24 valid plugs (Right)

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Table 1­ Average throat size for various rock types

Lithology Sample Depth(m) Porosity (%) Perm. (mD) BRC (µm) R35 (µm) Lim S12A 3444.9 17.5 26 7.61 8.95 Lim S22A 3471.84 12.8 4.02 8.15 8.34 Lim S25A 3474.59 5.6 7.9 11.04 13 Lim S11A 3442.39 9.4 17 11.9 12 Dol S16A 3458.81 13.1 18 12.3 13.5 Dol S33A 3485.66 14.7 74 12.33 14.6 Dol S26A 3475.1 14 8.1 12.57 13.4 Dol S27A 3476.03 21.3 157 14.25 15 Dol S31A 3483.5 15.5 320 15.5 21.6 Sand S53A 3546.01 23.7 241 18.36 21.7 Sand S45A 3521.9 14.7 35 18.38 20 Sand S55A 3548.17 25.8 1383 21.53 26.3 Sand S60A 3554.28 25.3 1359 22.29 27.25 Sand S46A 3523.2 17.8 2005 26.99 30 Sand S47A 3524.23 25.1 2322 28.43 32.5 Sand S39A 3513.78 26.4 3941 31.04 36.5 Sand S40A 3514.64 26.1 2465 32.11 36 Sand S41A 3515.32 28.2 6735 36.07 40.5 Sand S36A 3510.52 29 1679 39.71 47.5 Sand S37A 3511.79 27.2 3798 43.52 55 Sand S35A 3509.72 23.6 7396 45.23 56 Sand S42A 3516.68 27.5 7531 53.32 60 Sand S43A 3517.56 27.1 7868 58.36 65.3 Sand S76B 3621.7 24.9 6592 65.88 83

Conventional Core Analysis Tests (CCAL)

Cores of the reservoir were taken in three wells. From these three wells altogether some 667 plugs have been recovered, tested, and their porosity and ambient permeability were determined. One of the most important usages of results obtained from cores is establishing correlations between porosity and permeability in the rock types of reservoir for the construction of the geological model. If a reasonable correlation established between these two parameters (porosity and permeability), this correlation and the results obtained from well logs can form a solid basis for the distribution of these two parameters in the building of the geologic model. When there are enough data for each well and they are well separated as different categories, one can do the analysis in well by well bases and investigate areal variation of properties. Here we examine to see how the data of different wells distributed and to see if we can do such analysis. Figure 4 compares data of 3 wells which have conventional core tests. As this figure shows, there is no such difference between data of different wells and one get no advantage of considering each well separately. From permeability versus porosity correlations in different wells, separation

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of core plug samples based on lithology still does not assist in better categorizing of data and no major change between the wells is observable.

Figure 4­ Comparison of core data from different wells (up left), porosity and permeability Relationship in Sandstone plugs in different wells (up right), in Dolomite plugs (down left) & in Limestone (down

right)

Establishment of relationships based on petrophysical properties of various reservoir rocks The impact of rock types and their respective lithologies for the establishment of the relationship between porosity and permeability should be taken into account. To begin with, cores based on the rock type and lithology taken from each well was separated. As discussed before, based on petrophysical studies four rock types (lithotypes) were identified in this reservoir. These rock types are Shale, Limestone, Dolomite, and Sandstone. Shale is not considered a suitable reservoir rock and was eliminated. The remainder was divided into limestone, dolomite, and sandstone. The relationship between their porosities and permeabilities was then established. The distribution of permeability values vs. porosity shows a wide dispersion. To eliminate this wide variance, the averaging method, as explained below, was employed and will be elaborated upon.

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Since the porosity values can be added, calculation of their average values can easily be obtained. However, permeability data cannot be summed and their average obtained. There does not exist an agreed upon method for the determination of the average permeability. However, the proven theory for this purpose is as follows: The arithmetic mean for parallel layers can be calculated and is dominated by the higher permeability. This averaging will, naturally, show the mean of the upper mean values. Harmonic averaging for flows perpendicular to the layers is controlled by the lesser permeability and thus the average shows lower values. The geometric averaging applies for cross flow from regions which have mixed disordered values of permeability. Based on the above, the average permeability of various ranges of porosity is calculated. In the limestone rocks, 138 cores were analyzed and the result is shown in figure 5. This figure depicts the general correlation between permeability and porosity in this rock type and displays the relationship based on arithmetic, geometric, and harmonic averaging. The averages show the range of values of permeability and enable us to consider both pessimistic and optimistic cases. The relationship based on the geometric mean can form the base case for the construction of permeability distribution in the dynamic model. Once the model is built and in the phase of history matching one can move in either direction (arithmetic or harmonic) to see which of the cases results in a better match.

Figure 5­ General relationship of permeability and porosity for limestone (left), average permeability based on average porosity for limestone (right)

In the dolomite rocks 208 cores were analyzed (ordinary analysis). The results of these analyses are shown in figure 6. This Figure shows the relationship between permeability and porosity in this reservoir rock type and the arithmetic and geometric means of this relationship. The relationship based on the geometric mean shows larger R 2 than the others and so can form the base case for the construction of permeability distribution.

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Figure 6­General relationship of permeability and porosity for Dolomite (left), average permeability based on average porosity for Dolomite (right)

From the sandstone reservoir rocks altogether 110 cores were analyzed. The results are shown in figure 7. This figure shows the general relationship between permeability and porosity based on arithmetic, geometric, and harmonic averaging. Another time, the relationship based on the geometric mean is the best.

Figure 7­ General relationship of permeability and porosity for Sandstone (left), average permeability based on average porosity for Sandstone (right)

6­ Special Core Analysis (SCAL) Determination of rock types based on fluid behavior and dynamics requires SCAL on the core samples of the field. SCAL analysis includes capillary pressure curves and relative permeability of oil and water. In one well, SCAL tests have been carried out and relative permeability and capillary pressure curves obtained for some cores. Analysis includes 40 samples of capillary pressure and 11 relative permeability measurements. Capillary pressure curves using centrifugal method for both drainage and imbibition cycles were obtained. The capillary pressure curves show a wide range of variations indicating different pore geometries and reservoir properties. Since the capillary pressures curves are the direct result of structure and their geometric sizes and throats, for their proper classification, one has to pay special attention to lithology. For this reason, cores were classified according to their rock type and petrophysical characteristics into: limestone, dolomite, and sandstone. To conduct a proper comparison of capillary pressures of various samples

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with different porosities and permeabilities, one must draw these curves in dimensionless form and normalize the water saturation as below. This objective is achieved by using the square root of permeability divided by porosity of each sample and construction of the so called J­function, as displayed below:

( ) / ( )

cos c w

w P S k

J S ϕ

σ θ = , 1

w w irr w n o rm

w ir r o r

S S S S S

− =

− −

Figure 8 shows lithology based categorization of dimensionless drainage J­function for all available core plug samples. As this figure shows, lithology can nicely categorize the capillary pressure curves.

Figure 8­ Lithology based categorizing of available dimensionless capillary pressure curves

For a better comparison, classification of curves based on their lithology was done and each group was averaged so as to construct a representative J­function curve for each lithological group. As can be seen in figure 9, the J­function for limestone shows the smallest and sandstone the largest values indicating the worst and the best reservoir rock characteristics respectively. Dolomites show a relatively better reservoir rock quality compared to limestone. A similar approach was carried out on the capillary pressure of cores. Average Capillary Pressure Curves in different reservoir rock samples are shown in this figure. Based on these, limestone that has the largest irreducible water saturation 57% exhibits the worst reservoir rock type. Sandstones, with irreducible water saturation around 18%, are the best reservoir rock type. Dolomites with irreducible water saturations around 36% are considered medium quality reservoir rocks. The reliability and the relevance of each group of curves will be examined in the dynamic model building. As explained, the measurements of relative permeability were carried on 11 cores. Of these, in 7 cases capillary pressure tests on cores were carried out evenly thus enabling a valid comparison. Capillary pressure curves in comparison with permeability curves should show smaller end point saturations and so validity of curves were checked with this rule and all them were correct.

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Figure 9­ Averaged J­function (left) and Averaged Pc curves (Right) for three reservoir rock types

0

0.2

0.4

0.6

0.8

1

0 20 40 60 80 100 Sw (percent)

Kr (fraction)

Kr­dolomite

Kr­limestone

Kr­sandstone

Figure 10­ Averaged relative permeability for all reservoir rocks

Similar to classification of capillary pressure curves; lithological considerations were also taken into account for the construction of relative permeability curves. Attempts were made to construct the averaged curves for each rock type (limestone, dolomite, and sandstone). The results show that 1 core falls into limestone rocks, 4 cores in the dolomites, and 6 cores in the sandstones. The combination of all three relative permeability curves for limestone, dolomite, and sandstones is shown in figure 10. These curves are normalized and the normal saturation for water phase (Sw,norm) and relative permeability of normal phase (Krp,norm) are calculated with the following equations:

, ,max

rp rp norm

rp

K K

K = & , 1

w wirr w norm

wirr or

S S S S S

− =

− −

Unfortunately in the limestone group, only one core exists and the relative permeability curve for this rock type cannot be considered representative of the population (limestone

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rocks of the reservoir). Nevertheless, it was temporarily used until such time that the dynamic model is constructed and in the history matching process the required adjustment could be applied. Based on the end points of saturation and relative permeability values of maximum phases, limestone, dolomite and sandstone show the maximum to minimum irreducible water saturation and maximum to minimum effective permeability of the water phase (the ability to flow water). Thus, the quality of the reservoir rock can be classified as poor, intermediate, and good. A critical look at figure 10 reveals that the normalized relative permeability curves for the three rock types (limestone, dolomite, and sandstone), especially in the oil phase, are comparable and thus can be exhibited as one general curve. However, to facilitate the history matching process, we recommend the use of separate relative permeability for each rock type.

Rock Typing Based on RQI­FZI Method Amaefule et al. introduced the concept of Reservoir Quality Index (RQI) considering the pore­throat, pore and grain distribution, and other macroscopic parameters and the concept of flow zone indicator as defined from the basic equation of below:

gv s e

e

e S F k

τ φ φ

φ 1

1 0314 . 0

−

= , e

k RQI φ

0314 . 0 = , gv s S F

FZI τ 1

= ,

−

= e

e n φ

φ φ

1

Thus this Equation can be written as: Log RQI= log n φ +log FZI This equation yields a straight line on a log­log plot of RQI versus Φn with a unit slope. The intercept of this straight line at Φn =1 is the flow zone indicator (FZI). Samples with different FZI values will lie on other parallel lines. Samples that lie on the same straight line have similar pore throat characteristics and, therefore, constitute a flow unit. This method was applied for 590 plugs of all the wells that routine analyses on them were carried out. These samples had the lithology of Sandstone, Limestone and Dolomite. Index of Rock Quality and Normalized porosity are plotted on log­log scale as can be seen in figure 11.

0.001

0.010

0.100

1.000

10.000

0.0010 0.0100 0.1000 1.0000

RQI

Phi n

Dolomite

Limestone

Sandstone

Figure 11 ­ RQI­FZI for 3 lithology groups of reservoir rocks

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As can be seen in figure 11, one cannot separate the 3 groups of different rock type in the graph and all data shrink in one small area. So this method isn’t proper for HFU classification of the reservoir zones.

Conclusion An integrated approach is presented for proper rock typing to honor both geological and flow behavior characteristics of the cores. It is recommended to start rock typing based on geological and petrophysical properties of the cores and then do the next tuning based on mercury injection, routine and SCAL testes. This can results to a proper initial grouping of rock types and then obtain the representative dynamic functions for each of the confirmed groups. Throat sizes of mercury injection and J­functions are good check points for this confirmation. Although the method of RQI/FZI is one of the methods for Flow unit classification but it is not applicable for this reservoir and no consistency between this method and the others seen.

Acknowledgement This research was kindly supported by ICOFC. We would like to give special thanks to Mr. Ziyaei and Mr. Jahankhah for their support and cooperation.

10­ References: 1­Bruno Granier, A new approach in rock­typing, documented by a case study of layer cake reservoirs in field “A”, offshore Abu Dhabi (U.A.E), Notebooks on Geology, Article 2003/04 (CG2003_A04_BG).

2­(G.V. Chilingarian, S.J. Mazzullo, H.H. Rieke, Carbonate reservoir characterization: a geologic­ engineering analysis, part I, Elsevier, 1992).

3­Renard, Ph., Calculating of equivalent permeability: a review, Advances in water resources, Vol 20, 1997.

4­Wen X­H., and Gomez­Hernandez J.J., Upscaling hydraulic conductivities in heterogeneous media: an overview, Journal of hydrology 183 (1996).

5­Varavur S.,Shebl H.,Salman S.M. , Reservoir Rock Typing in a Giant Carbonate ,SPE 93477

6­Pittman, E.D. 1992, Relationship of Porosity and Permeability to Various Parameters Derived from Mercury Injection­Capillary Pressure Curves for Sandstone. Bull.American Association of Petroleum Geologists, 76, 191­198.

7­Stephanie Lafage, An Alternative To The Winland R35 Method For Determining Carbonate Reservoir Quality, August 2008

8­Amaefule, J.O, Altunbay, M., Tiab, D, Kersey, D.G., and Keelan, D.K.: "Enhanced Reservoir Description: Using core and log data to identify Hydraulic (Flow) Units and predict permeability in uncored intervals/wells", SPE 26436, presented at 68th Ann. Tech. Conf. And Exhibit., Houston, Tx, 1993.

9­Pestov, V.V., Doroginitskaya L.M.: "Application and integration of core, log and well testing data for reservoir description", JSC "TomskNIPIneft", final project report # 243.5­384 prepared for Yukos EP, Tomsk, 2002.

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