Effect of overburden pressure on determination of reservoir rock types using RQI/FZI, FZI* and Winland methods in carbonate rocks
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Rock typing is an important tool in evaluation and performance prediction of reservoirs. Different techniques such as flow zone indicator (FZI), FZI* and Winland methods are used to categorize reservoir rocks into distinct rock types. Generally, these methods are applied to petrophysical data that are measured at a pressure other than reservoir pressure. Since the pressure changes the pore structure of rock, the effect of overburden pressure on rock typing should be considered. In this study, porosity and permeability of 113 core samples were measured at five different pressures. To investigate the effect of pressure on determination of rock types, FZI, FZI* and Winland methods were applied. Results indicated that although most of the samples remain in the same rock type when pressure changes, some of them show different trends. These are related to the mineralogy and changes in pore system of the samples due to pressure change. Additionally, the number of rock types increases with increasing pressure. Furthermore, the effect of overburden pressure on determination of rock types is more clearly observed in the Winland and FZI* methods. Also, results revealed that a more precise reservoir dynamic simulation can be obtained by considering the reservoir rock typing process at reservoir conditions.
KeywordsOverburden pressure Carbonate rocks Rock type Reservoir quality index Flow zone indicator Winland method
Classification of reservoir rocks into different rock types, called reservoir rock typing, is an essential tool in drilling, production and especially reservoir studies. A petrophysical rock type is presented as a group of rock samples that have similar petrophysical and geological properties that influence fluid flow (Stolz and Graves 2003). Generally, petrophysical rock typing is categorized into two separate classes which are petrophysical static rock typing (PSRT) and petrophysical dynamic rock typing (PDRT). PSRT is defined as a group of rocks with a similar capillary pressure curve in the drainage process, whereas PDRT is described as a set of rocks that shows similar fluid flow behavior (Mirzaei-Paiaman et al. 2018). Proper application of rock typing provides more real dynamic reservoir behavior in simulation models (Attar et al. 2015; Saboorian-Jooybari et al. 2015, 2016). Several techniques have been reported in the literature to determine reservoir rock types. These techniques can be classified into two general groups: the theoretical and the empirical methods. The theoretical methods (such as rock quality index (RQI)/flow zone indicator (FZI) (Amaefule and Altunbay 1993), shale zone indicator (SZI) (Jongkittinarukorn and Tiab 1997; Nooruddin and Hossain 2011), modified FZI (Nooruddin and Hossain 2011), FZI* (FZI-star) (Mirzaei-Paiaman et al. 2015) and FZI** (FZI-double star) (Mirzaei-Paiaman and Saboorian-Jooybari 2016) and PSRTI (Mirzaei-Paiaman et al. 2018)) are basically derived from the well-known Kozeny–Carman equation. Empirical methods, such as Winland (Kolodzie Jr 1980; Pittman 1992; Aguilera 2002), generate relationships between porosity, permeability and a specific size of pore throat which is taken from mercury injection capillary pressure tests.
In this work, 113 core samples from one of the carbonate oil reservoirs in the Middle East have been categorized into different rock types using RQI/FZI, Winland and FZI* methods at five different pressures to investigate the effects of pressure on the process of rock type determination. It is worth mentioning that the effect of pressure in the rock typing process has not yet been investigated. In other words, most of the research examines rock type determination at a specific pressure.
In this study, first reservoir rock typing is defined. Then, three selected methods of rock typing are applied to classify studied rock samples at different overburden pressures, and finally, the results of the three methods are discussed thoroughly.
2 Reservoir rock typing
Various techniques have been suggested for classification of reservoir rocks into rock types such as the J-function method, RQI/FZI technique, capillary pressure approach and the Winland method (Soleymanzadeh et al. 2018). Among these methods, RQI/FZI and Winland approaches are the most widely used techniques of rock typing (Winland 1972; Abbaszadeh et al. 1996; Svirsky et al. 2004; Biniwale 2005; Obeida et al. 2007; Shenawi et al. 2007; Chekani and Kharrat 2009; Ye et al. 2011; Riazi 2018). However, as it is concluded from Mirzaei-Paiaman et al. (2018), the RQI/FZI method completely fails in complicated cases such as heterogeneous rocks. Therefore, they suggested that using FZI* gives more reliable results. These methods are described here briefly, and pressure effects on these techniques of rock typing are examined in following sections.
2.1 RQI/FZI approach
The rock typing methods were frequently used to classify reservoir rocks at atmospheric pressure. It is obvious that values of porosity and permeability in reservoir conditions are different from their values at atmospheric pressure. Therefore, using data at atmospheric pressure may result in an incorrect rock typing process and inaccurate reservoir performance prediction. A proper solution for considering pressure effect on rock type is to perform the RQI/FZI method at reservoir pressure.
2.2 Winland method
r35 can be used as a basis to classify a reservoir into different rock types. All rock samples with similar r35 constitute a single rock type and lie on an iso-pore throat curve.
2.3 FZI* method
It is inferred from Eq. (11) that in a log–log scale, the plot of \(0.0314\sqrt k\) versus \(\sqrt \phi\) for an individual rock type shows a straight line with the slope of unity and intercept of FZI* at the ϕ = 1.
3 Description of rock samples
Average and median of porosity, permeability and r35 of the rock samples at different pressures
4 Results and discussion
The first step of the rock typing process is data clustering. There are different clustering techniques can be used in rock typing processes, such as discrete rock type (DRT), histogram, parabolic plots and global hydraulic element (Abbaszadeh et al. 1996; Corbett and Potter 2004). The DRT method was used in this work.
No change: major part of studied samples remained in the same RQI/FZI rock type.
Four different trends due to pressure change based on the RQI/FZI method
58% dolomite, 52% vuggy and 28% anhydrite
76% limestone, 88% vuggy
Figures 8, 9, 10, 11 and 12 show that most of the rock samples shift to the left and downward simultaneously. In other words, this leads to change in the number of rock types and also changing a rock sample from one rock type to another one. In addition, these figures indicate that the number of data in the low k–ϕ zone (blue circle) increases with an increase in pressure.
Four different trends due to pressure change based on the Winland method
60% dolomite, 54% vuggy and 30% anhydrite
82% limestone, 81% vuggy
Four different trends due to pressure change based on FZI* method
61% dolomite, 52% vuggy and 28% anhydrite
72% limestone, 79% vuggy
67% vuggy and 67% anhydrite
Finally, it is noted that having a clearer picture of the rock pore structure, such as from micro-computed tomography (Micro-CT) scans, improves the analysis of the effect of pressure on the determination of rock types.
Studied samples were classified into different rock types using RQI/FZI, FZI* and Winland methods at five different pressures. Different behavior was observed for rock samples during changes in pressure. The majority of the samples remained in the same rock type during pressure increases. Some of the samples shifted from an upper curve to a lower curve, and a few samples change from a lower curve to an upper one. In addition, several of the rock samples showed fluctuating trends. These four different trends can be attributed to the mineralogy and change in pore structure of the studied samples.
Most of the rock samples which remained in the same rock type during pressure changes are dolomitic. It seems that this is related to the lower compressibility or higher density of this type of rock. In contrary, the drastic changes in rock types occur in the limestone rock samples which contain vuggy porosity. The higher compressibility of these samples is the main reason of this behavior.
In RQI/FZI, FZI* and Winland methods, it is observed that the number of rock types increases with an increase in pressure. Also, the number of rock samples in the lower rock types (the lower quality rock types) increases. Furthermore, generally, at pressures greater than a specific pressure (in this study, 4000 psia), the number of rock samples in the higher rock types (the higher quality rock types) remains constant.
The Winland method gives a clearer picture of changing rock samples between different rock types. This is because the Winland method has been developed based on the size of pore throats (r35).
The effect of pressure on the rock type determination implies that the process of reservoir rock typing should be performed at the reservoir conditions.
- Aguilera R. Incorporating capillary pressure, pore throat aperture radii, height above free-water table, and Winland r35 values on Pickett plots. AAPG Bull. 2002;86(4):605–24. https://doi.org/10.1306/61EEDB5C-173E-11D7-8645000102C1865D.CrossRefGoogle Scholar
- Amaefule JO, Altunbay M. Enhanced reservoir description using core and log data to identify hydraulic flow units and predict permeability in uncored intervals/wells. In: 68th Annual SPE conference and exhibition, 3–6 Oct Houston, Texas; 1993. https://doi.org/10.2118/26436-MS.
- Attar MS, Sedaghat MH, Kord S, Mayahi H. Field development strategy through full-field reservoir simulation considering asphaltene precipitation and deposition. In: SPE reservoir characterisation and simulation conference and exhibition, 14–16 Sept, Abu Dhabi, UAE; 2015. https://doi.org/10.2118/175684-MS.
- Biniwale S. An integrated method for modeling fluid saturation profiles and characterising geological environments using a modified FZI approach: Australian fields case study. In: SPE annual technical conference and exhibition, 9–12 Oct, Dallas, Texas; 2005. https://doi.org/10.2118/99285-STU.
- Chekani M, Kharrat R. Reservoir rock typing in a carbonate reservoir-cooperation of core and log data: case study. In: SPE/EAGE reservoir characterization and simulation conference, 19–21 Oct, Abu Dhabi, UAE; 2009. https://doi.org/10.2118/123703-MS.
- Corbett P, Potter D. Petrotyping: a basemap and atlas for navigating through permeability and porosity data for reservoir comparison and permeability prediction. In: International symposium of the society of core analysts, 5–9 Oct, Abu Dhabi, UAE; 2004.Google Scholar
- Kolodzie Jr S. Analysis of pore throat size and use of the Waxman–Smits equation to determine OOIP in Spindle Field, CO. In: SPE annual technical conference and exhibition, 21–24 Sept, Dallas, TX; 1980. https://doi.org/10.2118/9382-MS.
- Obeida TA, Al-Jenaibi F, Rassas S, Serag El Din SS. Accurate calculation of hydrocarbon saturation based on log-data in complex carbonate reservoirs in the Middle-East. In: SPE/EAGE reservoir characterization and simulation conference, 28–31 Oct, Abu Dhabi, UAE; 2007. https://doi.org/10.2118/111112-MS.
- Pittman ED. Relationship of porosity and permeability to various parameters derived from mercury injection-capillary pressure curves for sandstone (1). AAPG Bull. 1992;76(2):191–8.Google Scholar
- Saboorian-Jooybari H, Dejam M, Chen Z, Pourafshary P. Fracture identification and comprehensive evaluation of the parameters by dual laterolog data. In: SPE Middle east unconventional resources conference and exhibition, 26–28 Jan, Muscat, Oman; 2015. https://doi.org/10.2118/172947-MS.
- Shenawi SH, White JP, Elrafie EA, El-Kilany KA. Permeability and water saturation distribution by lithologic facies and hydraulic units: a reservoir simulation case study. In: SPE middle east oil and gas show and conference, 11–14 March, Manama, Bahrain; 2007. https://doi.org/10.2118/105273-MS.
- Stolz AK, Graves RM. Sensitivity study of flow unit definition by use of reservoir simulation. In: SPE annual technical conference and exhibition, 5–8 Oct, Denver, CO; 2003. https://doi.org/10.2118/84277-MS.
- Svirsky D, Ryazanov A, Pankov M, Corbett PW, Posysoev A. Hydraulic flow units resolve reservoir description challenges in a Siberian Oil Field. In: SPE Asia Pacific conference on integrated modelling for asset management, 29–30 March, Kuala Lumpur, Malaysia; 2004. https://doi.org/10.2118/87056-MS.
- Winland H. Oil accumulation in response to pore size changes, Weyburn field, Saskatchewan: Amoco Production Company Report F72-G-25, 20. p. 1972.Google Scholar
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