Hydraulic flow unit classification from core data: Case study of the Z gas reservoir, Poland

Nội dung text: Hydraulic flow unit classification from core data: Case study of the Z gas reservoir, Poland

1. Journal of Mining and Earth Sciences Vol. 62, Issue 3 (2021) 29 - 36 29 Hydraulic flow unit classification from core data: case study of the Z gas reservoir, Poland Man Quang Ha 1,*, Anh Ngoc Le 2, Jadwiga Jarzyna 3 1 PetroVietnam Exploration Production Corporation - PVEP, Vietnam 2 Faculty of Oil and Gas, Hanoi University of Mining and Geology, Vietnam 3 AGH University of Science and Technology, Poland ARTICLE INFO ABSTRACT Article history: th Permeability and porosity are essential parameters for estimating Received 09 Feb. 2021 hydrocarbon production from reservoir rocks. They are combined in an Accepted 24th May 2021 additional factor, the Flow Zone Index (FZI), which is the basis for defining Available online 30th June 2021 the hydraulic flow unit (HFU). Each HFU is a homogeneous section of a Keywords: reservoir rock with stable parameters that allow for media flow. Flow Zone Index, Hydraulic flow units are determined from the porosity and permeability of core or well logs. The simple statistical methods are applied for HFU Global Hydraulic Elements, classification and then improve permeability prediction. This paper also Hydraulic Flow Unit, shows how to quickly apply the global hydraulic elements (GHE) method Permeability prediction. for HFU classification. The methodology is tested on the Miocene formation of a deltaic facies from the Carpathian Foredeep in South- Eastern Poland. Copyright © 2021 Hanoi University of Mining and Geology. All rights reserved. devices provide relatively good data, but it is still 1. Introduction difficult to parameterize factors such as tortuosity, Porosity and permeability are two properties specific surface, or the radius of pores in a rock of reservoir rock that strongly influence the formation. To overcome these difficulties, the movement of media in the rock’s pore space. parameters of Flow Zone Index (FZI) and then Formulas for permeability vs porosity defined by Hydraulic Flow Unit (HFU) are defined as primary Cozeny and Carman (1927, 1937) are the most parameters that implicitly describe the ability of known equations used to describe water and media to flow in the pore space of reservoir rock. hydrocarbon ability to move throughout the pore Our goal is to prepare petrophysical data for space of rock. Improved lab equipment and logging modelling media flow in the pore space of reservoir rock. The rock formation represented by core ___ parameters and log data is divided into homogeneous hydraulic flow units. Core data will *Corresponding author E - mail: manhq@pvep.com.vn be used to calculate the Flow Zone Index (FZI) in DOI: 10.46326/JMES.2021.62(3).04 cored sections of the wells and then apply some
2. 30 Man Quang Ha and et al./Journal of Mining and Earth Sciences 62(3), 29 - 36 statistics method for HFU classification basic on FZI Carpathian Foredeep, is multi-horizontal. mean value. In this study, we also apply the Global Sequences of thin shale laminas are sealing gas Hydraulic Element (GHE) method introduced by horizons in thin sandstone strata and prevent the (Corbett et al., 2003, 2004; Matyasik et al., 2007) movement of gas up to the top of sedimentary for a rapid and more straightforward approach to systems. Porous sandstones of high permeability plot the porosity and permeability data on the belong to depositional elements of submarine fans, predetermined GHE template. sandstones of deltaic environments (largemouth bars, distributor channels, and others), shallow 2. Geological setting and data set marine clastic deposits of estuaries, and sandy barriers. In those horizons sandstones with gas are 2.1. Geological setting difficult to recognize because of the low contrast of The majority of gas deposits recognized in the parameters between sandstones and shales. Carpathian Foredeep basin occurred in the Deltaic sediments are well recognized in the Miocene strata. Most of them are small but Carpathian Foredeep. Sandstone reservoirs economically important. Generally, in all deposits, distribution in the Z gas field is of very good good reservoir properties are observed. The Z gas reservoir parameters (Mysliwiec, 2006; Mysliwiec field was discovered in the early 60 of the 20th et al., 2004). century in the Northern part of the Carpathian ForedeEquation The main gas horizons occurred in 2.2. Data set the upper part of the Sarmatian argillaceous - Core data from wells in the Z gas field in the NE arenaceous sequences at a depth interval between Polish part of the Carpathian Foredeep were 390 and 545 m. Good reservoir parameters were available (Figure 1). Laboratory core observed. The deeper productive horizons were measurements included effective porosity (Φe) discovered on the basis of a new approach to the and absolute permeability (K), which were taken seismic anomalies interpretation in the latest 90. from various depths in the selected wells. The Now, the Z gas field is recognized in three parts: study dataset included 570 core samples from 11 Eastern, central, and western. Productive horizons wells, and the core interval ranges from 253 m to are discovered in the whole profiles of wells in the 1154 m. Only three samples were taken from Sarmatian deposits ( Figure 1). littoral facies at a depth between 253÷272 m, and The Z gas reservoir, like many others in the the other one was taken from a deeper part section. Figure 1. Location of the Z gas - field with the 11 wells location (after Mysliwiec, 2006; Mysliwiec et al., 2004).
3. Man Quang Ha and et al./Journal of Mining and Earth Sciences 62(3), 29 - 36 31 All primary statistical analyses, i.e., Histogram of are internally consistent and predictably different porosity (Figure 2a), permeability (Figure 2b), FZI from the properties of other reservoir volumes. He (Figure 2c), and cross plot of permeability versus described the flow units as the following: porosity for 570 cores data (Figure 2d) were - A specific volume of a reservoir; it is performed on the full data set. Most of the samples composed of one or more reservoir - quality were obtained in deltaic sandy - muddy - shaly lithology and any none - reservoir - quality rock deltaic facies types within that same volume, as well as the fluids they contain, 3. Methodology - A correlative and mappable unit at the interwell scale, 3.1. Hydraulic Flow Unit - HFU - A recognizable section on wireline logs, The concept of hydraulic flow unit was - A unit is being in communication with other introduced by Ebanks et al. (1987, 1992), who flow units. However, flow units based on defined an HFU as a mappable portion of a lithostratigraphic characteristics are not always in reservoir within which the geological and pressure communication (Figure 3). petrophysical properties that affect the fluid flow Figure. 2. Histogram of porosity (a), permeability (b), FZI (c), and cross plot of log permeability versus porosity for 570 cores data (d).
4. 32 Man Quang Ha and et al./Journal of Mining and Earth Sciences 62(3), 29 - 36   e (2) z 1  e k RQI 0.0314 (3)  e 1 RQI FZI (4) F S  s gv z Figure 3. Various parameters are used in defining  3 geologic flow units; the flow units are defined based K 1014.24(FZI)2 e (5) (1  )2 on lithofacies, pore types, porosity, and permeability e cross - plots, capillary pressure measurements, and On the basis of Equations (Equation 1÷4), we gamma - ray log response (after Ebanks et al. 1992). assume that units of constant FZI have invariable reservoir parameters that differ from the The structure, texture, and mineral surrounding neighbourhood. Proper division of composition of rock formation strongly influence the data set into units of constant FZI forms the the relationship between porosity and basis for the HFU construction, resulting in the best permeability. Petrophysicists working for the oil partial relationships of permeability vs porosity for and gas industry and prospecting hydrogeology each HFU. and geothermal water reserves prospecting try to find the best relationship between those two 3.2. Global Hydraulic Elements - GHE reservoir parameters since the times of Kozeny Corbett et al. (2003, 2004) proposed the rapid (1927) and Carman (1937). A breakthrough was and more straightforward approach to plot the noted with an approach based on the FZI proposed porosity and permeability data on the by Amaefule et al. (1993), which was then followed predetermined global hydraulic elements (GHE) by other authors (Prasad, 2000). FZI is a derivative template ( Figure 4), which is constructed based on factor determined based on the generalized eq. (5). A systematic series of a priori FZI values Cozeny - Carman equation: were arbitrarily chosen to define 10 porosity - 3  e 1 permeability elements. Only ten were chosen to k 2 2 2 (1) split the wide range of porosity and permeability 1-e Fs S gv parameter space into a manageable number of where: K - the permeability; Φe - the effective GHEs (Table 1). porosity; Fs - the shape factor; τ - the tortuosity of Data in the study projected on the Corbett and pores; Sgv - the specific surface. Potter (2004) template shows the close Amaefule et al. (1993) introduced two relationship between permeability and porosity in auxiliary factors: Φz, the normalized porosity each HFU = GHE. Thus, established equations are (Equation 2), and RQI, the reservoir quality index used to calculate K from Φ and FZI. The (Equation 3). This results in a new formula relationship between permeability from the core (Equation 4), which is a definition of FZI. data and permeability calculated from the means of The basis of HFU classification is to identify FZIs in GHE is very close (Figure 4). groups of data that form the unit - slope straight lines on a log - log plot of RQI versus z. The 4. Results and discussions permeability of a sample point is then calculated Probability function to select number of HFU from a pertinent HFU using the mean FZI value and the corresponding sample porosity using the To confirm the division of the data set into the following Equation (5).
5. Man Quang Ha and et al./Journal of Mining and Earth Sciences 62(3), 29 - 36 33 Table 1. Global hydraulic elements (GHE) template parameters (Corbett et al. 2003). GHE GHE1 GHE2 GHE3 GHE4 GHE5 GHE6 GHE7 GHE8 GHE9 GHE10 FZI 0.0938 0.1875 0.375 0.75 1.5 3 6 12 24 48 10000000 1000000 100000 10000 1000 y[mD] 100 10 1 Permeabilit 0.1 0.01 0.001 0 0.1 0.2 0.3 0.4 0.5 Porosity [dec.] Figure 4. Global Hydraulic Element “basemap” template showing GHE1 to GHE10 (Corbett et al. 2003). proper number of HFUs the probability function of the HFU for all core data. The curves represent the log(FZI) is calculated. A normal probability plot porosity - permeability relationship based on illustrates how the local slope changes according to Equation 5 using the mean value of FZI for each selected groups with a constant FZI. In Figure 5.a, hydraulic unit. six straight lines connecting the selected sections of Simple statistics of permeability, porosity, and the probability plot determined six uniforms HFUs. FZI show that the separate uniform groups are unambiguously described by the mean value of FZI Clustering the core data (Table 2). For these six defined groups of data, each Hierarchical cluster analysis is also applied to with homogeneous HFU of constant reservoir agglomerate and differentiate the data (Davis, parameters, we calculated the equations relating 1973). Elements belonging together in the group FZI to the permeability and porosity using core are as similar as possible, and groups are as data. Finally, the permeability that was calculated different as possible from others. Based on Ward’s based on Equation 5 with mean values of FZI for algorithm, the data set of the FZI and HFU is divided each HFU was highly correlated to the core origin into 6 clusters. The three dashed lines show the permeability (Table 2 and Figure 8). possible cutoffs for the proposed divisions into 8, 6, The core porosity and permeability data from 4, and 3 groups. We decided to use six groups (the the Z gas field were projected on the appropriate red line in Figure 5.b). GHE template constructed for each HFU ( Figure 9). Because mean FZI values are not calculated It was observed that the data will fit in the from the probability plots or Ward’s HFU prediction processing model. In each HFU/GHE classification algorithm, a plot of z vs RQI for each pair, the close relationship between permeability HFU was constructed (Figure 6). The unit slope and porosity was established, and those equations lines were drawn for each HFU through their data were used to calculate K from Φ. Figure 10 shows a clusters according to the mean value of FZI comparison between permeability from the core calculated for each HFU at the intercept with z = data and permeability calculated from the 7 GHEs 1. The mean FZI values were then used to construct correspondings to 7 FZIs on Table 1. the porosity - permeability relationship within The GHE results gave approximately the same each HFU using Equation 5. Figure 7 shows the number of GHEs as the HFU. It was therefore useful porosity-permeability cross-plot combined with to compare the previous conventional approach
6. 34 Man Quang Ha and et al./Journal of Mining and Earth Sciences 62(3), 29 - 36 Figure 5. a) Normal probability plot of log(FZI) with division into 6 homogeneous groups of HFU with constant FZI; b) Dendogram of the FZI set into six groups, according to the Ward’s algorithm. 10 1 RQI HFU6 0.1 HFU5 HFU4 HFU3 HFU2 HFU1 0.01 0.01 0.1 PhiZ 1 Figure 6. z vs. RQI cross-plot of all the hydraulic units. The mean FZI values for each hydraulic unit are given by the intercept of the straight lines at z =1. Table 2. Simple statistics of permeability (K), porosity (Ф), FZI, and the determination coefficients (R2) for the permeability, calculated from the 6 FZI_mean and 6 HFU. R2 Nr. of data K (mD) PHI (%) FZI HUs (k_FZI_mean in HU min mean max min mean max min mean max vs. k_core) HU1 28 0.02 0.72 2.82 0.07 0.16 0.233 0.095 0.283 0.400 0.728 HU2 58 0.17 9.15 24.33 0.078 0.21 0.251 0.466 0.734 0.971 0.888 HU3 89 9.78 50.75 120.04 0.15 0.24 0.292 0.997 1.379 1.687 0.645 HU4 117 40.470 144.72 358.55 0.203 0.257 0.315 1.733 2.10 2.563 0.743 HU5 214 79.79 445.77 1461.7 0.189 0.26 0.32 2.587 3.51 4.512 0.603 HU6 64 430.07 1458.96 3631.1 0.229 0.27 0.306 4.555 5.85 8.833 0.411 All 0.97 (Figure 7) with the GHE approach (Figure 9) to probably appropriate. In the future, GHEs appear show that GHEs are a useful concept, and the to provide an easy, rapid way of classifying core number of arbitrary GHEs on the template is data.
7. Man Quang Ha and et al./Journal of Mining and Earth Sciences 62(3), 29 - 36 35 10000 1000 100 10 K [mD] HU1 1 HU2 HU3 0.1 HU4 HU5 HU6 0.01 0.05 0.1 0.15 0.2 0.25 0.3 0.35 PHI [fraction] Figure 10. Dispersion plot and correlation line Figure 7. Dispersion plot of Ф_core vs. K_core, and between the core permeability (K_core) vs. the the six HFU defined in the area of core origin data. permeability calculated (K_GHE) from the 7 GHEs. Conclusions 10000 The hydraulic flow unit technique has been 1000 developed and applied to identify the reservoir y = 1.33x0.95 100 characteristics. This technique has a wide variety of R2 = 0.97 practical field applications to both cored and 10 uncored intervals/wells. In the study, the Z gas HU1 reservoirs were classified into 6 HFUs based on K_pre[mD] 1 HU2 HU3 570 core plugs data by applying conventional HU4 0.1 cluster analysis techniques as probability plot and HU5 Ward’s algorithm. The calculated permeability HU6 0.01 using the 6 HFUs classification shows very good 0.01 0.1 1 10 100 1000 10000 results. The determination coefficient R2 between K_core [mD] the calculated permeability with the HFU method Figure 8. Dispersion plot and correlation line and the actual permeability measured on core between the core origin permeability (K_core) vs. plugs was 0.97, indicates a nearly perfect the permeability calculated (K_pre) from the mean correlation. values of FZI for HFU. Applying the GHE method, the Z gas reservoirs can be divided into 7 distinct GHEs. Estimated permeability using the GHE method has a slightly smaller correlation coefficient than using the HFU method, 0.96 compared with 0.97. However, the GHE method is very useful for a reservoir with limited core plugs data and very quickly to divide reservoirs into HFUs. In fact, using this method, we can reduce the amount of core data taken from the reservoir and still provide acceptably accurate results. Nomenclature (selected quantities) Ф: Porosity. Figure 9. Displaying permeability vs. porosity core Фe: Effective porosity. data on the background of 10 GHE shows that the Z Фz: Normalized porosity. gas reservoirs can divide to 7 GHE (range from K: Permeability. GHE1 to GHE7). : Tortuosity.
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