Field Application of a Modified Kozeny-Carmen Correlation to Characterize Hydraulic Flow Units

Nooruddin, Hasan A.; Hossain, M. Enamul; Sudirman, Sharizan B.; Sulaimani, Thamer

doi:10.2118/149047-ms

Cited by 11 publications

(4 citation statements)

References 13 publications

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“…[ 5–10 ] The hydraulic flow units (HFU) method, introduced by Amaefule et al, [ 6 ] is one of the most widely used methods to define flow units at the reservoir scale using core plug petrophysical data. [ 11–14 ] HFU are defined based on flow zone indicator (FZI) and rock quality index (RQI) values, which are derived from the Kozeny–Carman equation, for a sample with porosity (∅) and permeability ( K ), and expressed as the equations below

$$\text{FZI} = \frac{0.0314 \text{\ast } \sqrt{} \left(\right. \frac{K}{&amp;#x00026;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;empty;} \left.\right)}{\frac{&amp;#x00026;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;empty;}{\left(\right.

…”

Section: Introductionmentioning

confidence: 99%

Pore to Core Scale Characterization of Hydraulic Flow Units Using Petrophysical and Digital Rock Analyses

Chandra,

Tallec,

Finkbeiner

et al. 2023

Energy Tech

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This article illustrates an integrated method for characterizing hydraulic flow units in subsurface reservoirs using multiscale images and petrophysical data analysis from well cores. This method is applied to 35 meters of cores drilled from a Late Jurassic Upper Jubayla Formation outcrop in Saudi Arabia, which is analogous to the lower section of the Arab‐D reservoir. Scanning electron microscope (SEM) imagery, thin‐section petrography, and X‐ray computed tomography (CT) images of whole cores and core plugs are used to visualize and characterize these carbonate rocks across multiple length scales. Core plug porosity and permeability data are used for petrophysical characterization and deriving hydraulic flow units (HFU). The multimodal pore types associated with the HFUs and their connectivity using thin section and SEM image analysis, combined with mercury injection porosimetry are used. Whole‐core CT textures and heterogeneity logs are correlated with HFU and digital image analysis of core plugs. Whole‐core CT data prove to be a highly valuable tool for bridging the scale gap between well data and reservoir models. This methodology can be applied to datasets from a large number of wells, especially when combined with machine learning tools, allowing improved characterization of complex carbonate reservoirs.

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$$\text{FZI} = \frac{0.0314 \text{\ast } \sqrt{} \left(\right. \frac{K}{&amp;#x00026;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;empty;} \left.\right)}{\frac{&amp;#x00026;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;empty;}{\left(\right.

…”

Section: Introductionmentioning

confidence: 99%

Pore to Core Scale Characterization of Hydraulic Flow Units Using Petrophysical and Digital Rock Analyses

Chandra,

Tallec,

Finkbeiner

et al. 2023

Energy Tech

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“…Petro-physically speaking, the rock unit is that volume of the rock that represents similar petro-physical reaction in the well profile (Amaefule et al 1993;Noorddin et al 2011). Different rock types are identified by integrating petro-physical data with core description information using multi-variate statistical methods (Askari and Behrouz 2011).…”

Section: Introductionmentioning

confidence: 99%

“…In sandstone reservoirs, a hydraulic flow unit can generally be considered as a part of the reservoir where specific petro-physical and geological properties are the same and, therefore, capillary pressure and relative permeability are similar. (Noorddin et al 2011;Svirsky et al 2004).…”

Section: Introductionmentioning

confidence: 99%

Capillary-based method for rock typing in transition zone of carbonate reservoirs

Dakhelpour-Ghoveifel

Shegeftfard

Dejam

2018

J Petrol Explor Prod Technol

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Reservoir simulation is established as a good practice to make the best decision for a petroleum reservoir. The reservoir is characterized in terms of reservoir elements such as structural model, well data, rock and fluid properties. Then the reservoir model is enhanced through history matching and finally different prediction scenarios are tried to find the best plan for the reservoir understudy. The more accurate the reservoir is characterized, the faster and the more precisely the history match is finished and the more reliable predictions are obtained. The most important part of reservoir characterization is the rock typing, where the quality of CCAL (conventional core analysis) and SCAL (special core analysis) properties are evaluated and estimated for any simulation grid. The resulting oil in place must be confirmed by the OOIP (original oil in place) calculated based on average petro-physical parameters for any layer. To allocate different rock types to simulation grid, rock types should be assigned according to different ranges of rock differentiation parameter which has to be determined in any specific study. Based on our experience in Iranian carbonate reservoirs, most frequently irreducible water saturation is the rock differentiation parameter. In the oil zone, water saturation from log data is assumed to be the irreducible water saturation. Thus, the rock type is identified with no trouble. The problem arises in transition zone, where water saturation from log data is not equal to the irreducible water saturation of that rock. This study includes the observed variations in terms of water saturation data versus depth and how to assign rock types to the transition zone grids. The objective of the capillarybased method is to produce a water saturation map which honors laboratory data as well as the well log data and considers the depth so that it can handle the transition zone in a proper manner. In fact, novelty of this work is to explain how it is possible to consider log and capillary pressure data together so that the most accurate rock type is assigned to reservoir grids of the transition zone. Moreover, this method is consistent with equilibration method for initializing reservoir simulations. A procedure is presented for how to implement the capillary-based method in a stepwise manner. Once the proposed method is carried out, the initialized simulation model is consistent with all sources of data (core analysis and petro-physical data). In this procedure original oil in place calculated after the initialization for simulation is more accurate and can be crosschecked with volumetric calculation based on interpreted log data. Therefore, it is considered to facilitate subsequent stages of reservoir study, namely history match and prediction.

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“…The formulation for geological zonation is described in the next section, based firstly on the Carman-Kozeny equation [3] [4] [5], and subsequently this formulation was extended by Nooruddin et al [6], to be able to handle more diverse geological formations.…”

Section: Introductionmentioning

confidence: 99%

Defining Reservoir Quality Relationships: How Important Are Overburden and Klinkenberg Corrections?

Hoang¹,

Behrenbruch²,

Huu³

2017

GEP

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Reservoir quality from cored intervals has traditionally been described by grouping similar intervals according to rock type. The main shortcoming of this static modelling approach is that it lacks clarity and it is not conducive for setting up a dynamic simulation model. The alternative is to use a modelling approach based on Hydraulic Flow Zone Units (HFZUs). First proposed in the late 1980s and extensively published in the early 1990s such formulation uses the well-known Carman-Kozeny (C-K) equation. More recently, this approach has been extended to cover a wider range of geological formations with diverse pore structure types. In using a HFZU approach, a preprocessing step is customarily undertaken to first overburden correct the data and where necessary also to correct for the Klinkenberg effect (lower permeability formations, lab testing with gas). The study presented compares corrected and uncorrected data sets, to see if correction alters the overall outcome of HFZU analysis. Specifically, data sets are compared at three different conditions: ambient, overburden (only) corrected and finally data that has been corrected for both, overburden and Klinkenberg effects. In all cases it is the Flow Zone Indicator (FZI), an index representative of formation quality that is tracked, together with the type of relationship. Several comparative analysis examples are given for diverse formations. The results show that uncorrected data can yield a different correlation and FZI, especially for intervals that include low permeability samples. Results indicate that Overburden and Klinkenberg corrections should be applied before HFZU analysis.

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Field Application of a Modified Kozeny-Carmen Correlation to Characterize Hydraulic Flow Units

Cited by 11 publications

References 13 publications

Pore to Core Scale Characterization of Hydraulic Flow Units Using Petrophysical and Digital Rock Analyses

Pore to Core Scale Characterization of Hydraulic Flow Units Using Petrophysical and Digital Rock Analyses

Capillary-based method for rock typing in transition zone of carbonate reservoirs

Defining Reservoir Quality Relationships: How Important Are Overburden and Klinkenberg Corrections?

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