Permeability and Water Saturation Distribution by Lithologic Facies and Hydraulic Units: A Reservoir Simulation Case Study

Shenawi, Shamsuddin; White, Jerry P.; Elrafie, Emad A.; El-Kilany, Khaled Ahmed

doi:10.2118/105273-ms

Cited by 49 publications

(15 citation statements)

References 3 publications

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“…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.…”

Section: Reservoir Rock Typingmentioning

confidence: 99%

Effect of overburden pressure on determination of reservoir rock types using RQI/FZI, FZI* and Winland methods in carbonate rocks

2019

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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.

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Section: Reservoir Rock Typingmentioning

confidence: 99%

Effect of overburden pressure on determination of reservoir rock types using RQI/FZI, FZI* and Winland methods in carbonate rocks

2019

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“…1) with a total coverage area of 7500 km 2 by 2500 km 2 (Abdulrahman et al 2014;Fui 1978;Rijks 1981). The province is extensively faulted by a major northeastern heading fault zone known as the West Baram Line that forms the western margin of the province (Tan et al 1999).…”

Section: Regional Geology and Tectonic Setting Of The Sarawak Basinmentioning

confidence: 99%

“…FZI has also been derived from rock texture and pore geometry attributes to pore volume, pore throat size, length, connectivity and tortuosity (Amaefule et al 1993) with permeability estimated from uncored well using the Kozeny-Carman equation (Davies and Vessell 1996;Shenawi et al 2007).…”

Section: Introductionmentioning

confidence: 99%

Impact of rock fabric on flow unit characteristics in selected reservoir sandstones from West Baram Delta Offshore, Sarawak

Yusuf

Padmanabhan

2019

J Petrol Explor Prod Technol

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The successful application of enhance oil recovery (EOR) technology requires an accurate understanding of the internal architecture of reservoirs and complex distribution of flow unit characteristics within different lithofacies. However, flow unit characteristics in the reservoir lithofacies of West Baram Delta Offshore, Sarawak, remain unexplored. Therefore, this paper investigates potential hydraulic flow units (HFUs) present within the five (5) identified sandstone lithofacies using thin sections, scanning electron microscopy (SEM), two-dimensional (2D) petrographic image analysis and helium porosimetery. The identified lithofacies include massive coarse-grained sandstone (MCGS), massive medium-grained sandstone (MMGS), massive fine-grained sandstone (MFGS), massive friable fine sandstone (MFFS) and massive very fine grained sandstone lithofacies (MVFGS). A plot of reservoir quality index (RQI) against normalized porosity (фz) showed five main hydraulic flow units (HFUs) within the identified lithofacies. The MFGS, MFFS and MVFGS lithofacies exhibited moderate to high matrix fractions and moderate to poor sorting, suggesting high tortuosity and hence characterized by low flow zone indicator (FZI). On the other hand, the MCGS and MMGS lithofacies exhibit very low to low matrix fractions, medium to coarse grain sizes and moderate to well sorting, signifying lower tortuosity, and hence characterized by relatively higher flow zone indicator (FZI). Thus, a hydraulic flow unit (HFU) can be formed by more than one lithofacies that exhibit similar fluid characteristic attributes, regardless of the difference in depositional environment and diagenetic processes undergone. This study concludes that HFU scattered plot can serve as a qualitative model for the characterization of flow unit characteristics in a well-comprising different lithofacies, particularly for field development plans in the West Baram Delta. Keywords Rock fabric • Flow unit characteristic • Flow zone indicator (FZI) • Reservoir quality index (RQI) • West Baram Delta * I. Yusuf

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“…After calculating FZI values by Eq. (3), obtained continuous results should be converted to discrete ones using the following statistical formula (Shenawi et al, 2007):…”

Section: Flow Units Characterizationmentioning

confidence: 99%

Deriving Relative Permeability from Capillary Pressure Using Gaussian and Rational Equations

Shahvar¹,

Kharrat²,

Dashtbesh³

2014

Energy Sources, Part A: Recovery, Utilization, and Environmenta

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While dynamic data are necessary for a robust reservoir characterization, measuring these type of data in a laboratory is time consuming and very expensive. On the other hand, if dynamic data, especially relative permeability and capillary pressure, are available for discrete grids, they might lead to a more promising simulation model. In the following study, capillary pressure is predicted by artificial neural networks for distinct flow units. Then, two methods are introduced for estimating relative permeability: the first one is based on using Gaussian and rational equations for deriving relative permeability from capillary pressure data and the second one is by utilizing ANN.

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Permeability and Water Saturation Distribution by Lithologic Facies and Hydraulic Units: A Reservoir Simulation Case Study

Cited by 49 publications

References 3 publications

Effect of overburden pressure on determination of reservoir rock types using RQI/FZI, FZI* and Winland methods in carbonate rocks

Effect of overburden pressure on determination of reservoir rock types using RQI/FZI, FZI* and Winland methods in carbonate rocks

Impact of rock fabric on flow unit characteristics in selected reservoir sandstones from West Baram Delta Offshore, Sarawak

Deriving Relative Permeability from Capillary Pressure Using Gaussian and Rational Equations

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