An Integrated Method for Modeling Fluid Saturation Profiles and Characterising Geological Environments Using a Modified FZI Approach: Australian Fields Case Study

Biniwale, Shripad

doi:10.2118/99285-stu

Cited by 5 publications

(1 citation statement)

References 19 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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Flow unit modeling and fine-scale predicted permeability validation in Atokan sandstones: Norcan East field, Kansas

Bhattacharya¹,

Byrnes²,

Watney³

et al. 2008

Bulletin

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Applying Flow Zone Index Approach and Artificial Neural Networks Modeling Technique for Characterizing a Heterogeneous Carbonate Reservoir Using Dynamic Data: Case Study of an Iranian Reservoir

2010

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Although static characterization of reservoirs is an inevitable part of any reservoir studies, the most robust models of the reservoirs can be obtained through integrating static and dynamic data. The following study which is done in a heterogeneous carbonate reservoir utilizes the capillary pressure and relative permeability data to verify the task of static rock typing and investigate the role of hydraulic units in capillary pressure and relative permeability modeling. For this purpose, at first, various rock typing techniques are applied to the field data to seek the best method which has the most consistency with capillary pressure curves. Using Desouky method which is based on hydraulic unit concept, Leverett J-function was then correlated with the normalized water saturation for each rock type to normalize and estimate capillary pressure curves. An excellent agreement exists between the measured data and calculated ones approving the applicability of Desouky method in heterogeneous carbonate reservoirs. Then, using porosity, permeability, wetting phase saturation and some functional links as input data, artificial neural networks (ANN) and specifically the back propagation feed forward neural network is applied as a new approach to predict relative permeability. However, instead of constructing the model for the entire depth of the well, discrete models are developed for each hydraulic unit individually. This idea is generated from the fact that each hydraulic unit has its own set of relative permeability curves and each set differs from one unit to another so the task of separating the models can reduce the discrepancies of the data and thus providing the more accurate synthetic models for predicting relative permeability. Results obtained from this model are compared with those of applying multiple regressions and also ANN method for the entire depth of the well. This comparison showed that the new approach introduced in this study can enhance relative permeability prediction considerably.

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An Integrated Method for Modeling Fluid Saturation Profiles and Characterising Geological Environments Using a Modified FZI Approach: Australian Fields Case Study

Cited by 5 publications

References 19 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

Flow unit modeling and fine-scale predicted permeability validation in Atokan sandstones: Norcan East field, Kansas

Applying Flow Zone Index Approach and Artificial Neural Networks Modeling Technique for Characterizing a Heterogeneous Carbonate Reservoir Using Dynamic Data: Case Study of an Iranian Reservoir

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