Consistent porosity–permeability modeling, reservoir rock typing and hydraulic flow unitization in a giant carbonate reservoir

Ghadami, Nader; Rasaei, Mohammad Reza; Hejri, Shahab; Sajedian, Ali; Afsari, Khalil

doi:10.1016/j.petrol.2015.04.017

Cited by 49 publications

(15 citation statements)

References 24 publications

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“…Although successful application of the FZI method in carbonate rock typing has been reported in many studies (e.g. Ghadami et al, 2015, Aliakbardoust and Rahimpour, 2013and Chandra et al, 2015, it was used in this study in combination with other data and not in isolation. Pore throat size distribution (diameter) calculated from available MICP data was directly used in the rock typing study after quality checking.…”

Section: Methodsmentioning

confidence: 99%

“…Rock typing was subsequently described in several papers (e.g. Davies et al, 1991;Porras, et al, 1999;Porras and Campus, 2001;Boada, 2001;Soto, 2001;Marquez et al, 2001;Lee, et al, 2002;Al-Habshi et al, 2003;Ali-Nandal and Gunter, 2003;Perez et al, 2003;Al-Farisi et al, 2004;Ohen et al, 2004;Shenawi et al, 2007, Slatt, 2007, Nouri-Taleghani et al, 2015and Ghadami et al, 2015. Various geological (depositional environment, texture, grain size and sorting), petrophysical (porosity, permeability, flow zone indicator and water saturation) and reservoir (pore throat size, capillary pressure curves and wettability) data have been used in these studies to define rock types.…”

Section: Introductionmentioning

confidence: 99%

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The effect of rock type on natural water flooding and residual oil saturation below free water level and oil water contact: A case study from the Middle East

Heydari-Farsani

Neilson

Alsop

et al. 2020

Journal of Petroleum Science and Engineering

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The presence of residual oil below the present day free water level (FWL) and oil water contact (OWC) is common in many fields of the Middle East. This residual oil is seen in reservoirs prior to the start of production.A rock typing study was carried out to investigate the effect of rock types on the presence and distribution of residual oil below the free water level (FWL) and oil water contact (OWC) and potential recovery from ROZ's following water flooding.The rock typing study was carried out by integration of different data sources i.e. core description, thin section study, conventional core analysis (CCAL), special core analysis (SCAL) and petrophysical logs. Six major rock types with unique reservoir quality and behaviour were classified in cored wells and subsequently predicted in un-cored wells.Reservoir quality increases from rock type 1 to 6.The residual oil saturation (Sor) of each rock type was defined and it was concluded that all rock types except for rock type 1 contain residual oil below the present day FWL and OWC.The absence of residual oil in rock type 1 was related to the fact that it is unlikely that oil migration into it ever occurred and consequently no residual oil is seen below the present day FWL and OWC. It was also revealed that rock type 6, with better porosity and permeability, 2 includes the highest residual oil saturation. This was attributed to its bimodal pore throat size distribution and the effect of this on water flooding and hence potential recovery.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The effect of rock type on natural water flooding and residual oil saturation below free water level and oil water contact: A case study from the Middle East

Heydari-Farsani

Neilson

Alsop

et al. 2020

Journal of Petroleum Science and Engineering

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“…Amaefule et al (1993), and Guo et al (2007) are among researchers who made use of the flow zone indicator (FZI) to delineate various rock types. A discrete rock type (DRT) is defined using the flow zone indicator (FZI), as follows: (Guo et al 2007;Ghadami et al, 2015):…”

Section: Introductionmentioning

confidence: 99%

Predicting the flow zone indicator of carbonate reservoirs using NMR echo transforms, and routine open-hole log measurements: Insights from a field case study spanning extreme micro-structure properties

Al-Dousari

Almudhhi²,

Garrouch³

2021

JER is an international, peer-reviewed journal that publishes f

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Predicting the flow zone indicator is essential for identifying the hydraulic flow units of hydrocarbon reservoirs. Delineation of hydraulic flow units is crucial for mapping petrophysical and rock mechanical properties. Precise prediction of the flow zone indicator (FZI) of carbonate rocks using well log measurements in un-cored intervals is still a daunting challenge for petrophysicists. This study presents a data mining methodology for predicting the rock FZI using NMR echo transforms, and conventional open-hole log measurements. The methodology is applied on a carbonate reservoir with extreme microstructure properties, from an oil “M” field characterized by a relatively high-permeability with a median of approximately 167 mD, and a maximum of 3480 mD. The reservoir from the M field features detritic, or vuggy structure, covering a wide range of rock fabrics varying from microcrystalline mudstones to coarse-grained grainstones. Porosity has a median of approximately 22%. Dimensional analysis and regression analysis are applied for the derivation of four transforms that appear to capture approximately 80% of the FZI variance. These four transforms are formulated using the geometric mean of the transverse NMR relaxation time (T2lm), the ratio of the free fluid index (FFI) to the bulk volume irreducible (BVI), the bulk density, the sonic compressional travel time, the true resistivity, the photo-electric absorption, and the effective porosity. Non-linear regression models have been developed for predicting the FZI using the derived transforms, for the carbonate reservoir from the M field. The average relative error for the estimated FZI values is approximately 52%. The same transforms are used as input for training a developed general regression neural network (GRNN), built for the purpose of predicting rock FZI. The constructed GRNN predicts FZI with a notable precision. The average absolute relative error on FZI for the training set is approximately 3.1%. The average absolute relative error on FZI for the blind testing set is approximately 22.0 %. The data mining approach presented in this study appears to suggest that (i) the relationship between the flow zone indicator and open-hole log attributes is highly non-linear, (ii) the FZI is highly affected by parameters that reflect rock texture, rock micro-structure geometry, and diagenetic alterations, and (iii) the derived transforms provide a means for further enhancement of the flow zone indicator prediction in carbonate reservoirs.

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“…To better clarify the effects of PSD on the flow behavior of a reservoir rock, it is important to collect samples of similar rock types. Conventional reservoir rock typing has been defined as the classification of the reservoir rock based on its petrophysical properties acquired through wireline logs, porosity-permeability correlations, mercury injection curves, and geological features [ 5 , 16 , 17 , 18 , 19 , 20 ]. As a basis for reservoir rock classification, the pore geometry can be assessed based on the capillary pressure, which, in turn, can be better measured by the mercury injection analysis rather than other methods [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

Insights into the Effects of Pore Size Distribution on the Flowing Behavior of Carbonate Rocks: Linking a Nano-Based Enhanced Oil Recovery Method to Rock Typing

et al. 2020

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As a fixed reservoir rock property, pore throat size distribution (PSD) is known to affect the distribution of reservoir fluid saturation strongly. This study aims to investigate the relations between the PSD and the oil–water relative permeabilities of reservoir rock with a focus on the efficiency of surfactant–nanofluid flooding as an enhanced oil recovery (EOR) technique. For this purpose, mercury injection capillary pressure (MICP) tests were conducted on two core plugs with similar rock types (in respect to their flow zone index (FZI) values), which were selected among more than 20 core plugs, to examine the effectiveness of a surfactant–nanoparticle EOR method for reducing the amount of oil left behind after secondary core flooding experiments. Thus, interfacial tension (IFT) and contact angle measurements were carried out to determine the optimum concentrations of an anionic surfactant and silica nanoparticles (NPs) for core flooding experiments. Results of relative permeability tests showed that the PSDs could significantly affect the endpoints of the relative permeability curves, and a large amount of unswept oil could be recovered by flooding a mixture of the alpha olefin sulfonate (AOS) surfactant + silica NPs as an EOR solution. Results of core flooding tests indicated that the injection of AOS + NPs solution in tertiary mode could increase the post-water flooding oil recovery by up to 2.5% and 8.6% for the carbonate core plugs with homogeneous and heterogeneous PSDs, respectively.

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Consistent porosity–permeability modeling, reservoir rock typing and hydraulic flow unitization in a giant carbonate reservoir

Cited by 49 publications

References 24 publications

The effect of rock type on natural water flooding and residual oil saturation below free water level and oil water contact: A case study from the Middle East

The effect of rock type on natural water flooding and residual oil saturation below free water level and oil water contact: A case study from the Middle East

Predicting the flow zone indicator of carbonate reservoirs using NMR echo transforms, and routine open-hole log measurements: Insights from a field case study spanning extreme micro-structure properties

Insights into the Effects of Pore Size Distribution on the Flowing Behavior of Carbonate Rocks: Linking a Nano-Based Enhanced Oil Recovery Method to Rock Typing

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