Calculation of fracture parameters and their effect on porosity and permeability using image logs and petrophysical data in carbonate Asmari reservoir, SW Iran

Aghli, Ghasem; Soleimani, Bahman; Tabatabai, Salman; Zahmatkesh, Iman

doi:10.1007/s12517-017-3047-4

Cited by 13 publications

(1 citation statement)

References 20 publications

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“…Image logs have become recognized as the most important borehole logging technique for fractured reservoir evaluation, and they have developed rapidly due to their excellent performance in characterizing borehole features such as fractures and bedding (Serra and Serra 2004a, b;Tingay et al 2008). Furthermore, the use of image logs has decreased overall costs by reducing coring intervals and by accurately determining the perforation zones (Aghli et al 2017). In the carbonate formations, fractures, even very fine ones, strongly influence permeability (Lavenu et al 2014) and understanding their distribution patterns can help to determine the best locations for drilling and maximizing production (Serra and Serra 2004a, b).…”

Section: Introductionmentioning

confidence: 99%

Reservoir heterogeneity and fracture parameter determination using electrical image logs and petrophysical data (a case study, carbonate Asmari Formation, Zagros Basin, SW Iran)

2019

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Assessment of reservoir and fracture parameters is necessary to optimize oil production, especially in heterogeneous reservoirs. Core and image logs are regarded as two of the best methods for this aim. However, due to core limitations, using image log is considered as the best method. This study aims to use electrical image logs in the carbonate Asmari Formation reservoir in Zagros Basin, SW Iran, in order to evaluate natural fractures, porosity system, permeability profile and heterogeneity index and accordingly compare the results with core and well data. The results indicated that the electrical image logs are reliable for evaluating fracture and reservoir parameters, when there is no core available for a well. Based on the results from formation micro-imager (FMI) and electrical micro-imager (EMI), Asmari was recognized as a completely fractured reservoir in studied field and the reservoir parameters are mainly controlled by fractures. Furthermore, core and image logs indicated that the secondary porosity varies from 0% to 10%. The permeability indicator indicates that zones 3 and 5 have higher permeability index. Image log permeability index shows a very reasonable permeability profile after scaling against core and modular dynamics tester mobility, mud loss and production index which vary between 1 and 1000 md. In addition, no relationship was observed between core porosity and permeability, while the permeability relied heavily on fracture aperture. Therefore, fracture aperture was considered as the most important parameter for the determination of permeability. Sudden changes were also observed at zones 1-1 and 5 in the permeability trend, due to the high fracture aperture. It can be concluded that the electrical image logs (FMI and EMI) are usable for evaluating both reservoir and fracture parameters in wells with no core data in the Zagros Basin, SW Iran.

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Section: Introductionmentioning

confidence: 99%

Reservoir heterogeneity and fracture parameter determination using electrical image logs and petrophysical data (a case study, carbonate Asmari Formation, Zagros Basin, SW Iran)

2019

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Multiple linear regression and gene expression programming to predict fracture density from conventional well logs of basement metamorphic rocks

Hasan,

M. Tóth

2024

J Petrol Explor Prod Technol

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Fracture identification and evaluation requires data from various resources, such as image logs, core samples, seismic data, and conventional well logs for a meaningful interpretation. However, several wells have some missing data; for instance, expensive cost run for image logs, cost concern for core samples, and occasionally unsuccessful core retrieving process. Thus, a majority of the current research is focused on predicting fracture based on conventional well log data. Interpreting fractures information is very important especially to develop reservoir model and to plan for drilling and field development. This study employed statistical methods such as multiple linear regression (MLR), principal component analysis (PCA), and gene expression programming (GEP) to predict fracture density from conventional well log data. This study explored three wells from a basement metamorphic rock with ten conventional logs of gamma rays, thorium, potassium, uranium, deep resistivity, flushed zone resistivity, bulk density, neutron porosity, sonic porosity, and photoelectric effect. Four different methods were used to predict the fracture density, and the results show that predicting fracture density is possible using MLR, PCA, and GEP. However, GEP predicted the best fracture density with R2 > 0.86 for all investigated wells, although it had limited use in predicting fracture density. All methods used highlighted that flushed zone resistivity and uranium content are the two most significant well log parameters to predict fracture density. GEP was efficient for use in metamorphic rocks as it works well for conventional well log data as the data is nonlinear, and GEP uses nonlinear algorithms.

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Fractures Analysis Using Core Data and Image Logs: A Case Study in the Dalan–Kangan Reservoir of South Pars Gas Field, Iran

Mohsenipour

Zahmatkesh

Soleimani

2022

Iran J Sci Technol Trans Sci

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Calculation of fracture parameters and their effect on porosity and permeability using image logs and petrophysical data in carbonate Asmari reservoir, SW Iran

Cited by 13 publications

References 20 publications

Reservoir heterogeneity and fracture parameter determination using electrical image logs and petrophysical data (a case study, carbonate Asmari Formation, Zagros Basin, SW Iran)

Reservoir heterogeneity and fracture parameter determination using electrical image logs and petrophysical data (a case study, carbonate Asmari Formation, Zagros Basin, SW Iran)

Multiple linear regression and gene expression programming to predict fracture density from conventional well logs of basement metamorphic rocks

Fractures Analysis Using Core Data and Image Logs: A Case Study in the Dalan–Kangan Reservoir of South Pars Gas Field, Iran

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