Estimation of Geophysical Strata Rating (GSR) from Seismic Data in Carbonate Reservoirs by Using Intelligent Systems: an Example from the South Pars Gas Fiel

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FARAJI MOHAMMAD ALI; RAHIMPOUR BONAB HOSSAIN; KADKHODAIE ALI

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Journal: PETROLEUM RESEARCH Year:2017 | Volume:27 | Issue:92 Page(s): 27-43

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Title

Estimation of Geophysical Strata Rating (GSR) from Seismic Data in Carbonate Reservoirs by Using Intelligent Systems: an Example from the South Pars Gas Fiel

Author(s)

FARAJI MOHAMMAD ALI | RAHIMPOUR BONAB HOSSAIN | KADKHODAIE ALI | Issue Writer Certificate

Keywords

GSRQ1

Well LogsQ1

Wellbore StabilityQ2

Multi-Regression AnalysisQ2

Probabilistic Neural NetworkQ1

3D modelQ1

Abstract

The Geophysical Strata Rating (GSR), which is introduced in this study for carbonate reservoirs, is an empirical strength rating of rocks. It provides ratings from 10 to 100 where the lower values correspond to rocks; such as, shales which are weak from a borehole stability point of view and also the porous, permeable reservoir rocks. In comparison, the higher values of GSR are associated with intact rocks with few defects in the form of fractures and discontinuities and low porosity. In this study, GSR is calculated from petrophysical data using the equations developed in clastic rocks. The region investigated is the South Pars gas field where the Permo-Triassic Dalan and Kangan reservoirs host the largest accumulations of gas in the world. The GSR results are in good agreement with porosity and elastic moduli of these carbonate rocks. Discrimination between reservoir and non-reservoir shaly units would easily be obtained by comparing GSR and Well Logs. Very low GSR values, with high gamma ray log responses, indicate the shaly intervals. These can cause washouts, casing collapse and other related drilling problems. Intervals with low GSR values and low gamma ray log responses indicate the presence of good reservoir units. Finally, a 3D GSR model is estimated from 3D post-stack seismic data of the South Pars gas field, by using a Probabilistic Neural Network model. Strong correlations between neural network predictions and actual GSR data at unseen borehole locations proved the validity of the intelligent model in GSR estimation. This 3D GSR cube can be utilized for construction of geomechanical models over the South Pars gas field.

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APA: Copy

FARAJI, MOHAMMAD ALI, RAHIMPOUR BONAB, HOSSAIN, & KADKHODAIE, ALI. (2017). Estimation of Geophysical Strata Rating (GSR) from Seismic Data in Carbonate Reservoirs by Using Intelligent Systems: an Example from the South Pars Gas Fiel. PETROLEUM RESEARCH, 27(92 ), 27-43. SID. https://sid.ir/paper/114625/en

Vancouver: Copy

FARAJI MOHAMMAD ALI, RAHIMPOUR BONAB HOSSAIN, KADKHODAIE ALI. Estimation of Geophysical Strata Rating (GSR) from Seismic Data in Carbonate Reservoirs by Using Intelligent Systems: an Example from the South Pars Gas Fiel. PETROLEUM RESEARCH[Internet]. 2017;27(92 ):27-43. Available from: https://sid.ir/paper/114625/en

IEEE: Copy

MOHAMMAD ALI FARAJI, HOSSAIN RAHIMPOUR BONAB, and ALI KADKHODAIE, “Estimation of Geophysical Strata Rating (GSR) from Seismic Data in Carbonate Reservoirs by Using Intelligent Systems: an Example from the South Pars Gas Fiel,” PETROLEUM RESEARCH, vol. 27, no. 92 , pp. 27–43, 2017, [Online]. Available: https://sid.ir/paper/114625/en

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