IDENTIFICATION OF WELL LOGS WITH SIGNIFICANT IMPACT ON PREDICTION OF OIL AND GAS RESERVOIRS PERMEABILITY USING STATISTICAL ANALYSIS OF RSE VALUES

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BEYKI M.; RAHIMPOUR BONAB HOSSEIN; HOSSEINYAR G.R.; PIRAYEHGAR A.

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Journal: JOURNAL OF SCIENCE (UNIVERSITY OF TEHRAN) (JSUT) Year:2009 | Volume:35 | Issue:2 (SECTION: GEOLOGY) Page(s): 27-37

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Title

IDENTIFICATION OF WELL LOGS WITH SIGNIFICANT IMPACT ON PREDICTION OF OIL AND GAS RESERVOIRS PERMEABILITY USING STATISTICAL ANALYSIS OF RSE VALUES

Author(s)

BEYKI M. | RAHIMPOUR BONAB HOSSEIN | HOSSEINYAR G.R. | PIRAYEHGAR A. | Issue Writer Certificate

Keywords

PERMEABILITYQ3

BACK PROPAGATION NEURAL NETWORKQ3

RELATIVE STRENGTH OF EFFECT INDEXQ3

Abstract

PERMEABILITY is one of the very important features of the oil and gas reservoirs, and usually it could provided by core analysis and well test which are expensive. Geophysical logs are one of the best tools for identification of petrophysical properties of reservoir, such as porosity and PERMEABILITY. Most of the petrophysical parameters are directly related to a specific log. Whereas, PERMEABILITY doesn’t correlate with any specific logs. Sonic, density, neutron, specific resistivity and GR logs and also photoelectrical index are important logs and indices to determine the PERMEABILITY. It’s natural that all of the logs don't have identical effects on the PERMEABILITY. Thus, in order to determining the effects of each of them on PERMEABILITY values, we can use artificial neural networks. Also we can calculate factor as RSE by using artificial neural network ratios which show effects of each input parameters on the output ones. Via the study of RSE it can be concluded that each log has specific role in determination of PERMEABILITY .Thus, at first it’s better to pay attention to more effective logs. With this regard, after training the artificial neural networks, the data provided from the geophysical logs as input of network and the horizontal and vertical PERMEABILITY as the output; the weights of the most appropriate neural networks were saved.Then by using these relations, comparative effects of each of logs on the PERMEABILITY were indicated and RSE values were registered. These processes have been done several times and provided results have been calculated as Frequency of per cent of RSE value in the interval [-1, +1] and with 0.1 distance. Then bar diagrams have been drawn. With the Study of diagrams it could be shown that some of the logs such as NPHI, RHOB, DT and LLS have more effects on the determination of PERMEABILITY in reservoir rocks.

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APA: Copy

BEYKI, M., RAHIMPOUR BONAB, HOSSEIN, HOSSEINYAR, G.R., & PIRAYEHGAR, A.. (2009). IDENTIFICATION OF WELL LOGS WITH SIGNIFICANT IMPACT ON PREDICTION OF OIL AND GAS RESERVOIRS PERMEABILITY USING STATISTICAL ANALYSIS OF RSE VALUES. JOURNAL OF SCIENCE (UNIVERSITY OF TEHRAN) (JSUT), 35(2 (SECTION: GEOLOGY)), 27-37. SID. https://sid.ir/paper/2140/en

Vancouver: Copy

BEYKI M., RAHIMPOUR BONAB HOSSEIN, HOSSEINYAR G.R., PIRAYEHGAR A.. IDENTIFICATION OF WELL LOGS WITH SIGNIFICANT IMPACT ON PREDICTION OF OIL AND GAS RESERVOIRS PERMEABILITY USING STATISTICAL ANALYSIS OF RSE VALUES. JOURNAL OF SCIENCE (UNIVERSITY OF TEHRAN) (JSUT)[Internet]. 2009;35(2 (SECTION: GEOLOGY)):27-37. Available from: https://sid.ir/paper/2140/en

IEEE: Copy

M. BEYKI, HOSSEIN RAHIMPOUR BONAB, G.R. HOSSEINYAR, and A. PIRAYEHGAR, “IDENTIFICATION OF WELL LOGS WITH SIGNIFICANT IMPACT ON PREDICTION OF OIL AND GAS RESERVOIRS PERMEABILITY USING STATISTICAL ANALYSIS OF RSE VALUES,” JOURNAL OF SCIENCE (UNIVERSITY OF TEHRAN) (JSUT), vol. 35, no. 2 (SECTION: GEOLOGY), pp. 27–37, 2009, [Online]. Available: https://sid.ir/paper/2140/en

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