Cementation factor and carbonate formation properties correlation from well logs data for nasiriya field
The cementation factor has specific effects on petrophysical and elastic properties of porous media. A comprehensive investigation of carbonate rock properties which have an interlock with the cementation factor was done through core analysis and well log data. Five wells in Nasiriya oilfield, which...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2016
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/77938/1/FadhilSarhanKadhimPFChE2016.pdf |
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| Summary: | The cementation factor has specific effects on petrophysical and elastic properties of porous media. A comprehensive investigation of carbonate rock properties which have an interlock with the cementation factor was done through core analysis and well log data. Five wells in Nasiriya oilfield, which is one of the giant fields consists of the carbonate reservoirs in the Middle East were used in this study. The study was made across the Mishrif and Yamamma carbonate formations in the Nasiriya oilfield. Neurolog software (V5, 2008) was used to digitize the scanned copies of available logs while Interactive Petrophysics software (IP V3.5, 2008) was used to determine the properties of studied formations. The average cementation factor values were calculated from the F-PHI plot and Gomez methods and compared with Pickett method. Petrophysical and dynamic elastic properties were determined from well logs. In this study, a new approach was introduced to obtain correlations of cementation factor to petrophysical and dynamic elastic properties of Mishrif and Yamamma formations. An artificial neural network platform was used to determine these correlations depending on the determined properties of studied formations. The neural network model used two different training algorithms; Gradient Descent with Momentum and Levenberg–Marquardt. Results show that the plot of average core data and calculated data from IP software of porosity and permeability gave a good correlations coefficient of R2 = 0.86034 to 0.94303. Generally, cementation factor values obtained from all methods are found to be less than two. In addition, cementation factor values also increased with increasing depth of the studied formations. An efficient performance and excellent prediction of cementation factor have been obtained with less than 10-4 and 10-8 mean square error from both artificial neural network models. Three saturation models were used to estimate water saturation of carbonate formations, which are simple Archie equation, dual water model and Indonesian model. The Indonesian water saturation model recorded the lowest percentage error in comparison with water saturation of core samples, and the water saturation in Yamamma formation was higher than in the Mishrif formation. The accurate determination of a cementation factor gives reliable saturation results. |
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