Journal of Geophysics and Engineering · 2016 · 34 citations · 55 references
EngineeringEarth SciencesGeologyReservoir CharacterizationReservoir EngineeringRock MechanicsCore AnalysisFracture CharacterizationFracture PropagationPorosity AnalysisRock FractureRock PropertiesAnhydrite DepositsCrystal MorphologyCrystal Size AnalysisDiagenetic Controls On Rock TypesFracture AnalysisFracture Intensity QuantificationInteraction MatrixPost-depositional Alteration
Several studies have been carried out to understand and justify the influences of depositional and post depositional (diagenetic) processes on the fracturing and its features. However, the effecting parameters are not completely understood yet. In this research, 1440 datasets, obtained from thin sections and cores analysis from one well in the Dalan and Kangan carbonate reservoir are considered to evaluate the effect of various depositional-diagenetic parameters on the fracturing. The considered parameters include lithology, facies, dolomitization (crystal size and shape), porosity, stylolitization and anhydrite nodules and they are further subdivided based on their fracture intensity. Then, the rock engineering systems (RES) approach is employed to weight them. Moreover, an interaction matrix is provided in which the main parameters are arranged along its main diagonal elements while the interrelations between pairs of parameters are distributed in its off-diagonal elements. The weighting coefficient of each parameter is calculated through this matrix. According to the calculations, facies and porosity are the most causal and effected parameters, respectively. The Fracture Index is obtained by using the weighting coefficient and normalized code of the parameters in the classification. Additionally, a polynomial equation with the coefficient of determination (R2), in FI versus number of fractures (FN) diagram, is gained 0.735 where the number of fractures is enhanced with increase in FI. Finally, 300 datasets of the data are utilized to validate the methodology. The FIs of these data (predicted values) show a proper correlation with FNs (real values).
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