Model development for shear sonic velocity using geophysical log data: Sensitivity analysis and statistical assessment
Section snippets
Credit author statement
Mohammad Islam Miah: Conceptualization, Data curation, Methodology, Modeling/Simulation, Validation, Writing – original draft; Salim Ahmed: Supervision, Technical Discussion, Writing- Reviewing and Editing; Sohrab Zendehboudi: Supervision, Technical Discussion, Writing- Reviewing and Editing
Fundamentals of wireline logs and quality assurance of log data
The formation properties such as rock porosity, permeability, and acoustic travel time can be obtained through experimental core analysis and/or using petrophysical wireline log data. The logs are reliable to estimate in-situ porosity, permeability, and acoustic velocities in the absence of core data. The most common petrophysical logs are natural gamma-ray (GR), resistivity, sonic, neutron, and density logs, which are generally used for formation analysis and geomechanical properties
Data quality
The wireline log data such as gamma-ray, formation bulk density, neutron porosity, acoustic compressional, and shear sonic velocities are considerably changed with formation depth due to the complex behavior and heterogeneity of shaly sand sedimentary rocks. For the data set under study, the statistical information on the log data samples are presented in Table 2 for the entire depth of the formation. The correlation matrix between Vs and other formation properties (e.g., gamma-ray, neutron
Conclusions
In this study, Gaussian radial basis kernel function (RBF) - least square support vector machine (LSSVM) linked with coupled simulated annealing (CSA) optimization technique is employed to obtain the dynamic shear sonic velocity (Vs) using real field petrophysical log data such as gamma-ray, neutron porosity, bulk density, and compressional sonic velocity. The LSSVM approach also finds the most influential parameters and ranks them based on their relative significance. In the current research,
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
We thank Equinor Canada Ltd., Natural Sciences and Engineering Research Council of Canada (NSERC), Memorial University, and InnovateNL for providing financial support, and also the Chittagong University of Engineering and Technology, Bangladesh for research support to accomplish the study.
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