Abstract
Shear and Compressional Wave Velocities along with other Petrophysical Logs, are considered as upmost important data for Hydrocarbon reservoirs characterization. In this study, porosity of the extracted rocks form concerned wells is interest as it can indicate the oil capacity of the wells of interest. In this study, we employ the principles of Axiomatic Design theory, specially the first (independence) axiom, to more simplify the measurement system. Also, to clarify the strength of Axiomatic Design theory in reducing the complexity of the system and optimizing the measurement system, we utilize the The Lolimot model (LOcal LInear MOdel Tree) as a model from the neural network family and apply it before and after implementing the basic logic of Axiomatic Design (AD) theory. In addition, in order to illustrate strength of the proposed method emphasizing the effectiveness of a method which benefit from both AD theory and Lolimot model together, the existing system used to measure the rock porosity is addressed and actual data related to one of wells located in southern Iran is utilized. The results of the study show that integrating the Axiomatic Design principles with the LOLIMOT method leads to the least complex and most accurate results.
Highlights
In rock engineering, methodologies based on wave velocity are increasingly used to determine the dynamic properties of rocks (Singh et al, 2012)
Maleki et al (2014) used petrophysical logs corresponding to a well drilled in southern part of Iran to estimate the shear wave velocity using empirical correlations as well as two robust artificial intelligence methods knows as Support Vector Regression (SVR) and Back-Propagation Neural Network (BPNN)
Shear Wave Velocity (Vs) in Well Logging is commonly measured by some sort of Dipole Logging Tools, which are able to acquire Shear Waves as well as Compressional Waves such as Sonic Scanner, dipole shear sonic imager (DSI) (Dipole Shear Sonic imager) by Schlumberger and MDA (Monopole-Dipole Array) by Weatherford Company
Summary
Methodologies based on wave velocity are increasingly used to determine the dynamic properties of rocks (Singh et al, 2012). Poisson’s ratio is changing in a wide range in practice; the accuracy of estimated shear sonic data is questionable (Carroll, 1969) Another approach is to measure elastic properties of rocks through acoustic measurements of Vp and Vs using pulse transmission techniques in laboratory (Birch, 1960; Christensen, 1974; Kern, 1982; Burlini and Fountain, 1993; Ji and Salisbury, 1993; Watanabe et al, 2007). Petrophysical logs corresponding to a well drilled in the southern part of Iran were used to estimate the shear wave velocity using empirical correlations as well as novel AI techniques Such basic information are regarded as required input to predict the porosity of rocks extracted from the concerned wells. Because of rational, comprehensive, and strong principles of AD approach; we attempt to apply its fundamentals to the so called “Neuro Fuzzy Models”, specially The Lolimot model (LOcal LInear MOdel Tree) as a model from the neural network Family
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