Journal Paper

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Journal: GEOPERSIA (علوم)
Year:2011 | Volume: | Issue:
Start Page: | End Page:

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Title

PREDICTION OF SHEAR AND COMPRESSIONAL WAVE VELOCITIES FROM PETROPHYSICAL DATA UTILIZING GENETIC ALGORITHMS TECHNIQUE: A CASE STUDY IN HENDIJAN AND ABUZAR FIELDS LOCATED IN PERSIAN GULF

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 Start Page 1 | End Page 17

Abstract

 Shear and Compressional Wave Velocities along with other Petrophysical Logs, are considered as upmost important data for Hydrocarbon reservoirs characterization. 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, DSI (Dipole Shear Sonic imager) by Schlumberger and MDA (Monopole-Dipole Array) by Weatherford Company. Usually in Old Wells, there is lack of Shear Velocity data, or in other Wells, only some intervals may have Vs data. SHEAR WAVE VELOCITY is of high importance in Geophysical studies such as AVO (Amplitude Variation with Offset) and VSP (Vertical Seismic Profiling) and along with COMPRESSIONAL WAVE VELOCITY, it can be used for identification of Fluid Type, Lithology and Mechanical Rock Properties. GENETIC ALGORITHMS Technique as a subset of Evolutionary Computing is an important part of Intelligent Systems for solving Optimization Problems. In this study, Compressional and Shear Wave Velocities were modeled by GENETIC ALGORITHMS Technique in GHAR MEMBER of ASMARI FORMATION, HENDIJAN FIELD. For measuring the accuracy of the method, predicted values were compared with the real data in GHAR MEMBER of ASMARI FORMATION, ABUZAR FIELD. Multiple REGRESSION ANALYSIS was used as alternative technique to evaluate the accuracy of the GA Model. The results of this study show that GA could be considered as an efficient, fast and cost-effective method for predicting Vs and Vp data from conventional well Log data. Predicted COMPRESSIONAL WAVE VELOCITY along with SHEAR WAVE VELOCITY, validates the reliability of GENETIC ALGORITHMS as an Optimization Technique for predicting parameters such as Vs.

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