AI SearchIntergration of wellog and seismic attribute data to predict porosity along a 2-D seismic line in Nam Con Son Basin using ANN analysis
Call Number: AIT RSPR no.PME-GEPG-11-01 Material type:
TextSeries: Asian Institute of Technology. Research studies project report ; no. PME-GEPG-11-01Publication details: Pathum Thani : Asian Institute of Technology, 2012Description: 56 leaves : ill., charts + 1 online resourceSubject(s): Online resources: Dissertation note: Research Studies Project Report (M.Eng.) - Asian Institute of Technology, 2012 Summary: This study presents away to estimate porosity by seismic attribute and well logging data from limited well log. The first step in methodology is do a quick log analysis to choose the efficiency well log to calculate the porosity. Next, group of seismic attribute will be estimate by using Seismic Unix (SU).A seismic processing data was use as input to calculate the seismic attribute by using the complex analysis and L1-norm deconvolution. After that, the seismic attributes will be trained by porosity estimate from the well logging data. Neural network process is performed and generated an porosity prediction at well location. Final, the porosity along the 2D seismic line will be estimated based on seismic attribute by using ANN.
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Asian Institute of Technology Library AIT Publications | AIT RSPR no.PME-GEPG-11-01 (Browse shelf(Opens below)) | 1 | Available | 30050120721229 | |||||||||||||
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20-AIT Publication
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Asian Institute of Technology Library AIT Publications | AIT RSPR no.PME-GEPG-11-01 (Browse shelf(Opens below)) | 2 | Available | 30050120721211 |
A research study submitted in partial fulfillment of the requirement for the degree of Master of Engineering (Professional) in Geoexploration and Petroleum Geoengineering
Research Studies Project Report (M.Eng.) - Asian Institute of Technology, 2012
This study presents away to estimate porosity by seismic attribute and well logging data from limited well log. The first step in methodology is do a quick log analysis to choose the efficiency well log to calculate the porosity. Next, group of seismic attribute will be estimate by using Seismic Unix (SU).A seismic processing data was use as input to calculate the seismic attribute by using the complex analysis and L1-norm deconvolution. After that, the seismic attributes will be trained by porosity estimate from the well logging data. Neural network process is performed and generated an porosity prediction at well location. Final, the porosity along the 2D seismic line will be estimated based on seismic attribute by using ANN.
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