By M. Nikravesh, L. A. Zadeh, Fred Aminzadeh
This accomplished e-book highlights tender computing and geostatistics functions in hydrocarbon exploration and creation, combining functional and theoretical aspects.
It spans a large spectrum of functions within the oil undefined, crossing many self-discipline obstacles equivalent to geophysics, geology, petrophysics and reservoir engineering. it truly is complemented through numerous educational chapters on fuzzy common sense, neural networks and genetic algorithms and geostatistics to introduce those options to the uninitiated. the appliance components comprise prediction of reservoir homes (porosity, sand thickness, lithology, fluid), seismic processing, seismic and bio stratigraphy, time lapse seismic and middle analysis.
There is an effective stability among introducing gentle computing and geostatistics methodologies that aren't frequently utilized in the petroleum and numerous functions components. The ebook can be utilized by means of many practitioners similar to processing geophysicists, seismic interpreters, geologists, reservoir engineers, petrophysicist, geostatistians, asset mangers and expertise software pros. it's going to even be of curiosity to lecturers to evaluate the significance of, and give a contribution to, R&D efforts in appropriate areas.
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Additional resources for Soft Computing and Intelligent Data Analysis in Oil Exploration
Sample text
3. Inference p r o g r a m . . . . . . . . . . . . . . . . . . . . 4. Applications . . . . . . . . . . . . . . . . . . . . . . . . 1. Verification . . . . . . . . . . . . . . . . . . . . . . 2. C o m p a r i s o n of methods . . . . . . . . . . . . . . . . . . . 3. Field examples . . . . . . . . . . . . . . . . . . . . . 5. Conclusions . .
2. Noise elimination A related problem to FSA is editing noise from the seismic record. The objective here is to identify events with non-seismic origin (the reverse of FSA) and then remove them from the original data in order to increase the signal to noise ratio. Liu et al. (1989), McCormack (1990) and Zhang and Li (1995) are some of the publications in this area. Zhang and Li (1995) handled the simpler problem, to edit out the whole noisy trace from the record. They initiate the network in the 'learning' phase by 'scanning' over the whole data set.
3. Neural network models . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . -S. L i m . . . . . . . . . . . . . . . . . . . . . Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2. Multivariate statistical analysis . . . .