Titlebook: Geophysical Applications of Artificial Neural Networks and Fuzzy Logic; William A. Sandham,Miles Leggett Book 2003 Springer Science+Busine

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Automated Picking of Seismic First-Arrivals with Neural Networkshe multinomial distribution, since it can reflect the fact that there is only one first-arrival event on each data trace. Optimization of the neural network weights with an error function based on this probability distribution, produces a neural network that properly estimates the probability that the associated feature is a first-arrival event.

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Automated 3-D Horizon Tracking and Seismic Classification Using Artificial Neural Networkshown to be a viable technique for use as a standard tool and for enhancing efficiency in 3-D seismic interpretation. 1.5-D and 2-D methods have also been demonstrated successfully, which account for the seismic character above, below, behind and ahead of the current tracking position.

CRUE

Reservoir Property Estimation Using the Seismic Waveform and Feedforword Neural NetworksThe resulting prediction map was used to select new well locations and design horizontal well trajectories. Four wells were drilled based on the prediction, and all were successful. This increased oil field production by about 20%.

AMPLE

An Information Integrated Approach for Reservoir Characterization estimated likewise, and then the forward-operator-based reconstruction can improve the initial parameter model. The scheme has been applied in a complex continental deposit in western China and significantly improves the spatial description of reservoirs.

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https://doi.org/10.1007/978-3-322-99133-1. The network was trained using both 5 and 10 numerically modeled records of the vertical component. After convergence, the network was tested using a randomly generated three-layer transverse isotropic model. The inversion results are very encouraging.

红润

,Messung von Wirk- und Scheinwiderständen,the Mississippi Canyon. The properties of high amplitude, low frequency, and polarity reversal are observed from projections on the principal eigenvectors. The GHA network also provides significant seismic data compression.

GLOSS

https://doi.org/10.1007/978-3-322-85550-3quality measure of the classification results, and provides valuable information concerning the reliability of the model. The classification approach has been used successfully for determining Upper Carboniferous lithologies from open hole logs in a multi-well study.

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