Abstract
Ambiguous dependence of observed data related to lithologic parameters suggests that practical lithologic inversion problems are characterized by both deterministic mechanism and statistical behavior. The Caianiello neural network method is presented in this paper, including neural wavelet estimation, input signal reconstruction, and nonlinear factor optimization. A joint inversion scheme for porosity and clay-content estimations is established based on the combination of the Caianiello neural network with some deterministic petrophysical models. First, inverse neural wavelets are extracted using known solutions, and then the inverse-operator-based inversion is used to estimate an initial parameter model. Second, forward neural wavelets are 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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Fu, LY. (2003). An Information Integrated Approach for Reservoir Characterization. In: Sandham, W.A., Leggett, M. (eds) Geophysical Applications of Artificial Neural Networks and Fuzzy Logic. Modern Approaches in Geophysics, vol 21. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-0271-3_11
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DOI: https://doi.org/10.1007/978-94-017-0271-3_11
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