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A reservoir study was conducted at Gaucho Field in the Chiapas of Southern Mexico, the primary objective of which was to determine porosity in the base of the upper Cretaceous carbonate in order to facilitate further field development. Conventional seismic impedance inversion alone did not adequately predict porosity nor did neural network predictions using conventional seismic attributes. Spectral decomposition and neural network inversion were integrated to produce an estimated porosity cube at the target level that provided excellent porosity indication in validation wells. The lateral variation of porosities within the area ranged from about 2% to more than 30%. Thus, the application of these techniques allowed final adjustment of drilling locations, in order to capture the maximum local porosity possible. Resulting porosity maps within the field area are shown to have important implications for field development and further exploration in this area.

For spectral decomposition, this study illustrates the relationship between porosity thickness and peak frequency and between the magnitude of the average effective zone porosity and peak amplitude. Additionally, the study demonstrates the importance of training a neural network properly with (1) appropriate input attributes and (2) utilization of wells which cover the spectrum of possible porosity encountered in the area. We show how such a methodology can be applied to similar carbonate reservoirs so as to distinguish locations having minimal to no effective porosity from areas having excellent porosity where additional development drilling can be fruitful.

The aim of this paper is to showcase an integrated workflow for this type of study, rather than to focus on results.

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