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Abstract

Summary

This paper is concerned with a methodology and workflow developed to apply Artificial Neural Networks (ANN) to convert seismic attributes into a reservoir property. As a result, a Vshale cube was created for a complex fluvial-deltaic reservoir in the Azeri-Chirag-Gunashli field in the South Caspian Basin, offshore Azerbaijan. Selected well data was used as an input into the learning process of Multi-Layer Perceptron type of ANN technique to identify the relationships between seismic attributes and Gamma Ray log. The trained model was later applied to an attribute volume to cover a bigger area of the field away from the well control. Particular attention was given to preparation and quality assurance of the seismic data, which played a crucial role in improving the ANN training process. Four different testing techniques were used to validate the outcomes of the model using static and dynamic well data. Net-to-Gross maps, Vshale cross-sections and connectivity analysis from the well surveillance data were amongst the techniques used to verify ANN outputs both qualitatively and quantitatively.

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/content/papers/10.3997/2214-4609.201702628
2017-11-05
2024-03-29
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