1887

Abstract

Summary

As petroleum resources run out, subtle stratigraphic reservoirs have become an important source of future discoveries. Continental tertiary deposits of fluvio-deltaic origin are known to produce such complex reservoirs and pose a challenge for characterization. We propose a novel bio-integrated framework called multi-modal machine learning fusion to tackle this challenge. The approach bases reservoir characterization on biological interpretation of sensory data. Using seismic attributes as varied modal inputs and rock physics relationships as input data, we perform multi-modal machine learning fusion to predict the gamma ray spatial distribution. Before processing however, the seismic data is filtered with a dip steered filtering algorithm to remove random noise. The resulting volume is then used in computing the seismic attributes. Effective seismic attributes utilized were, instantaneous amplitude, instantaneous frequency, spectral decomposition, seismic inversion and similarity. Results from a case study in the Bohai Bay Basin in Eastern China demonstrate that multi-modal machine learning fusion can predict the spatial distribution of lithology using gamma ray logs. The conclusions from this study can be applied in analogous reservoirs in similar petroleum provinces in other regions of the world and can also be applied to other geophysical interpretational methods.

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/content/papers/10.3997/2214-4609.201801714
2018-06-11
2024-03-28
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References

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