A new fluid factor and its application using a deep learning approach
Amplitude interpretation for hydrocarbon prediction is an important task in the oil and gas industry. Seismic amplitude is dominated by porosity, the volume of clay, pore-filled fluid type and lithology. A few seismic attributes are proposed to predict the existence of hydrocarbon. This paper propos...
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| Main Authors: | , , , |
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| Format: | Article |
| Published: |
2019
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| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057314987&doi=10.1111%2f1365-2478.12712&partnerID=40&md5=cf9e3d951f0bf61da34fb1b4d3734172 http://eprints.utp.edu.my/22222/ |
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| Summary: | Amplitude interpretation for hydrocarbon prediction is an important task in the oil and gas industry. Seismic amplitude is dominated by porosity, the volume of clay, pore-filled fluid type and lithology. A few seismic attributes are proposed to predict the existence of hydrocarbon. This paper proposes a new fluid factor by adding a correct item based on the J attribute. The algorithm is verified through stochastic Monte Carlo modelling that contains various rock physical properties of sand and shale. Both gas and oil responses are separated by the new fluid factor. Furthermore, an approach based on the neural network model is trained using the deep learning method to predict the new fluid factor. The confusion matrix shows that this model performs well. This model allows the application of the new fluid factor in the seismic data. In this study, the Marmousi II data set is used to examine the performance of the new fluid factor, and the result is good. Most hydrocarbon reservoirs are identified in the shale�sandstone sequences. The combination of deep learning and the new fluid factor provides a more accurate way for hydrocarbon prediction. © 2018 European Association of Geoscientists & Engineers |
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