The exploration and exploitation of shale oil is an important aspect in the oil industry. Seismic properties and well-log data are essential to establish wave-propagation models. Specifically, the description of wave dispersion and attenuation under complex geological conditions needs proper lithological and petrophysical information. This complex physical mechanism has to be considered if a traditional modeling approach is adopted. In this sense, machine learning (ML) techniques provide new possibilities for this purpose. We compare two deep-neural-network (DNN)-based wave propagation models. In the first (pure data-driven), a DNN is trained to connect seismic attributes, such as wave velocities, to multivariate functions of rock-physics properties. By training DNNs with different initial parameters, the uncertainty of the proposed method can be quantified. The second method assumes the form of the wave equations. Then, the elastic constants of the constitutive relations are predicted by DNNs. The resulting dynamical equations describe the dispersion and attenuation and wavefield simulations can be performed to obtain more information. On the basis of a test, the two kinds of wave-propagation models yield acceptable estimations of the seismic properties, with the second approach showing a broader application because the DNN is trained without S wave data. The methodologies illustrate that the new wave-propagation model based on ML has high precision and can be general in terms of rheological description.

Data-driven design of wave-propagation models for shale-oil reservoirs based on machine learning

Gei D;
2021

Abstract

The exploration and exploitation of shale oil is an important aspect in the oil industry. Seismic properties and well-log data are essential to establish wave-propagation models. Specifically, the description of wave dispersion and attenuation under complex geological conditions needs proper lithological and petrophysical information. This complex physical mechanism has to be considered if a traditional modeling approach is adopted. In this sense, machine learning (ML) techniques provide new possibilities for this purpose. We compare two deep-neural-network (DNN)-based wave propagation models. In the first (pure data-driven), a DNN is trained to connect seismic attributes, such as wave velocities, to multivariate functions of rock-physics properties. By training DNNs with different initial parameters, the uncertainty of the proposed method can be quantified. The second method assumes the form of the wave equations. Then, the elastic constants of the constitutive relations are predicted by DNNs. The resulting dynamical equations describe the dispersion and attenuation and wavefield simulations can be performed to obtain more information. On the basis of a test, the two kinds of wave-propagation models yield acceptable estimations of the seismic properties, with the second approach showing a broader application because the DNN is trained without S wave data. The methodologies illustrate that the new wave-propagation model based on ML has high precision and can be general in terms of rheological description.
Reservoir characterisation; Well log data; Machine learning
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.14083/2146
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