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High-Resolution Reservoir Prediction Using Convolutional Neural NetworksNormal access

Authors: P. Xu, W. Lu, J. Tang and L. Chen
Event name: 81st EAGE Conference and Exhibition 2019
Session: Poster: AI/Digitalization for Interpretation - Various Application
Publication date: 03 June 2019
DOI: 10.3997/2214-4609.201901392
Organisations: EAGE
Language: English
Info: Extended abstract, PDF ( 1.96Mb )
Price: € 20

We propose a new method to predict the reservoir distribution based on convolutional neural networks by using a few well log data. Deep learning methods usually need a large amount of samples, while the well log data is usually limited. To train the convolutional neural network properly by the limited log well data, we propose a two-step strategy. In the first step, the network is pre-trained by a rough prediction result which is obtained by the traditional method. Then, in the second step, the pre-trained network is fine-tuned by well log data which can be regarded as accurate labels. Field data examples demonstrates that the deep network trained based on this strategy can achieve more accurate with a high resolution reservoir prediction results.

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