Well logging restoration based on spatial-temporal information mining from muti-scale correlation graph representation

Abstract

Geological reservoir characterization using well-loggingg data often faces challenges, such as data distortion or even missing values. To tackle this, traditional signal restoration theories and machine learning techniques, such as neural networks, have been commonly employed. However, they struggle to fully explore correlation information among logging curves within the same well and lack adaptability across different wells. In this paper, a novel Logging Curve Restoration method based on Spatial-Temporal Information Mining (LCR-STIM) using multi-scale correlation graph representation is proposed. Specially, a novel multi-scale graph representation technique is employed to build spatial (i.e., horizontal) nonlinear relationships among logging curves. Subsequently, deep forest model is introduced to generate multi-scale predictions to uncover the rich spatial information hidden in the multi-scale graph representations. Finally, to fully utilize temporal information in the obtained different multi-scale predictions, Long Short-Term Memory (LSTM) network is employed to combine the multi-scale restoration results to predict target logging data accurately. Taking the well logging data of 41 wells from Daqing oil field as training data and the target logging curves from 3 other wells as the test data, experimental results shown that the restoration performance can be improved by at least 20% compared with the state-of-the-art comparison methods.

Keywords:

• Deep Forest
• graph representation
• information mining
• restoration
• well logging

Acknowledgements

The authors report there are no competing interests to declare.

Disclosure statement

No potential conflict of interest was reported by the author(s)

Additional information

Funding

This work was supported by the [Hainan Provincial Science and Technology Special Fund] under Grant [number ZDYF2022GXJS222]and Grant [number ZDYF2022GXJS220].