Reservoir characterisation method with multi-component seismic data by unsupervised learning and colour feature blending

ABSTRACT

Multi-attribute analysis of multi-component seismic data can provide abundant information on seismic hydrocarbon reservoirs. It can exploit the different responses of compressional and shear waves to the reservoir, in conjunction with colour blending and fusion technology applied to seismic images, to enable the human eye to better discriminate features in seismic data, thereby improving the accuracy of reservoir characterisation. The integration of multi-component data and sophisticated visualisation techniques helps to reduce the number of possible models of the reservoir. Thus, we designed a reservoir prediction method based on unsupervised learning and colour feature blending. First, a large number of compressional and shear wave seismic attributes were extracted using cluster analysis to conduct unsupervised learning to optimise the attributes. Then, using the different responses of compressional and shear waves to oil and gas, and an understanding of rock physics, three types of composite attribute were constructed to highlight oil and gas anomalies by multi-component seismic attributes. Finally, the three composite attributes were transformed to the colour space by a first-order linear transformation and RGB colour blending. Applying this scheme to reservoir prediction shows that unsupervised learning and colour blending techniques could help the human eye perceive geological anomalies, highlight common hydrocarbon characteristics, reduce differences and decrease interpretation ambiguity. The prediction results are essentially consistent with actual data and can be used to predict favourable exploration areas.

KEYWORDS:

• Unsupervised learning
• cluster analysis
• multi-component composite attributes
• colour blending
• seismic reservoir prediction

Acknowledgements

We thank the editor and reviewers for their valuable comments and suggestions for this paper. We thank Sinopec Petroleum Exploration and Production Research Institute for providing data for this study and Professor Xiucheng Wei and senior engineers Yuxing Ji, Tiansheng Chen, Chunyuan Liu and Tao Liu for their helpful suggestions. We are grateful to Jessica for editing and improving the readability of this article. We would also like to thank Bo Wen, Qianqian Wei, Chuanwei Zhao, Xiangchao Liu and Jie Peng for their contributions to this study.

Disclosure statement

No potential conflict of interest was reported by the authors .

Additional information

Funding

This work was supported by the National High tech Research and Development Program [grant number 2012AA061202 and 2013AA064201] and the National Natural Science Foundation of China [grant number 41174098].