Abstract
The formation dip angle is an important characteristic parameter that reflects underground structures, and it is widely used in seismic exploration and geological interpretation. However, the routine dip angle calculation method suffers from the problems of artifact interference and insufficient accuracy, which restricts the development of fine exploration technology. More accurately obtaining the formation dip angle has become a general concern. To solve these problems, this paper proposes a method for calculating the formation dip angle using deep learning. The dip angle calculation is considered a regression problem. By establishing a database of synthetic seismic data and dip data tags, data-driven fitting of nonlinear functional relationships between the seismic data and dip angles are achieved, and intelligent dip calculations are realized. The method is verified using the synthetic sedimentary model and actual data and is compared with the mainstream dip angle calculation method in the industry. The results show that this method more realistically reflects the undulating characteristics of a structure with both high accuracy and anti-interference ability.
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Reference
Al-Dossary S., Marfurt K.J. 2006. 3D volumetric multispectral estimates of reflector curvature and rotation. Geophysics, 71(5): 41–51.
Bakker P., Van L.J., Verbeek P.W. 1999. Edge preserving orientation adaptive filtering. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Fort Collins, CO: IEEE, 535–540.
Barnes A. E. 1996. Theory of 2D complex seismic trace analysis. Geophysics, 61(1): 264–272.
Barnes A.E. 1999. Attributes for auto mating seismic facies analysis. Seg Technical Program Expanded Abstracts, 19(1):2484.
Claerbout J. F. 1992. Earth soundings analysis: processing versus inversion. Cambridge, Massachusetts, USA: Blackwell Scientific Publications.
Claerbout J. and Brown M. 1999. Two-dimensional textures and prediction error filters. 61st Annual Conference and Exhibition, EAGE, Extended Abstracts, Session 1009
Hinton G. E. 2006. Reducing the dimensionality of data with neural networks. Science, 313(5786): 504–507.
Fomel, S. 2002. Applications of plane-wave destruction filters: Geophysics, 67(6), 1946–1960.
Lecun Y., Bengio Y., Hinton G. E. 2015. Deep learning. Nature, 521(7553): 436–444.
Lecun Y., Bottoo L, Bengio Y. l998. Gradient based learning applied to document recognition. Proceedings of the IEEE, 86(11): 2278–2324.
Mu L.H., YANG G.T., Gao W.Z. 2017. Formation dip calculation using plane-wave decomposition based on logging and seismic integration. Geophysical Prospecting for Petroleum, 56(6):820–826
Ronneberger O., Fischer P., Brox T. 2015. U-Net: Convolutional networks for biomedical image segmentation: International Conference on Medical Image Computing and Computer-Assisted Intervention, 234–241.
Wu X.M., Liang L.M, Shi Y. Z. 2019. FaultSeg3D: Using synthetic data sets to train an end-to-end convolutional neural network for 3D seismic fault segmentation. Geophysics, 84(3):35–45
Yin X. Y., Gao J. H., Zong Z. Y. 2014. Curvature attribute based on dip scan with eccentric window. Chinese J. Geophysics.(in Chinese), 57(10):3411–3421
Acknowledgments
Thank Professor Zhu Guangyou and Li Chuang of China Petroleum Exploration and Development Research Institute for their valuable comments on this paper. This study is funded by the CNPC (China National Petroleum Corporation) Scientific Research and Technology Development Project (Grant No. 2021DJ05).
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Feng Chao graduated from China University of Petroleum (East China) with a master’s degree in 2015. Now he is an engineer engaged in the research of oil and gas geophysics and reservoir prediction technology in the Northwest Branch of China Petroleum Exploration and Development Research Institute.
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Feng, C., Pan, JG., Yao, QZ. et al. Calculation method of formation dip angle based on deep learning. Appl. Geophys. 21, 303–315 (2024). https://doi.org/10.1007/s11770-022-0975-x
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DOI: https://doi.org/10.1007/s11770-022-0975-x