Abstract
Hydrocarbon resources shortage is a wide-world issue, which causes great efforts being made for hydrocarbon resources exploration all over the world. Songliao Basin is one of the most important potential regions for hydrocarbon resources in Northeast China. Owing to cost saving of hydrocarbon exploration, it is necessary to evaluate the regional potential of hydrocarbon by remote sensing before costly hydrocarbon exploration and drilling. In the study, Landsat TM data at the western slope of Songliao Basin are processed to improve hydrocarbon-related linear-circular structures and micro-seepages information. A self-organizing neural network is built for the evaluation to hydrocarbon potentials of unknown areas. Twelve features are integrated into the model from remote sensing, geophysical anomaly, and geological setting around the western slope of Songliao Basin. The model is trained by a competitive learning of twelve features of four hydrocarbon-known boreholes. The hydrocarbon potentials in three unknown circular clusters are evaluated successfully by the trained self-organizing neural networks model.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Chen, S., Zhou, Y.: Classified Depth-layered Geological Structures on Landsat TM Images by Gravity Data: a Case Study of The Western Slope of Songliao Basin, Northeast China. J. International Journal of Remote Sensing 26, 2741–2754 (2005)
Ricchetti, E.: Structural Geological Study of Southern Apennine (Italy) Using Landsat 7 Imagery. J. International Geoscience and Remote Sensing Symposium (IGARSS) 1, 211–213 (2002)
Liu, T., Shi, X., Hou, L., Yuan, H.: Evaluating The Hydrocarbon-bearing Characteristics of Geologic Traps in Yakela Area in Northern Tarim Basin by Comprehensive Pattern Recognition Technique. J. Oil Geophysical Prospecting 32, 556–564 (1997)
Uddin, S., Dousari, A., Literathy P.: Evidence of Hydrocarbon Contamination from The Burgan Oil Field, Kuwait-Interpretations from Thermal Remote Sensing Data. Journal of Environment Management 86, 605–615 (2008)
Werff, H., Bakker, W., et al.: Combing Spectral Signals and Spatial Patterns Using Multiple Hough Transformations: An Application for Detection of Natural Gas Seepages. J. Computers & Geosciences 32, 1334–1343 (2006)
Xu, D., Ni, G., Jiang, L., et al.: Exploring for Natural Gas Using Reflectance Spectra of Suface Soils. J. Adances in Space Research 41, 1800–1817 (2007)
Sun, S., Wu, Z., Bei, F.: Application of an Artificial Neural Network to Evaluating Conditions of Hydrocarbon Generation. J. Oil & Gas Geology 17, 68–70 (1996)
Zhang, X., Li, Y., Liu, H.: Hydrocarbon Prediction Using Dual Neural Network. J. SEG annual meeting 70, 1440–1443 (2000)
Xiong, Y., Bao, J., Xiao, C.: Hydrocarbon Reservoir Prediction Using Fuzzy Neural Network. J. Oil Geophysical Prospecting 35, 222–227 (2000)
Doraisamy, H., Vice, D., Halleck, P.: Detection of Hydrocarbon Reservoir Boundaries Using Neural Network Analysis of Surface Geochemical Data. J. AAPG Bulletin 84, 1893–1904 (2000)
Kenea, N.H.: Digtal Enhancement of Landsat Data, Spectrsal Analysis and GIS Data Integration for Geological Studies of The Derudeb Area, Southern Ren Sea Hills, NE Sudan. Berliner Geowissenschaftliche Abhandlungen(D) 14, 111–116 (1997)
Ricotta, C., Avena, G.C., Marchetti, M.: The Flaming Sandpile: Self-organized Criticality and Wildfires. J. Ecol. Model 119, 73–77 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chen, S. (2009). Remote Sensing Based on Neural Networks Model for Hydrocarbon Potentials Evaluation in Northeast China. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5553. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01513-7_93
Download citation
DOI: https://doi.org/10.1007/978-3-642-01513-7_93
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01512-0
Online ISBN: 978-3-642-01513-7
eBook Packages: Computer ScienceComputer Science (R0)