A permeability prediction method based on pore structure and lithofacies
| Autor: | Ming Zhang, Hao Yang, Lideng Gan, Xiaofeng Dai, Xianbin Li, Yaojun Wang, Xianzhe Luo |
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| Rok vydání: | 2019 |
| Předmět: |
Field data
0211 other engineering and technologies Energy Engineering and Power Technology Geology Soil science 02 engineering and technology 010502 geochemistry & geophysics Geotechnical Engineering and Engineering Geology 01 natural sciences Permeability (earth sciences) Shear (geology) Geochemistry and Petrology lcsh:TP690-692.5 Linear regression Economic Geology 021108 energy Porosity lcsh:Petroleum refining. Petroleum products 0105 earth and related environmental sciences |
| Zdroj: | Petroleum Exploration and Development, Vol 46, Iss 5, Pp 935-942 (2019) |
| ISSN: | 1876-3804 |
| DOI: | 10.1016/s1876-3804(19)60250-8 |
| Popis: | Permeability prediction using linear regression of porosity always has poor performance when the reservoir with complex pore structure and large variation of lithofacies. A new method is proposed to predict permeability by comprehensively considering pore structure, porosity and lithofacies. In this method, firstly, the lithofacies classification is carried out using the elastic parameters, porosity and shear frame flexibility factor. Then, for each lithofacies, the elastic parameters, porosity and shear frame flexibility factor are used to obtain permeability from regression. The permeability prediction test by logging data of the study area shows that the shear frame flexibility factor that characterizes the pore structure is more sensitive to permeability than the conventional elastic parameters, so it can predict permeability more accurately. In addition, the permeability prediction is depending on the precision of lithofacies classification, reliable lithofacies classification is the precondition of permeability prediction. The field data application verifies that the proposed permeability prediction method based on pore structure parameters and lithofacies is accurate and effective. This approach provides an effective tool for permeability prediction. Key words: seismic reservoir prediction, pore structure, permeability, lithofacies, shear frame flexibility factor, boosting learning |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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