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Reservoir prediction and application of multi-attribute fusion
Author(s) -
Xingye Wang
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/651/3/032072
Subject(s) - selection (genetic algorithm) , data mining , computer science , quality (philosophy) , petroleum engineering , sedimentary rock , fusion , geology , artificial intelligence , paleontology , philosophy , linguistics , epistemology
Under the existing well pattern density conditions, there are still uncertainties in the understanding of reservoir distribution in XX Oilfield. The main reasons are that the different sedimentary environments and complex and changeable sedimentary models lead to uncertainties in reservoir prediction under the guidance of sedimentary models; Second, the sedimentary mechanism of different types of sand bodies is different, and there are multiple solutions in cross-well prediction. Therefore, other means must be used for auxiliary analysis in reservoir prediction, so as to improve the accuracy of sand cross-well prediction. This paper focuses on the basic method of reservoir prediction based on seismic multi-attribute fusion, which can further improve the accuracy of cross-well prediction in the cross-well prediction of thick oil layers In addition, the sand body distribution characteristics corrected by multi-attribute fusion slice are applied to measure well selection and layer selection, and comprehensive analysis of remaining oil is carried out by integrating various disciplines, and relatively high-quality potential tapping horizons are determined, which further improves the coincidence rate of potential tapping by measures.

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