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Quantitative identification and prediction of mixed lithology, Bohai Sea, China

Authors :
Shaopeng Wang
Longtao Cui
Li'an Zhang
Chao Ma
Hebing Tang
Source :
Energy Geoscience, Vol 5, Iss 3, Pp 100284- (2024)
Publication Year :
2024
Publisher :
KeAi Communications Co., Ltd., 2024.

Abstract

The Paleogene Shahejie Formation in the KL16 oilfield, Bohai bay, is characterized by a thinly interbedded mixed sedimentary system, with complex sedimentary facies, lithologic types and distributions. It is hard for conventional logging methods to identify the lithology therein. In order to solve the difficulty in lithologic identification of mixed sedimentary system, analyses based on graph data base using elemental capture energy spectrum log have been proposed. Due to the different composition for the various minerals, we innovatively established the molar numbers of silicon, calcium, magnesium, and aluminum as characteristic parameters for sandstone, limestone, dolomite, and mudstone, and a graph clustering analysis method was applied to identify lithology. Considering the seismic waveforms corresponding to lithologic impedance of reservoir, three seismic phases were identified by neural network clustering analysis of seismic waveform, and the seismic attributes with high sensitivity to reservoir thickness were then selected to realize the fine description of the mixed carbonate-siliciclastic reservoir. Drilling results confirmed that the sedimentary facies were accurately identified, with reservoir prediction accuracy reaching up to 80%. Under the guidance of reservoir research, the oil-in-place discovered in the oilfield were estimated to be more than 5 million tonnes. This technology provides reference for the exploration and development of oilfields of mixed sedimentary system.

Details

Language :
English
ISSN :
26667592
Volume :
5
Issue :
3
Database :
Directory of Open Access Journals
Journal :
Energy Geoscience
Publication Type :
Academic Journal
Accession number :
edsdoj.7019fe21ce064401b4bb98e892617e37
Document Type :
article
Full Text :
https://doi.org/10.1016/j.engeos.2023.100284