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Comprehensive Identification and Distribution Prediction of Source Rocks in Es1 in Nanpu Sag
Author: LI Yuezhe, YIN Jie, YE Lin, WANG Zhenqi Time: 2023-08-03 Counts:

doi:10.16186/j.cnki.1673-9787.2022060055

Received:2022-06-22

Revised:2022-09-28

Online Date:2023-08-03

Comprehensive Identification and Distribution Prediction of Source Rocks in Es1 in Nanpu SagOnline

LI Yuezhe, YIN Jie, YE Lin, WANG Zhenqi

School of GeosciencesYangtze UniversityWuhan 430100HubeiChina

Abstract:In order to solve the problem of obvious heterogeneity of source rock development and distribution in different sub sags in Nanpu Sagthe identification and distribution prediction of hydrocarbon source rock series were carried out in depth ΔlgR  method and the multivariate linear Law of Return method are used to establish the quantitative prediction model of TOC of source rock for different sedimentary facies belts in the study area. The corresponding relationship between seismic reflection characteristics and geochemical characteristics of Source rock in the work area is establishedand horizontal tracking is carried out. The results show that the TOC curves of high quality source rocks are mainly box-shaped with sawtoothshaped characteristic curvesand the seismic reflection features are mainly of medium-strong amplitude medium-high frequencyhigh continuity and subparallel-parallel reflection structurethe TOC curves of medium quality are high value spike sawtooth-shapedand the seismic phase features are of medium-weak amplitudemedium-low frequencyhigh continuity and subparallel structure.The source rocks are mainly distributed between the No.5 and No.1 tectonic zonesthe upper plate of the combination of the Gaoliu fault andthe Southwest Zhuang faultand near the No.3 tectonic zone.As the exploration practice of unconventionaloil and gas reservoirs in Nanpu Depression gradually deepensthe above results provide theoretical guidance for the subsequent exploration and development.

Key words:Nanpu sag;Es;source rock;well logging response;distribution prediction

CLCP618.13

 

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