相控叠前地质统计学反演在剩余油预测中的应用

doi:10.13810/j.cnki.issn.1000-7210.2023.05.016

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Abstract In the middle and later stages of old oilfield development, how to accurately describe the remaining oil distribution has become an urgent problem to be solved.Pre-stack seismic data contain seismic gather information at different incidence angles.By analyzing the variation characteristics of amplitude intensity with incidence angles, reservoir lithology and fluid distribution laws can be predicted.Therefore, taking a BSX block of Daqing Placanticline as an example, based on the analysis of fluid and rock physical characteristics, this paper conducts facies-controlled pre-stack geostatistical inversion to predict the distribution characteristics of remaining oil to find potential areas and implement precise tapping.Firstly, the longitudinal and transverse wave response characteristics caused by the gradually increasing water saturation during water injection development are clarified, and a quantitative interpretation chart for elastic fluid rock physics with longitudinal and transverse wave velocity ratio and longitudinal wave impedance is established.Secondly, with the relationship between P-S wave velocity ratio, P-wave impedance, and water (oil) saturation as the link, a facies-controlled Monte Carlo inversion algorithm is adopted to generate high-resolution rock elastic parameter volume and oil saturation volume.The application results show that the predicted remaining oil distribution has a coincidence rate of 76.5% with the measured data of core wells.This method guides the potential tapping strategies for five remaining oil types in the BSX area and can provide references for the efficient development of other terrestrial oilfields.

Key words： facies control pre-stack geostatistical inversion remaining oil Daqing Placanticline

Received: 03 November 2022

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P631

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	Articles by authors
	WANG Peng
	XU Liheng
	YANG Huidong
	HUANG Yong
	ZHOU Tiejun

Cite this article:

WANG Peng,XU Liheng,YANG Huidong等. Application of facies-controlled pre-stack geostatistical inversion in residual oil prediction[J]. OGP, 2023, 58(5): 1192-1201.

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http://www.ogp-cn.com/EN/10.13810/j.cnki.issn.1000-7210.2023.05.016 OR http://www.ogp-cn.com/EN/Y2023/V58/I5/1192

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