A Facies Proportional Determination Method Based on the Theory of Confidence Intervals: A Case Study in the M Gas Field in the East China Sea

A reservoir is the space and gathering place of underground oil and gas reservoirs. Lithofacies proportion is the key parameter of a reservoir. Due to the heterogeneity of geological bodies, the multi-solution of seismic data and the limited and uneven distribution of logging data, it is unclear how...

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Bibliographic Details
Main Authors: Shichao Wei, Shaohua Li, Siyu Yu
Format: Article
Language:English
Published: MDPI AG 2023-07-01
Series:Applied Sciences
Subjects:
Online Access:https://www.mdpi.com/2076-3417/13/14/8068
Description
Summary:A reservoir is the space and gathering place of underground oil and gas reservoirs. Lithofacies proportion is the key parameter of a reservoir. Due to the heterogeneity of geological bodies, the multi-solution of seismic data and the limited and uneven distribution of logging data, it is unclear how to determine the lithofacies proportion in the study area directly from the available data. The mean is a way to make a quick estimate, but the mean cannot quantify the uncertainty of the lithofacies proportion using the available data. In this paper, a new method for quantifying uncertainty and determining the value of the three levels of the facies proportion is proposed. Taking sandstone proportion as an example, a normality test was carried out to verify the applicability of the sandstone proportion data to this method, and then the mean and variance of samples were calculated. Based on the confidence interval theory, the uncertainty of the mean value and the range of sandstone proportion were determined, that is, the optimistic value and the pessimistic value of the sandstone proportion. The results show that this method provides a new reference for quantifying values for the three levels of the uncertainty, and it uses the petrographic proportion as an example to provide the data basis for subsequent modeling and reservoir research.
ISSN:2076-3417