多组分油井水泥石弹性模量预测模型

刘建; 张林海; 陶谦; 刘伟; 周仕明; 王立双

doi:10.3969/j.issn.1001-5620.2016.02.016

多组分油井水泥石弹性模量预测模型

doi: 10.3969/j.issn.1001-5620.2016.02.016

中国石化石油工程技术研究院, 北京 100101

详细信息

作者简介:
刘建,1983年生,2009年毕业于西南石油大学油气田材料与应用专业,主要从事固井水泥浆体系和油井水泥外加剂相关的研究工作。电话(010)84988238;E-mail:liujian6702003@126.com。

中图分类号: TE256
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出版历程
- 收稿日期: 2015-12-03
- 刊出日期: 2016-03-31

A Model Predicting Elastic Moduli of Multi-component Set Cement

Sinopec Research Institute of Petroleum Engineering, Beijing 100101, China

摘要

摘要: 针对目前多组分油井水泥石弹性模量测试的复杂性和重复性差,弹性模量预测缺乏理论模型的现状,利用高精度抗压抗折一体机探索了多组分油井水泥石弹性模量的变化规律,建立了多组分油井水泥石弹性模量预测模型。通过实验数据验证了模型的可行性,实验发现,对于两组分油井水泥石弹性模量的计算,只需要测定弹性材料体积分数为0和0.2这2个端点的弹性模量大小,然后根据线性关系就可以在整个[0,0.2]区间内建立油井水泥石弹性模量预测模型。而多组分油井水泥石弹性模量变化规律在[0,0.2]区间内呈非线性变化,通过反推法和相关系数法确定多组分油井水泥石弹性模量的预测模型参数。该模型的建立对于减少实验工作量和进行深入的理论研究具有重要的借鉴意义。
- 多组分 /
- 油井水泥石 /
- 弹性模量 /
- 相关系数法 /
- 预测模型
Abstract: Complexity and poor repeatability of the procedure for the testing of the elastic moduli of multi-component set cement,and the lack of theoretical model in predicting the elastic moduli have led to the use of high precision compressive bending test machine to study the changing pattern of,and to build a prediction model for the elastic moduli of multi-component set cement.It has been found that for two-component set cements,their moduli at elastic components concentration of 0 and 0.2(volume fraction) can be used to predict the change of modulus with the composition of the cement,because a linear relationship exists between the composition and the modulus.For multi-component cement,this linearity does not exist between the elastic components concentration of 0 and 0.2(volume fraction).In this case,inverse method and correlation-coefficient method are to be used to predict the elastic moduli of the set multicomponent set cement.This model is of importance in minimizing the experimental workload and in theoretical study of the elastic moduli of the multi-component set cement.
- Multi-component /
- Set cement for oil well /
- Elastic modulus /
- Correlation-coefficient method /
- Prediction model

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参考文献(9)

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