We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
试验数据相似性修正减阻效果预测模型.
- Authors
王小丹; 王寿喜
- Abstract
In order to evaluate the running state of oil pipeline additives, a drag reduction effect prediction model was established. In order to solve the problems that the existing model cannot realize the analogy prediction of drag reduction effect under different working conditions, the undetermined coefficients of the model need to be solved in advance in the application, and the obtained parameters do not have the applicability of lateral expansion, based on the relevant model, the similarity correction is carried out by analyzing and processing the field additive test data, and the correction coefficients of pipe diameter and oil viscosity are introduced to simplify the prediction complexity of drag reduction effect and expand the application range of the prediction model. After considering the problem of shear dilution of drag reducer, the degradation rate coefficient is quantitatively calculated according to the actual adding condition of pipeline to improve the prediction accuracy of the model. Based on the field test data, regression verification is carried out to obtain the value of the undetermined coefficients of the model. Finally, a general model for predicting the drag reduction effect of crude oil and product oil pipelines is obtained, and error analysis is carried out in combination with field test data to check the accuracy of the prediction model. The results show that the drag reduction effect prediction model obtained by the similarity correction of lest data has better prediction precision compared with empirical equation, and can be used to guide the application of drag reduction agents in the field, improve the problem that the model parameters cannot be applied laterally, and realize the function of drag reduction effect analogy prediction.
- Publication
Natural Gas & Oil, 2018, Vol 36, Issue 6, p7
- ISSN
1006-5539
- Publication type
Article
- DOI
10.3969/j.issn.1006-5539.2018.06.002