Reliable Wax Predictions for Flow Assurance

Author:

Coutinho João A.P.1,Edmonds Beryl2,Moorwood Tony2,Szczepanski Richard2,Zhang Xiaohong2

Affiliation:

1. University of Aveiro

2. Infochem Computer Services Ltd.

Abstract

Abstract A number of wax models are currently in use by the oil industry which are based on parameters that were empirically determined to match available data for black oils. These data are often not very precise. The recently developed model of Coutinho is, however, based on high accuracy thermodynamic data. The paper describes how the Coutinho model can be used in conjunction with conventional equations of state to perform wax equilibrium calculations for black oils. Examples are given showing how well the model can predict both wax appearance temperature and the amount of wax precipitated at varying temperatures with or without n-paraffin analyses. The examples include the effect of pressure on live oils. Improved thermodynamic modelling of wax formation in turn allows better prediction of wax deposition rates for flow assurance. Introduction With the on-going trend to deep water developments, flow assurance has become a major technical and economic issue. The avoidance or remediation of wax deposition is one key aspect of flow assurance. The ability to predict wax deposition rates depends on a number of factors one of which is examined in this paper: the thermodynamic equilibrium between oil and wax. Wax is a solid phase formed from the components of the oil that have the highest melting points. For temperatures of operational interest, i.e. above ~0°C, wax consists predominantly of the C20+ n-paraffins. A number of engineering models have been proposed for calculating oil-wax equilibria, for example the work of Won [1], Hansen et al. [2], Erickson et al. [3], Pedersen [4], Rønningsen et al.[5], Lira-Galeana et al. [6] and Pan et al. [7]. The authors of all these models propose a number of correlations to predict the key thermodynamic parameters, but there is no direct experimental evidence to show that the assumptions made are correct. Instead the authors rely on experimental data for wax formation from oils to validate their models, predominantly measurements of wax appearance temperature (WAT). However, in a recent survey for Deepstar, Monger-McClure et al. [8] suggested that uncertainties in WAT for good modern measurements may be ±5°F. For older measurements the uncertainties can be considerably higher. Thus using data of this kind can only provide an approximate method to evaluate proposed models; it is not possible on this basis to discriminate in any detail between models. In order to put wax calculations on a firmer footing, Coutinho and co-workers have developed a wax model that is directly based on high-quality laboratory data for the properties of liquid and solid hydrocarbons and their mixtures [9,10]. The model is summarised in the Appendix. Coutinho went on to show that the model accurately predicts the waxing behaviour of diesel fuels, jet fuels [11] and crude oils [12]. The Coutinho model exists in two variants, the Wilson and Uniquac wax models. The Wilson model is simpler to apply as it treats the wax phase as a single solid solution of n-paraffins. The Uniquac model is more realistic in that it predicts that the wax phase splits into a number of coexisting solid solution phases; experimental evidence confirms this to be the case [13]. Both variants require the n-paraffin distribution of the oil to be specified; however, in cases where this is not available, it can be estimated from the total wax content using a method devised by Coutinho and Daridon [12]. The method can therefore make optimum use of whatever data are available for a particular oil.

Publisher

SPE

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3