Prediction of minimum miscibility pressure in CO2 flooding based on general regression neural network

XUE Yanxia; LIAO Xinwu; ZHAO Chunming; HUO Chunliang; ZHANG Rucai

doi:10.3969/j.issn.1673-8926.2012.01.021

Lithologic Reservoirs >

2012 , Vol. 24 >Issue 1: 108 - 111

DOI: https://doi.org/10.3969/j.issn.1673-8926.2012.01.021

Prediction of minimum miscibility pressure in CO2 flooding based on general regression neural network

XUE Yanxia ,
LIAO Xinwu ,
ZHAO Chunming ,
HUO Chunliang ,
ZHANG Rucai

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College of Petroleum Engineering， China University of Petroleum， Dongying 257061， China

Online published: 2012-02-20

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Abstract

To reduce the inconvenience and errors in traditional prediction methods for minimum miscibility pressure （MMP）, a general regression neural network（GRNN） model was established for MMP prediction in CO₂ flooding. The main factors affecting CO₂ MMP, such as reservoir temperature, mole percentage of oil components (volatile and intermediate） and molecular weight of C5+ , were employed as the input variables of the GRNN, and the CO₂ MMP was used as the output variable. To evaluate the advantage of the new method, the predicted results were compared between the GRNN model and the traditional empirical formula. The results show that the GRNN model is feasible for CO₂ MMP prediction, and also has the characteristics of good precision, fast convergence, wide applicability and simple application.

Key words： stochastic modeling; fluvial facies; experimental design; Monte-Carlo calculation; uncertainty analysis

Cite this article

XUE Yanxia , LIAO Xinwu , ZHAO Chunming , HUO Chunliang , ZHANG Rucai . Prediction of minimum miscibility pressure in CO2 flooding based on general regression neural network[J]. Lithologic Reservoirs, 2012 , 24(1) : 108 -111 . DOI: 10.3969/j.issn.1673-8926.2012.01.021

References

［1］ Alvarado V，Manrique E． Enhanced oil recovery：An update review［J］． Energies，2010，3（9）：1529-1575．
［2］夏为卫，王新海，雷娟青．低渗透油藏注二氧化碳气体的井网优选研究———以松辽盆地南部 H 油田 L 油藏为例［J］．岩性油气藏，2009，21（4）：105-107．
［3］王锦昌，杜建芬，曾铮，等．二氧化碳含量对天然气性质影响的实验研究［J］．岩性油气藏，2011，23（3）：115-118．
［4］赵明国，周海菲，王谦．原油与CO2驱最小混相压力预测方法研究［J］．西部探矿工程，2007，19（12）：60-62．
［5］郝永卯，陈月明，于会利．CO2驱最小混相压力的测定与预测［J］．油气地质与采收率，2005，12（6）：64-66．
［6］董长虹． MATLAB 神经网络与应用［M］．北京：国防工业出版社，2007：125-139．
［7］杨建，杨程博，张岩，等．基于改进神经网络的渗透率预测方法［J］．岩性油气藏，2011，23（1）：98-102．
［8］张顶学，廖锐全，徐永高，等．基于广义回归神经网络的压裂井砂量优化［J］．石油天然气学报，2006，28（3）：88-90．
［9］ Shokir E M． CO2-oil minimum miscibility pressure model for impure and pure CO2 streams［J］． Journal of Petroleum Science and Engineering，2007，58（1/2）：173-185．
［10］陈钢花，杨叶．利用常规测井资料确定黏土矿物含量的方法研究［J］．岩性油气藏，2011，23（2）：109-113．
［11］ Shahvaranfard A，Moradi B，Tahami S A，et al． Evaluation of different flooding scenarios as enhanced oil recovery method in a fractured reservoir：A case study［J］． Brazilian Journal of Petroleum and Gas，2009，3（3）：113-124．
［12］ Aleidan A A S． Experimental and simulation studies to evaluate the improvement of oil recovery by different modes of CO2 injection in carbonate reservoirs ［D］． Texas：Texas A & M University，2010：65-68．
［13］ Al-Ajmi M，Alomair O，Elsharkawy A． Planning miscibility test and gas injection projects for four major Kuwaiti reservoirs ［R］． SPE 127537，2009．
［14］ Al-Netaifi A S． Experimental investigation of CO2-miscible oil recovery at different conditions［D］． Riyadh：King Saud University，2008：9-16．
［15］熊钰，孙雷，李士伦，等．辽河稀油区注CO2提高采收率潜力实验评价［J］．西南石油学院学报，2001，23（2）：30-32．
［16］王涛，颜明，郭海波．一种新的回归分析方法———响应曲面法在数值模拟研究中的应用［J］．岩性油气藏，2011，23（2）：100-104．

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