西南石油大学学报(自然科学版) ›› 2009, Vol. 31 ›› Issue (6): 51-55.DOI: 10.3863/j.issn.1674-5086.2009.06.010

• 地质勘探 • 上一篇    下一篇

基于测井资料的低孔低渗储层产能预测研究

王智 许江文 谷斌   

  1. 中国石油新疆油田分公司勘探公司,新疆 克拉玛依 834000
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2009-12-20 发布日期:2009-12-20

LOW POROSITY AND LOW PERMEABILITY RESERVOIR DELIVERABILITY BASED ON WELL LOGGING DATA

WANG Zhi Xu Jiang-wen Gu bin   

  1. Exploration Company of Xinjiang Oilfield Company,CNPC,Karamay Xinjiang 834000,China)JOURNAL OF SOUTHWEST PETROLEUM UNIVERSITY(SCIENCE & TECHNOLOGY EDITION),VOL.31,NO.6,51-55,2009(ISSN 1674-5086,in Chinese
  • Received:1900-01-01 Revised:1900-01-01 Online:2009-12-20 Published:2009-12-20

摘要: 针对低孔低渗储层产能预测模型受敏感因素影响大,主次因素在低产情况下区别不明显,产能预测评价相对较困难等实际问题,以准噶尔盆地夏子街地区夏77井、夏79井为例,开展低孔低渗储层产能预测的前期试验研究,对多个产能预测模型及其适用条件进行了对比分析,从Darcy模型、Jones(1976)模型、Vogel/Harrison(1968)模型、裂缝模型中进行优选和调试。通过分析产能预测模型结果与测试结果在产量上的相关性,表明基于测井资料的预测结果与试油测试结果相当吻合,从而确认产能预测模型结果的可靠性。

关键词: 测井, 产能预测, 低孔低渗, 储层, 预测模型

Abstract: Aiming at the issues that low porosity and low permeability reservoir deliverability prediction model is impacted a lot by sensitivity factors,there is an unapparent difference between the main and secondary factors when deliverability is low,and deliverability prediction and evaluation are relatively difficult,the Xia wells 77 and 79 in Xiazhijie area,Junggar Basin are taken as examples to carry out pilot test for low porosity and low permeability reservoir deliverability prediction,different deliverability prediction models and the applicable conditions are summarized,further,the optimized Darcy model,Jones.et al model,Vogel/Harrison (1968) model and fracture model are selected to predict and test low porosity and low permeability reservoir deliverability.By analyzing the compatibility of predicted and tested deliverability,it is verified that predicted result based on well logging data matches well with test result,reliability of the prediction deliverability model can be confirmed.

Key words: well logging, deliverability prediction, low porosity and low permeability, reservoir, prediction model

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