复合渗透率测井评价方法在砂砾岩稠油油藏的应用
  <sup>*</sup>——以克拉玛依油田某区八道湾组为例

doi:10.11867/j.issn.1001-8166.2015.07.0773

地球科学进展 ›› 2015, Vol. 30 ›› Issue (7): 773 -779. doi: 10.11867/j.issn.1001-8166.2015.07.0773

复合渗透率测井评价方法在砂砾岩稠油油藏的应用 ^*——以克拉玛依油田某区八道湾组为例

陈科贵 ¹, 陈旭 ^1*, 张家浩 ²

1.西南石油大学地球科学与技术学院,四川成都 610500;
2.中石油新疆油田分公司勘探开发研究院,新疆克拉玛依 834000

收稿日期:2015-01-26 出版日期:2015-07-20
通讯作者: 陈旭（1990-）,男,四川南充人,硕士研究生,主要从事测井解释与储层评价研究.E-mail:jingjiuxi@qq.com
基金资助:
国家自然科学基金项目“四川盆地油钾兼探的地球物理评价方法研究”（编号：41372103）资助

Combined Methods of Permeability Logging Evaluate in Glutenite Reservoirs——A Case Study of Badaowan formation in Karamay Oilfield

Chen Kegui ¹, Chen Xu ¹, Zhang Jiahao ²

1.School of Geoscience and Technology, Southwest Petroleum University, Chengdu 610500, China;
2.Research Institute of Exploration and Development, PetroChina Xinjiang Oilfield Company, Karamay 834000, China

Received:2015-01-26 Online:2015-07-20 Published:2015-07-20

全文 ( 2 ) 下载PDF ( 748 )

砂砾岩储层孔隙结构复杂、非均质性强,在渗透率计算方面传统的测井解释方法误差较大,目前还没有经典的计算砂砾岩渗透率的测井解释模型。以克拉玛依油田某区八道湾组砂砾岩稠油油藏为例,首先在微观层面上分析了渗透率的主控因素。其次根据本地区的实际情况建立了3套渗透率测井解释方法：一是在前人研究基础上改进了多元回归模型;二是在岩性识别的基础上分不同岩性建立了渗透率模型;三是利用BP神经网络进行了渗透率的预测。最后对传统的经验公式与文中的3种方法进行检验。结果表明,比起传统的经验公式和多元回归模型,基于不同岩性的渗透率模型与BP神经网络在实际应用中效果更好,较大幅度地提高了测井解释精度,在非均质性强的砂砾岩油藏中具有更好的应用前景。

关键词: BP神经网络, 判别分析, 砂砾岩, 渗透率模型

Glutenite reservoir has complicated pore structure and strong heterogeneity and traditional logging interpretation methods often have nonignorable calculation errors in permeability evaluation. Thus, there is no classic model to calculate the permeability of glutenite. This Paper takes Badaowan group formation of Karamay Oilfield as an study example. Firstly, the main controlling factors of permeability were analyzed at the micro level. Secondly, three sets of permeability logging interpretation methods were built according to the study area’s situation: the first is the improved multivariate regression model based on the predecessors’ research; the second is the different permeability models of different lithology based on lithology identification; the third is the BP neural network. Finally the verification results showed that compared with the traditional empirical formula and the multivariate regression model, permeability model based on different lithology and the BP neural network had better effects in the practical application, with significant improvements in the precision of logging interpretation and better application prospects in strong heterogenous glutenite reservoir.

Key words: Permeability model, BP neural network, Glutenite, Discriminant analysis.

中图分类号:

P618.13

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