石油与天然气地质 ›› 2009, Vol. 30 ›› Issue (2): 240-244.doi: 10.11743/ogg20090217

• 技术方法应用 • 上一篇    下一篇

基于确定性和随机模型的薄储层岩性预测

李军1, 熊利平1, 赵为永2, 刘建3   

  1. 1. 中国石油化工股份有限公司 石油勘探开发研究院,北京 100083;
    2. 中国石油天然气股份有限公司 青海油田分公司 勘探开发研究院,甘肃 敦煌 736202;
    3. 中国石油化工股份有限公司 胜利油田有限公司 滨南采油厂地质研究所,山东 滨州 256606
  • 收稿日期:2008-12-12 出版日期:2009-04-25 发布日期:2012-01-16
  • 基金资助:

    中国石油化工集团公司科技攻关项目(JP06001)

Lithology prediction of thin reservoirs based on deterministic and stochastic models

Li Jun1, Xiong Liping1, Zhao Weiyong2, Liu Jian3   

  1. 1. SINOPEC Exploration & Production Research Institute, Beijing 100083, China;
    2. Exploration and Development Research Institute of PetroChina Qinghai Oilfield, Dunhuang, Gansu 736202, China;
    3. Institute of Geology, Binnan Oil Production Plant of SINOPEC Shengli Oilfield Company, Binzhou, Shandong 25660, China
  • Received:2008-12-12 Online:2009-04-25 Published:2012-01-16

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摘要：

三角洲前缘地区一直是油气勘探的重点,但大多数情况下砂泥岩波阻抗值重叠,而且薄砂层较多,地震数据反演结果达不到分辨薄层的精度,致使无法准确预测砂体分布规律。从综合利用不同储层建模技术各自优势识别薄砂层的思路出发,针对三角洲前缘地区的特点提出了确定性建模和随机建模联合的方法,即首先用确定性建模得到声波波阻抗,在此基础上用随机模拟方法得到薄砂层的展布。采用的具体方法为地震与测井联合反演方法及马尔可夫链随机模拟方法,随后按照此方法对松辽盆地北部Y地区进行了试验,最终得到更为准确的薄砂体预测结果。

关键词: 岩性预测, 薄砂层, 确定性建模, 随机建模, 松辽盆地

Abstract:

The delta front is always one of the focuses for oil and gas exploration.However,reservoirs in delta front commonly occur as thin sandstone layers,which cannot be recognized accurately for the low resolution of seismic inversion,hence the distribution pattern of sandbodies could not be accurately predicted,leading to the difficulties for exploration and production.Taking advantage of various reservoir modeling techniques,this paper puts forward a new methodology of joint deterministic-stochastic modeling for lithology prediction of thin reservoirs in delta front areas.It first obtains acoustic impedance through deterministic modeling,and then predicts distribution of thin reservoirs through stochastic modeling.The specific methods used are joint seismic-log inversion and Markov chain modeling.Case study in the Y block of the northern Songliao Basin shows that more accurate prediction of thin sand layers could be achieved.

Key words: lithology prediction, thin sand layer, deterministic modeling, stochastic modeling, Songliao Basin

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