摘要
南堡35-2油田是海上稠油油田,具有原油黏度高、油层岩心渗透率高、胶结疏松和非均质性严重等特点,水驱开发过程中指进现象严重,难以获得较高的水驱采收率。本文利用仪器检测和物理模拟实验优选调驱剂。依据优选结果,利用物理模拟参数为学习样本的BP神经网络预测方法和经济评价方法,对调驱方案的平均浓度和段塞尺寸进行优化。室内实验分析和优化结果表明,对于该油田A21井,最佳段塞尺寸和平均质量浓度分别为0.1 PV和2200 mg/L。聚合物凝胶调驱矿场试验过程中,注入压力明显上升,增油效果显著。在调驱有效期内,在不考虑自然递减情况下受效油井增油27639.59 m3,取得了明显的增油降水效果,经济效益十分明显。
Nanbao 35-2 was characteristic of the high viscosity, high permeability, serious heterogeneity, etc. During water flooding process, because of the serious fingering phenomenon, it was difficult to obtain high water fllooding recovery. In the paper, the profile control agents were optimized by the methods of the instrument testing and core flooding tests. And then the average concentration and slug size were optimized by using the BP neural network prediction method based on core flooding parameters sample and economic evaluation. Compared with laboratory analysis data and optimizafion results, for A21 well, the best slug size and average concentration were 0. 1 PV and 2200 rag/L, respectively. During the profile control process, injection pressure increased significantly. In the case without considering the natural decline rate, the oil wells incremental oil production was 27639.59 m3. The measurements achieved an obvious oil increment and water reduction. And economic benefits are very obvious.
出处
《油田化学》
CAS
CSCD
北大核心
2012年第2期206-211,共6页
Oilfield Chemistry
基金
国家重大专项课题"砾石充填防砂完井深部调剖(驱)技术研究"(项目编号2008ZX05024-004-007-004)
关键词
海上稠油油田
物理模拟
BP神经网络
聚合物凝胶
增油效果
： offshore heavy oil oilfield
core flooding test
BP neural network
polymer gel
incremental oil effects
