摘要
确定增油量是聚合物驱方案技术经济可行性评价的关键,也是聚合物驱方案在现场实施后措施调整的重要依据。运用BP神经网络训练学习算法,建立基于早期注聚油藏的水驱产油量预测模型,得到早期注聚增油量的评价基准,解决了海上油田进行早期聚合物驱试验时,无法采用常规方法计算早期注聚增油量的难题。经验证,该方法计算得到的增油量与实际增油量间误差仅为0.88%。实例应用证明,该方法具有较高的准确度,为渤海油田早期注聚提供了一种切实可行的增油量评价新方法,对海上早期注聚技术经济可行性和实施效果评价及措施调整具有重要指导意义。
Determination of oil production increase is one of the keys for evaluation of lechnical and economic feasibility of polymer flooding program,as well as an important basts for measures control after field execution of polymer flooding program.By use of BP neural network learning algorithm,modeling for walerflooding nil production prediction based on early polymer injection reservoir in conducted.As a result,evaluation criteria for oil production increase during early polymer flooding are obtained,and the difficult problem of failure in calculation of oil production increase during early polymer flooding by conventional methods when early pilot of polymer flooding is carried on offshore has been tackled.After verification,,the oil production increase calculated by this method has a difference of 0.88%to the actual figure.From application case study,it is proven that this method has high accuracy,providing a practical new method for oil production increase evaluation during early polymer flooding in the Bohai oilfield and being an important guidance for evaluation of lechnical and economic feasibility and implementation results during early polymer flooding offshore.
出处
《特种油气藏》
CAS
CSCD
北大核心
2014年第1期106-109,156,共4页
Special Oil & Gas Reservoirs
基金
国家科技重大专项"海上稠油高效开发新技术"(2011ZX05024-004)
关键词
海上油田
早期注聚
增油量
BP神经网络
应用评价
offshore oilfield
early polymer flooding
till production increase
BP neural network
evaluation of application
