Abstract: The development of bottom water reservoir is affected by strong bottom water, low height of oil column, high viscosity of formation crude oil, and complex distribution of separation interlayer, etc. In order to solve the shortcomings of current conventional methods, such as large amount of multi⁃factor data analysis and application limitations, a new method based on BP neural network data mining algorithm for predicting recoverable reserves of horizontal wells in bottom water reservoir is proposed, and its reliability is analyzed by means of mathematical model. Aiming at the static and dynamic data of bottom⁃water reservoir, the effective hidden information is fully mined, and a data⁃driven model for the prediction of recoverable reserves of bottom⁃water reservoir is constructed on the basis of the establishment of basic data set. The results of practical application show that this method can realize the quantitative analysis of the influencing factors and the limit of technical parameters of horizontal well development in bottom⁃water reservoir, the maximum error of recoverable reserves prediction is less than 8%, and the fitting effect is good, which can be further applied to the research of production performance, development limit and well location design of horizontal Wells in bottom⁃water reservoir.

Key words: Data mining, Bottom water reservoir, Horizontal well, Recoverable reserves, Production technology limitation

摘要： 底水油藏开发过程中受强底水、油柱高度低、地层原油黏度大、隔夹层分布复杂等因素影响，导致单井开发效果差异大。为解决目前常规方法所存在的多因素数据分析量大、应用局限性大等缺点，提出基于BP神经网络数据挖掘算法的底水油藏水平井可采储量预测新方法，通过数模机理模型分析了该方法的可靠性。针对底水油藏静动态资料，充分挖掘隐含其中的有效信息，在完成基础数据集建立的基础上，构建了基于数据驱动的底水油藏可采储量预测模型。实际应用结果表明，该方法实现了底水油藏水平井开发的影响因素和技术参数界限的定量分析，可采储量预测最大误差低于8%，拟合效果较好，可进一步应用于底水油藏水平井生产动态、开发界限、井位设计等方面。

关键词: 数据挖掘, 底水油藏, 水平井, 可采储量, 开发界限