采油井措施增产效果预测模式优化研究  

Research on Optimization of Prediction Model of Stimulation Effect of Production Well Measures

作　　者：刘伟[1] LIU Wei

机构地区：[1]延长油田股份有限公司吴起采油厂,陕西延安717600

出　　处：《化工设计通讯》2025年第4期18-20,共3页Chemical Engineering Design Communications

摘　　要：采油井措施增产效果预测对油田高效开发具有重要意义。基于机器学习方法,结合地质储层特征、生产动态数据和措施参数,构建了一种新的增产效果预测模型。以胜利油田为例,采用集成学习技术,融合随机森林和梯度提升树算法,对183口典型采油井的数据进行分析,预测模型的平均相对误差为8.2%,较传统多元线性回归方法提高15.6%的准确率。结果表明,该模型能有效优化方案设计,提高增产效果预测的准确性,为油田精细化管理和高效开发提供了决策支持。The prediction of stimulation effect of production well measures is of great significance to the effi cient development of oil fi eld.Based on machine learning method,a new prediction model of stimulation effect was constructed by combining geological reservoir characteristics,production dynamic data and measure parameters.Taking Shengli Oilfi eld as an example,integrated learning technology,random forest and gradient lift tree algorithm are used to analyze the data of 183 typical production Wells.The average relative error of the prediction model is 8.2%,which is 15.6%higher than the traditional multiple linear regression method.The results show that the model can effectively optimize the scheme design,improve the accuracy of production stimulation prediction,and provide decision support for fi ne management and effi cient development of oil fi elds.

关 键 词：采油井 增产措施 效果预测 机器学习 模型优化 

分 类 号：TE357[石油与天然气工程—油气田开发工程]