石油钻采工艺2024，Vol.46Issue(4)：443-454,12.DOI:10.13639/j.odpt.202409051
试井数据监督式机器学习方法预测变质岩潜山储层产量
A supervised machine learning method based on well test data to predict reservoir production in buried-hill metamorphic rock
摘要
Abstract
The vertical and radial heterogeneity of multi-zone dispersed reservoirs is strong,which brings difficulties to reservoir evaluation.Well test dynamic characteristics machine learning technology,improve the evaluation level.This article analyzes the double logarithmic curves of 10 wells/layers in the complex reservoir of Bohai A structure's ancient,buried hill,and forms the characteristic curve of this complex reservoir.At the same time,through the fusion data analysis technology,the typical feature template of exploration and well testing in the complex reservoir of Bohai A structure's ancient,buried hill is formed by analyzing the well testing parameters of the complex reservoir.The study suggests that the ancient,buried hill test wells in Bohai A structure belong to the same reservoir,and it also proves that the continuity of the reservoir is good.At the same time,this article successfully achieved node-based production capacity prediction through regional well testing results and provided a production capacity prediction formula.The reverse validation of the prediction model achieved a compliance rate of 75%.Through this study,a dynamic interpretation template for the ancient buried hill test of Bohai A structure was provided,which achieved the identification of the dynamic change trend of the oil reservoir.The regional well testing method studied in this article is derived from traditional regional well testing techniques and effectively combines data analysis techniques,which can broaden the application scenarios of well testing.关键词
勘探开发/工程技术/变质岩潜山/产量预测/机器学习/数据使用/建模/定量评价产量Key words
exploration and development/Engineering Technology/Metamorphic rock buried hill/productivity forecast/machine learning/data use/model/Quantitative evaluation of productivity分类
能源科技引用本文复制引用
王雪飞,高科超,张兴华,罗鹏,杜连龙,刘境玄..试井数据监督式机器学习方法预测变质岩潜山储层产量[J].石油钻采工艺,2024,46(4):443-454,12.基金项目
中国海洋石油集团有限公司重大专项"海上深层/超深层油气勘探技术"(编号:CNOOC-KJGG2022-04-06). （编号:CNOOC-KJGG2022-04-06）
