现代信息科技2025，Vol.9Issue(5)：33-38,6.DOI:10.19850/j.cnki.2096-4706.2025.05.006
基于CNN-LSTM的油田产量预测模型
Oilfield Production Forecasting Model Based on CNN-LSTM
摘要
Abstract
Oilfield production forecasting is a fundamental task for formulating production and development scheme.With the increasing volume of data,traditional methods face significant challenges.Establishing forecasting models through Machine Learning has become a more accurate and efficient method.This paper combines two fundamental models,Convolutional Neural Networks(CNN)and Long Short-Term Memory(LSTM),to construct a CNN-LSTM model.It extracts some prominent features from the oilfield data by CNN,and uses the features as input to construct the LSTM time series dataset.It reveals the hidden relationship between features and spatial interaction in data,and then enhances the accuracy of the forecasting results.In addition,the experiment results demonstrate that compared with standalone LSTM model and CNN model,the CNN-LSTM model has significant advantages in terms of forecasting performance.It can forecast the oilfield production data more accurately and provide reliable data support for the subsequent oilfield development.关键词
CNN-LSTM/机器学习/产量预测Key words
CNN-LSTM/Machine Learning/production forecasting分类
计算机与自动化引用本文复制引用
张汉文,辛显康,喻高明..基于CNN-LSTM的油田产量预测模型[J].现代信息科技,2025,9(5):33-38,6.基金项目
国家自然科学基金青年科学基金(52104020) （52104020）
