基于变异模式分解和集合学习模型的石油产量预测方法

IF 4.2 2区 地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Computers & Geosciences Pub Date : 2024-09-24 DOI:10.1016/j.cageo.2024.105734
Junyi Fang , Zhen Yan , Xiaoya Lu , Yifei Xiao , Zhen Zhao
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引用次数: 0

摘要

油井产量预测可以为油田生产和管理提供科学指导,是油田开发过程中不可或缺的一部分。在本研究中,首先通过变异模态分解(VMD)将油井的日产量数据分解为不同频率的成分,这种方法通常用于处理复杂的时间序列。然后将分解得到的新特征和其他过滤特征作为输入数据,分别用于 GRU、TCN 和 Transformer 模型的训练和预测。最后,使用混合法将这三个模型整合为基础学习器,具体来说,就是将这三个模型的预测输出作为 RBFNN 的新输入,用于训练和实现最终预测。基于中国塔里木地区某油田三口生产井的生产动态数据,将 VMD-Blending 模型与传统模型进行了比较。结果表明,VMD 能有效提高基础学习器的预测效果,而这些模型的预测效果在经过 Blending 集成后得到进一步提高,其各项预测指标均明显优于基础学习器和传统 SVM、RNN 模型。所提出的 VMD-Blending 模型在油井产能预测任务中表现良好,是一种准确有效的石油产量预测方法。
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An oil production prediction approach based on variational mode decomposition and ensemble learning model
Well production forecasting can provide scientific guidance for oilfield production and management, which is an indispensable part of the oilfield development process. In this study, the daily oil production data from oil wells are first decomposed into components with different frequencies by variational mode decomposition (VMD), which is usually used to process complex time series. The new features obtained from decomposition and other filtered features are then used as input data and for training and forecasting of GRU, TCN and Transformer models respectively. In the end, the three models are integrated as base learners using the Blending method, which specifically involves using the predicted outputs of the three models as new inputs to the RBFNN for training and realizing the final predictions. The VMD-Blending model was compared with traditional models based on the production dynamics data of three production wells in an oil field in the Tarim area, China. The result shows that VMD can effectively improve the prediction effect of the base learners, and the prediction effect of these models is further improved after Blending integration, and all of their prediction indexes are significantly better than those of the base learners and the traditional SVM and RNN models. The proposed VMD-Blending model has a well performance in the task of well capacity prediction and is an accurate and effective method for oil production prediction.
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来源期刊
Computers & Geosciences
Computers & Geosciences 地学-地球科学综合
CiteScore
9.30
自引率
6.80%
发文量
164
审稿时长
3.4 months
期刊介绍: Computers & Geosciences publishes high impact, original research at the interface between Computer Sciences and Geosciences. Publications should apply modern computer science paradigms, whether computational or informatics-based, to address problems in the geosciences.
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