Data-Driven Based Modelling of Pressure Dynamics in Multiphase Reservoir Model-Reference-Cited by-同舟云学术

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Data-Driven Based Modelling of Pressure Dynamics in Multiphase Reservoir Model

Published:2022-04-20 Issue: Volume: Page:
ISSN:
Container-title:2022 UKACC 13th International Conference on Control (CONTROL)
language:
Short-container-title:

Author:

Ali Aliyuda¹,Diala Uchenna²,Guo Lingzhong³

Affiliation:

1. Birmingham City University,Department of Computing and Data Science,UK

2. University of Derby,Department of Electrical and Electronic Engineering,Derby,UK

3. University of Sheffield,Department of Automatic Control and Systems Engineering,Sheffield,UK

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9781435/9781358/09781447.pdf?arnumber=9781447

Reference19 articles.

1. Data-driven based machine learning models for predicting the deliverability of underground natural gas storage in salt caverns

2. First Principles and Machine Learning Virtual Flow Metering: A Literature Review

3. Data-driven based investigation of pressure dynamics in underground hydrocarbon reservoirs

4. Automatic well test interpretation based on convolutional neural network for infinite reservoir

5. Well predictive performance of play-wide and Subarea Random Forest models for Bakken productivity

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Towards more accurate and explainable supervised learning-based prediction of deliverability for underground natural gas storage;Applied Energy;2022-12

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