Special Issue "Modeling of Fluids Flow in Unconventional Reservoirs"

Submission Deadline: 15 September 2022
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Guest Editors
Prof. Jianchao Cai, China University of Petroleum, China
Prof. Victor Manuel Calo, Curtin University, Australia
Dr. Shuangmei Zou, China University of Geosciences, China

Summary

Unconventional oil/gas resources have become significant contributors to global hydrocarbon production in the past two decades and continue to grow in importance for the next few decades. Due to the complex pore structures and geological storage in unconventional rocks, it remains challenging to describe and model fluid flow and transport in unconventional reservoirs. Therefore, new multiscale, multiphase, and multiphysics transport modelling methods are being continuously developed to describe these complex flows within them.

 

This Special Issue aims to cover the recent advances and challenges for modelling low and transport for unconventional reservoirs, including shale gas/oil, coal seam gas, tight gas/oil, gas hydrate, etc. Papers that apply cutting-edge technologies and novel techniques to investigate flow and transport in unconventional reservoirs, case studies, and comprehensive overviews are all welcome.

 

Potential topics include but are not limited to:

 

Reservoir characterization

• New simulation methods

Multiphase flow

Multiscale modelling

Multiphysics modelling

• immiscible flow

Rock–fluid interface interactions

• Pore network modelling/Lattice Boltzmann modelling

Machine learning and big data applications for unlocking new insights in unconventional reservoirs production



Published Papers
  • A Data-Driven Oil Production Prediction Method Based on the Gradient Boosting Decision Tree Regression
  • Abstract Accurate prediction of monthly oil and gas production is essential for oil enterprises to make reasonable production plans, avoid blind investment and realize sustainable development. Traditional oil well production trend prediction methods are based on years of oil field production experience and expertise, and the application conditions are very demanding. With the rapid development of artificial intelligence technology, big data analysis methods are gradually applied in various sub-fields of the oil and gas reservoir development. Based on the data-driven artificial intelligence algorithm Gradient Boosting Decision Tree (GBDT), this paper predicts the initial single-layer production by considering geological data, fluid PVT… More
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  • Deep-Learning-Based Production Decline Curve Analysis in the Gas Reservoir through Sequence Learning Models
  • Abstract Production performance prediction of tight gas reservoirs is crucial to the estimation of ultimate recovery, which has an important impact on gas field development planning and economic evaluation. Owing to the model’s simplicity, the decline curve analysis method has been widely used to predict production performance. The advancement of deep-learning methods provides an intelligent way of analyzing production performance in tight gas reservoirs. In this paper, a sequence learning method to improve the accuracy and efficiency of tight gas production forecasting is proposed. The sequence learning methods used in production performance analysis herein include the recurrent neural network (RNN), long… More
  •   Views:1208       Downloads:556        Download PDF