A review on optimization algorithms and surrogate models for reservoir automatic history matching
Reservoir history matching represents a crucial stage in the reservoir development process
and purposes to match model predictions with various observed field data, including …
and purposes to match model predictions with various observed field data, including …
A dual-porosity flow-net model for simulating water-flooding in low-permeability fractured reservoirs
The physics-based data-driven flow-network models with high computational efficiency have
received great attention as the promising surrogate models for reservoir numerical …
received great attention as the promising surrogate models for reservoir numerical …
Extension of fourier neural operator from three-dimensional (x, y, t) to four-dimensional (x, y, z, t) subsurface flow simulation
Numerical simulation of subsurface flow in porous media is crucial for various geoscience
applications. However, conducting numerical simulations for such problems, particularly in …
applications. However, conducting numerical simulations for such problems, particularly in …
A Physics-Informed Spatial-Temporal Neural Network for Reservoir Simulation and Uncertainty Quantification
Surrogate models play a vital role in reducing computational complexity and time burden for
reservoir simulations. However, traditional surrogate models suffer from limitations in …
reservoir simulations. However, traditional surrogate models suffer from limitations in …
A maximum entropy deep reinforcement learning method for sequential well placement optimization using multi-discrete action spaces
Well placement optimization is a crucial method to solve the planar conflicts in reservoir
development, mainly to determine the optimal well locations and drilling sequence to …
development, mainly to determine the optimal well locations and drilling sequence to …
Optimization of geological carbon storage operations with multimodal latent dynamic model and deep reinforcement learning
Identifying the time-varying control schemes that maximize storage performance is critical to
the commercial deployment of geological carbon storage (GCS) projects. However, the …
the commercial deployment of geological carbon storage (GCS) projects. However, the …
Transfer Learning in Subsurface Flow Surrogate Model with Physics-Guided Neural Network
HB Cheng, JH Qiao, YC Wei, SC Li, P Zeng… - SPE Annual Technical …, 2024 - onepetro.org
It is a great challenge for reservoir engineers to accurately and quickly model the subsurface
flow surrogate for oil and gas reservoirs. The traditional numerical simulation methods are …
flow surrogate for oil and gas reservoirs. The traditional numerical simulation methods are …
Deep Learning Surrogate Model Based on Residual Bottleneck Block for History Matching
T Gao, J Zang, L Zhang, X Wang… - … Conference on New …, 2024 - ieeexplore.ieee.org
History matching is a key link to predict the development performance of oil field by using
reservoir numerical simulation, which directly determines the reliability of numerical …
reservoir numerical simulation, which directly determines the reliability of numerical …
ConvNeXt Block-Based Deep Learning Surrogate Model for Polymer-Flooding Reservoir Simulation
Surrogate modeling has emerged as a crucial strategy for time and cost savings in reservoir
development. However, the majority of research on surrogate modeling has been …
development. However, the majority of research on surrogate modeling has been …
Multimodal Surrogate-Enhanced Deep Reinforcement Learning for Policy Optimization of Subsurface Energy Systems
Efficient optimization of subsurface energy production and carbon storage is crucial for
environmental, energy, and economic security. However, it is confronted with huge …
environmental, energy, and economic security. However, it is confronted with huge …