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 …
Efficient and robust optimization for well patterns using a PSO algorithm with a CNN-based proxy model
One objective of the field development plan (FDP) is to optimize well patterns, that is, the
number and type of well (producers or injectors) and their locations, in order to maximize net …
number and type of well (producers or injectors) and their locations, in order to maximize net …
Reservoir automatic history matching: Methods, challenges, and future directions
Reservoir history matching refers to the process of continuously adjusting the parameters of
the reservoir model, so that its dynamic response will match the historical observation data …
the reservoir model, so that its dynamic response will match the historical observation data …
Efficient deep-learning-based history matching for fluvial channel reservoirs
In history matching, the calibration of a prior reservoir model is computationally expensive
because many forward reservoir simulation runs are required. Multiple posterior (or …
because many forward reservoir simulation runs are required. Multiple posterior (or …
A deep learning-based workflow for fast prediction of 3D state variables in geological carbon storage: A dimension reduction approach
Deep learning (DL) models are extensively used as surrogate models for high-fidelity
simulations of multiphase fluid flow in porous media at large scales, enabling fast forecasts …
simulations of multiphase fluid flow in porous media at large scales, enabling fast forecasts …
Machine-Learned Surrogate Models for Efficient Oil Well Placement Under Operational Reservoir Constraints
Recent predictive analytics and soft computing methods enhanced the exploration of new
hydrocarbon reserves. Machine learning (ML) has showed a promising role in oil and gas …
hydrocarbon reserves. Machine learning (ML) has showed a promising role in oil and gas …
Sequential field development plan through robust optimization coupling with CNN and LSTM-based proxy models
Well operation optimization is vital in maximizing the net present value (NPV) of the field
development plan (FDP). As well operation is a time-series scenario, this problem contains …
development plan (FDP). As well operation is a time-series scenario, this problem contains …
Data-driven three-phase saturation identification from X-ray CT images with critical gas hydrate saturation
This study proposes three-phase saturation identification using X-ray computerized
tomography (CT) images of gas hydrate (GH) experiments considering critical GH saturation …
tomography (CT) images of gas hydrate (GH) experiments considering critical GH saturation …
An improved method of reservoir facies modeling based on generative adversarial networks
Q Liu, W Liu, J Yao, Y Liu, M Pan - Energies, 2021 - mdpi.com
As the reservoir and its attribute distribution are obviously controlled by sedimentary facies,
the facies modeling is one of the important bases for delineating the area of high-quality …
the facies modeling is one of the important bases for delineating the area of high-quality …
Assimilation of geophysics-derived spatial data for model calibration in geologic co2 sequestration
Uncertainty in geological models usually leads to large uncertainty in the predictions of risk-
related system properties and/or risk metrics (eg, CO 2 plumes and CO 2/brine leakage …
related system properties and/or risk metrics (eg, CO 2 plumes and CO 2/brine leakage …