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Application of mixed kernels function (MKF) based support vector regression model (SVR) for CO2 - Reservoir oil minimum miscibility pressure prediction
Abstract Carbon dioxide (CO2) injection into oil reservoirs is considered a mature enhanced oil recovery (EOR) technique for conventional reservoirs. The local displacement efficiency of the CO2-EORExpand
A deep learning approach to anomaly detection in geological carbon sequestration sites using pressure measurements
Abstract Carbon capture and storage (CCS) has been extensively investigated as a potential engineering measure to reduce anthropogenic carbon emission to the atmosphere. Real-time monitoring of theExpand
Combining Physically-Based Modeling and Deep Learning for Fusing GRACE Satellite Data: Can We Learn from Mismatch?
We develop and apply deep convolutional neural network (CNN) models to learn the spatiotemporal patterns of mismatch between TWS anomalies (TWSA) derived from GRACE and those simulated by NOAH, a widely used land surface model. Expand
Building complex event processing capability for intelligent environmental monitoring
We present a complex event processing (CEP) engine for detecting anomalies in real time, and demonstrates it using a series of real monitoring data from the geological carbon sequestration domain. Expand
Dew point pressure prediction based on mixed-kernels-function support vector machine in gas-condensate reservoir
Abstract Dew point pressure, at which the first condensate liquid comes out of solution in gas condensate reservoir, is a vital parameter for fluid characterization, field development, reservoirExpand
Application of a convolutional neural network in permeability prediction: A case study in the Jacksonburg-Stringtown oil field, West Virginia, USA
Permeability is a critical parameter for understanding subsurface fluid flow behavior, managing reservoirs, enhancing hydrocarbon recovery, and sequestering carbon dioxide. In general, permeabilityExpand
Predicting field production rates for waterflooding using a machine learning-based proxy model
We design and implement a proxy model using a conditional deep convolutional generative neural network (cDC-GAN), which can be used to quickly calculate the dynamic fluid distribution of a heterogeneous reservoir under waterflooding. Expand
A case study on homogeneous and heterogeneous reservoir porous media reconstruction by using generative adversarial networks
Generative adversarial networks (GANs) is used for generating the synthetic micro representations of porous rock by acquiring non-linear statistical information from the real 3D rock images in an unsupervised learning scheme. Expand