Transfer learning with recurrent neural networks for long-term production forecasting in unconventional reservoirs
Robust production forecasting allows for optimal resource recovery through efficient field
management strategies. In hydraulically fractured unconventional reservoirs, the physics of …
management strategies. In hydraulically fractured unconventional reservoirs, the physics of …
Physics-guided deep learning for improved production forecasting in unconventional reservoirs
The complexity of physics-based modeling of fluid flow in hydraulically fractured
unconventional reservoirs, together with the abundant data from repeated factory-style …
unconventional reservoirs, together with the abundant data from repeated factory-style …
Improving the accuracy of short-term multiphase production forecasts in unconventional tight oil reservoirs using contextual Bi-directional long short-term memory
To improve the accuracy of short-term multiphase production forecasts with one-step-ahead
predictions, a Contextual Bi-directional Long Short-Term Memory (C–Bi-LSTM) was …
predictions, a Contextual Bi-directional Long Short-Term Memory (C–Bi-LSTM) was …
Embedding physical flow functions into deep learning predictive models for improved production forecasting
Data-driven methods have surged in popularity due to increased field development and data
collection effort in the last two decades, and partly because flow physics in hydraulically …
collection effort in the last two decades, and partly because flow physics in hydraulically …
Rapid High-Fidelity Forecasting for Geological Carbon Storage Using Neural Operator and Transfer Learning
Carbon sequestration is a promising technique to minimize the emission of CO2 to the
atmosphere. However, the computational time required for CO2 forecasting using …
atmosphere. However, the computational time required for CO2 forecasting using …
Residual-Enhanced Physic-Guided Machine Learning with Hard Constraints for Subsurface Flow in Reservoir Engineering
H Cheng, Y He, P Zeng… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Subsurface flow is the core of reservoir engineering. Research on subsurface flow problems
can enhance our understanding of the development status of oilfields, thus enabling the …
can enhance our understanding of the development status of oilfields, thus enabling the …
Neural Network-Assisted Clustering for Improved Production Predictions in Unconventional Reservoirs
Given sufficiently extensive data, deep-learning models can effectively predict the behavior
of unconventional reservoirs. However, current approaches in building the models do not …
of unconventional reservoirs. However, current approaches in building the models do not …
Transfer Learning with Prior Data-Driven Models from Multiple Unconventional Fields
Constructing reliable data-driven models to predict well production performance (eg,
estimated ultimate recovery, cumulative production, production curves, etc.) for …
estimated ultimate recovery, cumulative production, production curves, etc.) for …
Application of Neural Operator Technique for Rapid Forecast of CO2 Pressure and Saturation Distribution
Geological carbon storage (GCS) is the most popular technique for sequestering CO2.
Usually, GCS is modeled using commercial numerical simulators to make CO2 forecasts for …
Usually, GCS is modeled using commercial numerical simulators to make CO2 forecasts for …
Transfer learning with multiple aggregated source models in unconventional reservoirs
Developing a reliable deep learning model for new unconventional reservoirs, is often
constrained by the limited number of wells available. Transfer learning is a useful approach …
constrained by the limited number of wells available. Transfer learning is a useful approach …