An ensemble transfer learning strategy for production prediction of shale gas wells
W Niu, Y Sun, X Zhang, J Lu, H Liu, Q Li, Y Mu - Energy, 2023 - Elsevier
In order to overcome the training data insufficient problem of model for shale gas wells
production prediction in new block, this study proposes a transfer learning strategy of …
production prediction in new block, this study proposes a transfer learning strategy of …
A deep-learning-based graph neural network-long-short-term memory model for reservoir simulation and optimization with varying well controls
H Huang, B Gong, W Sun - SPE Journal, 2023 - onepetro.org
A new deep-learning-based surrogate model is developed and applied for predicting
dynamic oil rate and water rate with different well controls. The surrogate model is based on …
dynamic oil rate and water rate with different well controls. The surrogate model is based on …
[HTML][HTML] A novel framework for predicting non-stationary production time series of shale gas based on BiLSTM-RF-MPA deep fusion model
Shale gas, as an environmentally friendly fossil energy resource, has gained significant
commercial development and shows immense potential. However, accurately predicting …
commercial development and shows immense potential. However, accurately predicting …
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 …
Opportunities in Utilization of Digital Twins in Unconventional Gas Fields: Enhancing Efficiency and Performance through Virtual Replication
N Alsulaiman, K Reddy, U Odi, J Rabines… - International Petroleum …, 2024 - onepetro.org
Objective Digital twin technology offers significant opportunities for the utilization and
optimization of unconventional gas fields by providing virtual replicas of physical assets and …
optimization of unconventional gas fields by providing virtual replicas of physical assets and …
Enhanced hydrocarbon production forecasting combining machine learning, transfer learning, and decline curve analysis
GM Mask, X Wu, C Nicholson - Gas Science and Engineering, 2025 - Elsevier
Accurate production forecasting for multi-fractured horizontal wells (MFHW) in
unconventional resources is essential for decision-making during early-stage field …
unconventional resources is essential for decision-making during early-stage field …
Integration of artificial neural network and fast marching method for rate prediction in unconventional reservoir
Unconventional reservoirs, such as shale formations and tight sands, have become crucial
in worldwide oil and gas production. The complicated structure of these reservoirs, which …
in worldwide oil and gas production. The complicated structure of these reservoirs, which …
Application of Machine Learning for Productivity Prediction in Tight Gas Reservoirs
M Fang, H Shi, H Li, T Liu - Energies, 2024 - mdpi.com
Accurate well productivity prediction plays a significant role in formulating reservoir
development plans. However, traditional well productivity prediction methods lack accuracy …
development plans. However, traditional well productivity prediction methods lack accuracy …
Use of Transfer Learning in Shale Production Forecasting
Production forecasting is vital in the oil and gas sector, empowering engineers with insights
for effective reservoir management. This paper introduces the concept of Transfer Learning …
for effective reservoir management. This paper introduces the concept of Transfer Learning …
Machine Learning Assisted Forecasting of Reservoir Performance
E Artun - Machine Learning Applications in Subsurface Energy …, 2022 - api.taylorfrancis.com
Forecasting reservoir performance is an integral part of economic evaluation studies for a
hydrocarbon asset as the process quantifies the recoverable volume of hydrocarbons over a …
hydrocarbon asset as the process quantifies the recoverable volume of hydrocarbons over a …