A machine learning framework for rapid forecasting and history matching in unconventional reservoirs
Published 2021 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
A machine learning framework for rapid forecasting and history matching in unconventional reservoirs
Authors
Keywords
-
Journal
Scientific Reports
Volume 11, Issue 1, Pages -
Publisher
Springer Science and Business Media LLC
Online
2021-11-05
DOI
10.1038/s41598-021-01023-w
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Optimal planning and modular infrastructure dynamic allocation for shale gas production
- (2020) Bingyuan Hong et al. APPLIED ENERGY
- Modeling of multi-scale transport phenomena in shale gas production — A critical review
- (2020) Hui Wang et al. APPLIED ENERGY
- A geomechanical approach to casing collapse prediction in oil and gas wells aided by machine learning
- (2020) Nima Mohamadian et al. JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
- Hybrid machine learning algorithms to enhance lost-circulation prediction and management in the Marun oil field
- (2020) Mohammad Sabah et al. JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
- A machine learning approach to predict drilling rate using petrophysical and mud logging data
- (2019) Mohammad Sabah et al. Earth Science Informatics
- Multifidelity Information Fusion with Machine Learning: A Case Study of Dopant Formation Energies in Hafnia
- (2019) Rohit Batra et al. ACS Applied Materials & Interfaces
- Coupled thermo-hydro-mechanical analysis of stimulation and production for fractured geothermal reservoirs
- (2019) Sanbai Li et al. APPLIED ENERGY
- Optimization of enhanced oil recovery operations in unconventional reservoirs
- (2019) Andrés J. Calderón et al. APPLIED ENERGY
- Upscaled discrete fracture matrix model (UDFM): an octree-refined continuum representation of fractured porous media
- (2019) Matthew R. Sweeney et al. COMPUTATIONAL GEOSCIENCES
- Drilling rate prediction from petrophysical logs and mud logging data using an optimized multilayer perceptron neural network
- (2018) Mohammad Anemangely et al. Journal of Geophysics and Engineering
- Efficient Monte Carlo With Graph-Based Subsurface Flow and Transport Models
- (2018) D. O'Malley et al. WATER RESOURCES RESEARCH
- Advancing Graph-Based Algorithms for Predicting Flow and Transport in Fractured Rock
- (2018) H. S. Viswanathan et al. WATER RESOURCES RESEARCH
- Geomechanical parameter estimation from mechanical specific energy using artificial intelligence
- (2018) Mohammad Anemangely et al. JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
- Flow and Transport in Tight and Shale Formations: A Review
- (2017) Amgad Salama et al. GEOFLUIDS
- Recovery Efficiency in Hydraulically Fractured Shale Gas Reservoirs
- (2017) Maxian B. Seales et al. JOURNAL OF ENERGY RESOURCES TECHNOLOGY-TRANSACTIONS OF THE ASME
- Production-Pressure-Drawdown Management for Fractured Horizontal Wells in Shale-Gas Formations
- (2017) Ankit Mirani et al. SPE RESERVOIR EVALUATION & ENGINEERING
- dfn W orks : A discrete fracture network framework for modeling subsurface flow and transport
- (2015) Jeffrey D. Hyman et al. COMPUTERS & GEOSCIENCES
- Effect of advective flow in fractures and matrix diffusion on natural gas production
- (2015) Satish Karra et al. WATER RESOURCES RESEARCH
- Recovery rates, enhanced oil recovery and technological limits
- (2013) A. Muggeridge et al. PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
- From the Cover: Cozzarelli Prize Winner: Gas production in the Barnett Shale obeys a simple scaling theory
- (2013) T. W. Patzek et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- A Survey on Transfer Learning
- (2009) Sinno Jialin Pan et al. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
Add your recorded webinar
Do you already have a recorded webinar? Grow your audience and get more views by easily listing your recording on Peeref.
Upload NowAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started