Data Science and Machine Learning Applications in Subsurface Engineering
This book covers unsupervised learning, supervised learning, clustering approaches, feature engineering, explainable AI and multioutput regression models for subsurface engineering problems. Processing voluminous and complex data sets are the primary focus of the field of machine learning (ML). ML...
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| Format: | eBook |
| Language: | Inglés |
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CRC Press (Unlimited)
2023
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| See on Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009869097106719 |
Table of Contents:
- Enhancing drilling fluid lost-circulation prediction : using model agnostic and supervised machine learning
- Application of a novel stacked ensemble model in predicting total porosity and free fluid index via wireline and NMR logs
- Compressional and shear sonic log determination : using data-driven machine learning techniques
- Data-driven virtual flow metering systems
- Data-driven and machine learning approach in estimating multi-zonal ICV water injection rates in a smart well completion
- Carbon dioxide low salinity water alternating gas (CO2 LSWAG) oil recovery factor prediction in carbonate reservoir : using supervised machine learning models
- Improving seismic salt mapping through transfer learning using a pre-trained deep convolutional neural network : a case study on groningen field
- Super-vertical-resolution reconstruction of seismic volume using a pre-trained deep convolutional neural network : a case study on opunake field
- Petroleum reservoir characterisation : a review from empirical to computer-based applications
- Artificial lift design for future inflow and outflow performance for jubilee oilfield : using historical production data and artificial neural network models
- Modelling two-phase flow parameters utilizing data-driven machine-learning methodology.
