Improved porosity estimation in complex carbonate reservoirs using hybrid CRNN deep learning model
Authors:
- 27 July 2024
- journal article
- research article
- Published by Springer Nature in Earth Science Informatics
- Vol. 17 (5) , 4773-4790
Abstract
No abstract availableKeywords
All Related Versions
This publication has 40 references indexed in Scilit:
- Estimate Petrophysical Properties by Using Machine Learning Methods2023
- Modern Deep Learning for Tabular Data2023
- Porosity prediction using Fuzzy SVR and FCM SVR from well logs of an oil field in south of IranActa Geophysica, 2022
- Pore Pressure Prediction by Empirical and Machine Learning Methods Using Conventional and Drilling Logs in Carbonate RocksRock Mechanics and Rock Engineering, 2022
- Automated porosity estimation using CT-scans of extracted core dataComputational Geosciences, 2022
- Fuzzy support vector regression for permeability estimation of petroleum reservoir using well logsActa Geophysica, 2021
- Multi-attribute Selection for Salt Dome Detection Based on SVM and MLP Machine Learning TechniquesNatural Resources Research, 2021
- A survey of the recent architectures of deep convolutional neural networksArtificial Intelligence Review, 2020
- A general approach for porosity estimation using artificial neural network method: a case study from Kansas gas fieldStudia Geophysica et Geodaetica, 2015
- Carbonate Reservoir Rocks2015