Well Logging Verification Using Machine Learning Algorithms
| Autor: | Alla Andrianova, Semen Budennyy, Artem Tsanda, Alexander Bukharev |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Boosting (machine learning)
business.industry Computer science Well logging Logging 02 engineering and technology Machine learning computer.software_genre Convolutional neural network Automation Identification (information) Oil reserves 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Oil field business computer |
| Zdroj: | 2018 International Conference on Artificial Intelligence Applications and Innovations (IC-AIAI). |
| DOI: | 10.1109/ic-aiai.2018.8674435 |
| Popis: | Well logging analysis plays a crucial role in the design of oil field development. The analysis determines the location of the reservoir and its thickness, which defines directly the estimation of oil reserves. Present paper proposes an approach to the automation and verification of logging studies, namely reservoir identification along the wellbore, based on machine learning methods. Logging data for training were taken from the real oil field in Western Siberia. The paper describes approach used for data pre-processing and key aspects of the data. In this study, we considered two methodologies for reservoir prediction: by sample with the help of gradient busting method and by interval based on one dimensional convolutional neural network. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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