Application of Machine Learning for Oilfield Data Quality Improvement
| Autor: | Alla Andrianova, Darya Serebryakova, Maxim Simonov, Semen Budennyy, Alexander Bukharev, Yuriy Bogdanov, Nikita Volkov, Artem Tsanda, Andrey Margarit, D. S. Perets |
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| Rok vydání: | 2018 |
| Předmět: |
Computer science
business.industry 020204 information systems Data quality 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing 02 engineering and technology Artificial intelligence Machine learning computer.software_genre business computer |
| Zdroj: | Day 2 Tue, October 16, 2018. |
| DOI: | 10.2118/191601-18rptc-ms |
| Popis: | The paper describes the principal possibility of using machine learning methods for verifying and restoring the quality of oilfield measurements. Basic methods for screening incorrect values have been given and approaches for solving three problems have been recommended: Correctness analysis of well logging data Quality control of physical and chemical fluid properties (PVT-studies) Separation between the base production and effect from well interventions (WI) to predict the performance of hydraulic fracturing (frac). The main deliverable is a set of algorithms based on machine learning methods, which allows to automatically process large volumes of field data. A number of approaches is proposed, including using modern methods of machine learning, to restore the missing values and the quality of algorithms operation. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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