Application of Machine Learning for Oilfield Data Quality Improvement (Russian)
- Alla Andrianova (Gazpromneft Science & Technology Centre) | Maxim Simonov (Gazpromneft Science & Technology Centre) | Dmitry Perets (Gazpromneft Science & Technology Centre) | Andrey Margarit (Gazpromneft Science & Technology Centre) | Darya Serebryakova (Gazpromneft Science & Technology Centre) | Yuriy Bogdanov (MIPT Engineering Center) | Semen Budennyy (MIPT Engineering Center) | Nikita Volkov (MIPT Engineering Center) | Artem Tsanda (MIPT Engineering Center) | Alexander Bukharev (MIPT Engineering Center)
- Document ID
- Society of Petroleum Engineers
- SPE Russian Petroleum Technology Conference, 15-17 October, Moscow, Russia
- Publication Date
- Document Type
- Conference Paper
- 2018. Society of Petroleum Engineers
- 3 Production and Well Operations, 3 Production and Well Operations, 5 Reservoir Desciption & Dynamics, 4.1.2 Separation and Treating, 5.6 Formation Evaluation & Management, 2 Well completion, 2.4 Hydraulic Fracturing, 4 Facilities Design, Construction and Operation, 7.6.6 Artificial Intelligence, 3.5 Well Intervention, 4.1 Processing Systems and Design, 5.2 Fluid Characterization, 5.6.1 Open hole/cased hole log analysis, 5.2.1 Phase Behavior and PVT Measurements
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The PDF file of this paper is in Russian.
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.
|File Size||1 MB||Number of Pages||16|