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Verification of Field Data and Forecast Model Based on Variational Autoencoder in the Application to the Mechanized Fundстатья
- Авторы: Volkov Nikita Alekseevich, Dakhova Elizaveta Yuryevna, Adrianova Alla Mikhailovna, Budennyy Semen Andeevich
- Сборник: SPE Russian Petroleum Technology Conference
- Год издания: 2020
- Издательство: Society of Petroleum Engineers, Inc.
- Местоположение издательства: United States
- DOI: 10.2118/201936-ms
- Аннотация: The importance of quality, consistent data is difficult to overestimate. The better the field data describes the real system, the higher the predictive ability of models at all levels based on it and the higher the accuracy of production decisions. This issue is particularly relevant in the context of data from the mechanized well stock. The paper presents both methods of data analysis in real time, and an approach for retrospective analysis (analysis of historical data) in the application to well data in the operation of the ESP. The key advantage of the model presented in the paper is that it allows considering a complex set of time dependencies, taking into account their mutual influence.In order to account for the dependencies between physical quantities and time, a model using probabilistic neural networks has been developed allowing for retrospective filtering and data filtering in streaming mode. The model is based on the principle of the conditional variational autoencoder model. This model of a neural network is characterized by the fact that it allows establishing the main dependencies in the data with acceptable quality under given conditions. A special feature of the research model is its probabilistic nature, that is, it is able to calculate data distributions, as well as distributions of values on certain layers. The distribution at the model output is used to estimate the degree of abnormality of objects. Special data transformations, the introduction of weights and the addition of key features, allow dealing with missing values (often observed in field data due to sensor malfunction, features of measurement measures, inconsistencies in time scales for several measurements, etc.), and fix important patterns in the data. The key point in working with features is a special scheme for preprocessing time series, namely, the introduction of weights depended on the frequency of measurements, and the calculation of weighted quantiles for different time intervals.
- Добавил в систему: Буденный Семен Андреевич