Ссылка для цитирования (ENG)
RUSTAMOV A R , PENKOV D. G., PETRAKOV D. G., RUSTAMOVA M. A. MODERN METHODS OF USING MACHINE LEARNING AS A TOOL FOR OIL PRODUCTION FORECASTING НЕДРОПОЛЬЗОВАНИЕ. 2024. №1. pp. 44-50. https://www.elibrary.ru/item.asp?id=65315374
Авторы
RUSTAMOV A R , PENKOV D. G., PETRAKOV D. G., RUSTAMOVA M. A.
Журнал
НЕДРОПОЛЬЗОВАНИЕ
Год
2024
Ключевые слова
Аннотация
Oil production forecasting plays an important role in efficient oil field development. This helps to adjust the current field development system. Detailed and accurate forecasting of oil production levels is necessary to assess the economic and technological efficiency of oil field development. Forecasting production levels can be done in various ways. One of these may be the use of special software (tNavigator, etc.). The use of this software sometimes involves lengthy calculations, so to quickly predict production levels, it is possible to use other tools, such as machine learning. The use of machine learning and artificial intelligence in the oil and gas industry has become increasingly popular in recent years, as by using historical production data, it is possible to predict oil/liquid production levels. In addition, similar deposits with similar geological characteristics and exploitation history can be used for similar purposes. In addition to using machine learning and artificial intelligence as a forecasting tool, it is possible to use decline curve analysis. Given the importance of forecasting from a strategic planning perspective, a wide range of methods have been proposed to obtain accurate forecasts based on the nature of available data and computing power. This article provides a comprehensive analysis of the tools used for long-term oil production forecasting, including machine learning algorithms and decline curve analysis (DCA), in particular. This article presents the results of applying the long- and short-term memory model and its practical applicability using the example of its use on a candidate well.
