title: ÐÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ðµ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¸ Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð½ÐµÑÑÐµÐ³Ð°Ð·Ð¾Ð´Ð¾Ð±ÑÑÐ¸
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Application of machine learning and deep learning models for forecasting oil and gas production
Creators: name: ÐÑÐ¸Ð³Ð¾ÑÑÐµÐ², ÐÐ¸ÑÐ¸Ð»Ð»
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2023
Subjects: ÐÐ°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÐÐµÑÑÐµÐ´Ð¾Ð±ÑÑÐ°
ÐÑÑÐµÐ½Ð¸Ðµ Ð½Ð° Ð³Ð°Ð·
Ð¼Ð¾Ð´ÐµÐ»Ñ
Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ
model
deep learning
forecasting

description: Ð¢ÐµÐ¼Ð° Ð²ÑÐ¿ÑÑÐºÐ½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ: Â«ÐÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ðµ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¸ Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð½ÐµÑÑÐµÐ³Ð°Ð·Ð¾Ð´Ð¾Ð±ÑÑÐ¸Â». ÐÐ°Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¸ Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð² Ð¾Ð±Ð»Ð°ÑÑÐ¸ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð½ÐµÑÑÐµÐ³Ð°Ð·Ð¾Ð´Ð¾Ð±ÑÑÐ¸. ÐÐ°Ð´Ð°ÑÐ¸: Ð²ÑÐ¿Ð¾Ð»Ð½Ð¸ÑÑ Ð°Ð½Ð°Ð»Ð¸Ð· Ð¿ÑÐµÐ´Ð¼ÐµÑÐ½Ð¾Ð¹ Ð¾Ð±Ð»Ð°ÑÑÐ¸; ÑÐ°ÑÑÐ¼Ð¾ÑÑÐµÑÑ ÑÑÑÐµÑÑÐ²ÑÑÑÐ¸Ðµ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ñ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¸ Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ; Ð¿ÑÐ¸Ð¼ÐµÐ½Ð¸ÑÑ ÑÑÐ¸ ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÑ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° Ð´Ð»Ñ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° Ð¸ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð½ÐµÑÑÐµÐ´Ð¾Ð±ÑÑÐ¸; Ð²ÑÐ¿Ð¾Ð»Ð½Ð¸ÑÑ Ð¾Ð¿ÑÐ¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ñ Ð»ÑÑÑÐµÐ³Ð¾ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð°. Ð ÑÐ°Ð±Ð¾ÑÐµ Ð²ÑÐ¿Ð¾Ð»Ð½ÐµÐ½ Ð°Ð½Ð°Ð»Ð¸Ð· Ð´Ð°Ð½Ð½ÑÑ Ð¿Ð¾ Ð½ÐµÑÑÐµÐ´Ð¾Ð±ÑÑÐµ, Ð° Ð¸Ð¼ÐµÐ½Ð½Ð¾ Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÑ ÑÐºÑÐ¿Ð»ÑÐ°ÑÐ¸ÑÑÑÑÐ¸Ð¹ÑÑ ÑÐºÐ²Ð°Ð¶Ð¸Ð½Ñ. ÐÑÐ¾Ð¸Ð·Ð²ÐµÐ´ÐµÐ½Ð° Ð¾ÑÐ¸ÑÑÐºÐ°, ÑÐ¸Ð»ÑÑÑÐ°ÑÐ¸Ñ Ð¸ ÑÐ³Ð»Ð°Ð¶Ð¸Ð²Ð°Ð½Ð¸Ðµ Ð²ÑÐµÐ¼ÐµÐ½Ð½ÑÑ ÑÑÐ´Ð¾Ð², Ð¿Ð¾Ð´Ð³Ð¾ÑÐ¾Ð²ÐºÐ° Ð´Ð°Ð½Ð½ÑÑ Ðº Ð¼Ð¾Ð´ÐµÐ»Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ. Ð ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐµ Ð±ÑÐ»Ð¸ Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ñ ÑÑÐ¸ Ð¼Ð¾Ð´ÐµÐ»Ð¸ (Arima, LSTM, Random Forest), Ð¾ÑÐµÐ½ÐµÐ½Ñ Ð¸Ñ Ð¿Ð¾ÐºÐ°Ð·Ð°ÑÐµÐ»Ð¸ Ð¾ÑÐ¸Ð±Ð¾Ðº. ÐÐ»Ñ Ð²ÑÐ±ÑÐ°Ð½Ð½Ð¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Arima Ð²ÑÐ¿Ð¾Ð»Ð½ÐµÐ½Ð° Ð¾Ð¿ÑÐ¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ñ ÐµÐµ Ð³Ð¸Ð¿ÐµÑÐ¿Ð°ÑÐ°Ð¼ÐµÑÑÐ¾Ð². Ð¢Ð¾ÑÐ½Ð¾ÑÑÑ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð¿Ð¾Ð²ÑÑÐ¸Ð»Ð°ÑÑ.
description: Graduate work topic: "Application of machine learning and deep learning models for forecasting oil and gas production". This work is devoted to the study of machine and deep learning models in the field of forecasting oil and gas production. Tasks: perform an analysis of the subject area; consider existing algorithms for machine and deep learning; apply three different algorithms for analysis and forecasting of oil production; optimize the best algorithm. The paper analyzes data on oil production, namely the parameters of the wells used. Cleaning, filtering and smoothing of time series, preparation of data for modeling.As a result, three models (Arima, LSTM, Random Forest) were built and their error rates were evaluated. For the selected Arima model, optimization of its hyperparameters was performed. Prediction accuracy has improved.