Multiple Production Time Series Forecasting Using Deepar and Probabilistic Forecasting

Abstract

Abstract The majority of production forecasting methods currently used are point forecasting methods developed in the setting of individual well forecasting. For an actual oilfield, instead of needing to predict individual production time series, one is faced with forecasting thousands of related time series and the uncertainty can be assessed. The objective of this work is to enable global modeling and probabilistic forecasting of a large number of related production time series using Deep Autoregressive Recurrent Neural Networks (DeepAR). The DeepAR model consists of three parts. First, the auxiliary data such as static classification covariates and dynamic covariates are encoded. Second, establish a forward model based on an autoregressive recurrent neural network. Third, the normal distribution is defined as the output distribution function. And the variance and mean are obtained by solving the maximum log-likelihood function using the gradient descent algorithm. We demonstrate how the application of DeepAR to forecasting can overcome many of the challenges(e.g. frequent well shut-in and opening, probabilistic prediction, classification prediction) that are faced by widely-used classical approaches to the problem. In this work, history fitting and prediction were performed on a dataset from more than 2000 tight gas reservoir wells in the Ordos Basin, China. The DeepAR and conventional methods were tested and compared based on the datasets. We show through extensive empirical evaluation on several real-world forecasting data sets accuracy improvements of around 30% compared to RNN-based networks. In the case of frequent well shut-ins and openings, the RNN-based network structure cannot capture the fast pressure response and extreme fluctuations, which eventually leads to high errors. In contrast, DeepAR is more stable to frequent or significant well variations, can learn different dynamic and static category features, generates calibrated probabilistic forecasts with high accuracy, and can learn complex patterns such as seasonality and uncertainty growth over time from the data. This study provides more general production forecasting and analysis of production dynamics methods from a big data perspective. Instead of performing costly well tests or shut-ins, reservoir engineers can extract valuable long-term reservoir performance information from predictions estimated by DeepAR trained on an extensive collection of related production time series data.