Short-Term Forecasting of Well Production Based on a Hybrid Probabilistic Approach

Abstract

The presented article is dedicated to creation and testing of probabilistic ensemble computational tool for operational forecasting of well production in short term (STF). The ensemble consisted of models based on such physical and mathematical tools as: the equation of non-stationary filtration, material balance, Darcy's law and machine learning models. After calculations by each model, their forecasts are combined into a single ensemble forecast. The hybrid approach is based on the Monte Carlo method on Markov chains as a separate probabilistic model using Bayes’ formula. In this case, statistical weights of each model (the degree of confidence in each model) is determined in the form of a probability distribution based on the reliability of previously performed forecasts. The test results presented in this article were obtained on the real field data. The obtained forecasts of individual models and the ensemble were compared to real data. Real data tool usage analysis showed that the proposed approach gives a small error in comparison with actual measurements. Efficiency of calculations allows to automatically adapt the model to the entire well production history (several hundred wells) within a few hours.