Prophet modeling for oil production forecasting in an enhanced oil recovery field

Abstract

Accurate daily oil production forecasting is essential for efficient reservoir management and investment strategy. Forecasting oil production in enhanced oil recovery (EOR) and conformance-dominated fields is a complex process due to the nonlinear, voluminous, and often uncertain nature of reservoir parameters and hidden factors. As a result, conventional tools such as decline curve analysis frequently fail to accurately predict daily oil production in conformance-controlled areas. In contrast, machine learning works efficiently for large datasets, even if the parameter values are unknown. The current study employs a Prophet time series forecasting method for five oil production wells in an EOR applied field, but it fails to achieve the desired sweep efficiency. This study compares the results of conventional decline curve analysis (DCA) and popular autoregressive integrated moving average time series forecasting methods with the Prophet model. This is the first attempt to use Prophet for oil well production forecasting, where polymer flooding is used. In all, 60% of the data are used for training, and the remaining 40% are used for testing. The Prophet shows the best performance for all the wells. This study is also the first to handle shut-in data using the Prophet model for oil production. Well-2 achieves the highest accuracy after incorporating shut-in results, with an R2 score of 92%. The result shows that though the DCA performs reasonably well with higher linearity and trend stationary data, Prophet modeling shows superior results than conventional DCA for all EOR applied producing wells.