Following the rapid growth of unconventional resources, petroleum engineers have been focusing on the use of various tools to predict the performances and operational lives of unconventional reservoirs. Several studies have used machine learning (ML) algorithms to improve the productivity of reservoir fields. However, owing to a lack of stability and other limitations of ML in regard to long-term forecasts, including the occurrence of unphysical results, reservoir engineers often do not trust ML.

In this work, we present a new workflow for automating a decline curve analysis (DCA) calculation in a more robust way, and for predicting the production from new wells using a state-of-the-art Bayesian neural ordinary differential equation (ODE). This provides a powerful framework for modeling physical simulations, even when the ODEs governing the system are not explicitly defined. This study utilizes publicly available databases from the Bakken Shale Formation to develop a novel ML predictive modeling method for connecting well-completion-related and geological variables to the parameters of a stretched exponential decline model (SEDM). These SEDM-estimated parameters are integrated with a Bayesian neural ODE framework based on Bayesian inference. A "No-U-Turn" Markov chain Monte Carlo (MCMC) sampler (denoted "NUTS") is used to rapidly predict the decline curves for new or existing wells, without the need for costly reservoir simulators. This methodology is found to be accurate for predicting the decline rates of new wells. Depending on the data obtained from existing wells, this method can also be used to predict the ultimate recovery from a new well.

To the best of our knowledge, this is the first study to simultaneously employ both the Bayesian neural ODE and ML algorithm to predict and analyze functional capabilities based on decline curve parameters