Abstract
Monitoring of production rates is essential for reservoir management, history matching, and production optimization. Traditionally, such information is provided by multiphase flow meters or test separators. The growth of the availability of data, combined with the rapid development of computational resources, enabled the inception of digital techniques, which estimate oil, gas, and water rates indirectly. This paper discusses the application of continuous deep learning models, capable of reproducing multiphase flow dynamics for production monitoring purposes. This technique combines time evolution properties of a dynamical system and the ability of neural networks to quantitively describe poorly understood multiphase phenomena and can be considered as a hybrid solution between data-driven and mechanistic approaches. The continuous latent ordinary differential equation (Latent ODE) approach is compared to other known machine learning methods, such as linear regression, ensemble-based model, and recurrent neural network.
In this work, the application of Latent ordinary differential equations for the problem of multiphase flow rate estimation is introduced. The considered example refers to a scenario, where the topside oil, gas, and water flow rates are estimated using the data from several downhole pressure sensors. The predictive capabilities of different types of machine learning and deep learning instruments are explored using simulated production data from a multiphase flow simulator.
The results demonstrate the satisfactory performance of the continuous deep learning models in comparison to other machine learning methods in terms of accuracy, where the normalized root mean squared error (RMSE) and mean absolute error (MAE) of prediction below 5% were achieved. While LODE demonstrates the significant time required to train the model, it outperforms other methods for irregularly sampled time-series, which makes it especially attractive to forecast values of multiphase rates.
