Abstract

The flow and transport processes that take place during hydrocarbon production from hydraulically fractured unconventional reservoirs are not well understood. As a result, current simulators cannot provide reliable predictions of the production behavior in the field. In addition to imperfect physics, the prediction errors can be caused by the inability to conveniently integrate important field data, such as well logs, drilling, and completion parameters, into existing physical models. A neural network (NN) model is developed to learn the (residual) errors in simulation-based production prediction as a funcation of input parameters of an unconventional well. Once trained, the NN model augments the physics-based predictions by adding the learned reiodual to predict the production response of a new well. To learn the discrepancy between the simulated and observed production data, the NN model is trained using a labeled …

Date

2022

Authors

Jodel Cornelio, Syamil Mohd Razak, Young Cho, Hui-Hai Liu, Ravimadhav Vaidya, Behnam Jafarpour

Journal

SPE Journal

Volume

27

Issue

06

Pages

3328-3350

Publisher

OnePetro