Abstract

Data-driven methods have surged in popularity due to increased field development and data collection effort in the last two decades, and partly because flow physics in hydraulically fractured low-permeability formations is poorly understood. Such statistical tools have limited extrapolation ability and require sufficient training data, where training an under-determined neural network predictive model with limited data can result in overfitting and poor prediction performance. Unlike statistical models, physics-based models impose causal relations that can provide reliable predictions over a wide range of input. While a detailed physics-based description of fluid flow in unconventional reservoirs is not yet available, approximate physical flow functions have been proposed to capture the general production behavior of unconventional wells. These physical functions can be augmented with the available data to enhance …

Metadata

publication

Unconventional Resources Technology Conference, 20–22 June 2022, 2098-2117, 2022

year

2022

publication date

2022/10/10

authors

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

link

https://library.seg.org/doi/abs/10.15530/urtec-2022-3702606

book

Unconventional Resources Technology Conference, 20–22 June 2022

pages

2098-2117

publisher

Unconventional Resources Technology Conference (URTeC)