Abstract

Data-driven models, such as neural networks, provide an alternative to physics-based simulations in predicting well behavior within unconventional reservoirs. However, these models struggle to interpret the factors behind production performance, the underlying physics of the process, and correlations unique to individual wells and their flow behaviors. These models are designed to approximate general trends present in data sets, which can hinder their ability to learn behaviors specific to certain data segments. In large unconventional fields, wells within a single formation can exhibit varied production behaviors, emphasizing the need to segment the data into more relevant subsets. Such data segmentation enables the development of local models (LMs) that capture regional or behavioral correlations. In contrast, a field-wide (global) AI model, trained on the entirety of the data, tends to produce averaged …

Date

May 14, 2025

Authors

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

Journal

SPE Journal

Pages

1-18