Enhancing pressure gradient prediction in multi-phase flow through diverse well geometries of North American shale gas fields using deep learning

Sungil Kim, Tea-Woo Kim, Yongjun Hong, Juhyun Kim and Hoonyoung Jeong

Energy, 2024, vol. 290, issue C

Abstract: To ensure the efficient operation of production wells and to avoid flow assurance issues, accurately predicting pressure gradients, Δp/ΔL, in wells is crucial. This study explores the relationship between Δp/ΔL and production well geometries. The data from 29,047 wells across five shale gas basins is utilized: Bakken, Barnett, British Columbia, Marcellus, and Delaware. These basins have distinct shale gas reservoir properties and diverse well geometries. Each well's geometry was characterized by ten parameters, and k-medoids clustering was applied to identify geometry patterns. However, this method overlooked minor geometric variations impacting Δp/ΔL. An adaptive factorization network model, a deep learning approach, was employed to address this. This model demonstrated significant predictive accuracy, as evidenced by high determination coefficients, R2, comparing actual and predicted Δp/ΔL in test data (overall 0.904; Bakken 0.840; Barnett 0.848; BC 0.844; Marcellus 0.872; Loving-Delaware 0.914; Reeves-Delaware 0.880). The method's reliability in forecasting Δp/ΔL is supported by its R2 values of ∼0.8 and the mean absolute percentage error of ∼5 %, indicating its applicability in newly developing basins. This methodology offers reliable Δp/ΔL predictions for all possible well geometry scenarios in shale gas fields but also eliminates the need for additional multi-phase flow simulations, enhancing design and production plan.

Keywords: Pressure gradient; K-medoids clustering; Multi-phase flow simulation; Flow assurance; Shale gas reservoir; Adaptive factorization network (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544224000628
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:290:y:2024:i:c:s0360544224000628

DOI: 10.1016/j.energy.2024.130291

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().