EERC Publications
Document Type
Conference Proceeding
Files
Download Full Text (11.9 MB)
Publication Date
6-20-2022
Publication Title
SPE/AAPG/SEG Unconventional Resources Technology Conference
Keywords
Bakken Formation, Williston Basin, Machine learning, Oil reservoirs
Abstract
This paper aims to generate permeability-porosity data-driven models for the Bakken formation, representing an unconventional reservoir within the Williston Basin in the US, using 426 core data with a wide range of porosity and permeability.
Publisher
SPE/AAPG/SEG Unconventional Resources Technology Conference
DOI
https://doi.org/10.15530/urtec-2022-3725863
Extent
22 pages (PDF/A, 11.9 megabytes)
Repository
University of North Dakota, Energy & Environmental Research Center, Library & Information Services. 15 North 23rd Street, Grand Forks, ND 58202. eerclibrary@undeerc.org.
Identifier
1996788
Rights
This paper reprinted with permission from Unconventional Resources Technology Conference, Houston, Texas, U.S., June 20–22, 2022, Copyright 2022 by Unconventional Resources Technology Conference (URTeC). Permission from URTeC is required for further use.
Recommended Citation
Laalam, Aimen; Ouadi, Habib; Merzoug, Ahmed; Chemmakh, Abderraouf; Boualam, Aldjia; Djezzar, Sofiane; Mellal, Ilyas; and Djoudi, Meriem, "Statistical Analysis of the Petrophysical Properties of the Bakken Petroleum System" (2022). EERC Publications. 20.
https://commons.und.edu/eerc-publications/20
Notes
URTEC-3725863-MS