EERC Publications

Document Type

Conference Proceeding

Files

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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 reservoir engineerings

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

Notes

URTEC-3725863-MS

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.

Statistical Analysis of the Petrophysical Properties of the Bakken Petroleum System

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