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Record W4401481626 · doi:10.56952/arma-2024-0858

Analysis of the Deadwood Formation in North Dakota: Applying Rock Physics

2024· article· en· W4401481626 on OpenAlexaboutno aff
Moones Alamooti, S. Namie

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsnot available
Fundersnot available
KeywordsGeologyComputer scienceEarth scienceMining engineeringArchaeologyGeography

Abstract

fetched live from OpenAlex

ABSTRACT: The geothermal potential within North Dakota's Deep Sedimentary Basin formations, particularly the Deadwood Formation, holds promise. Viability of developing unconventional geothermal reservoirs in these complex lithologies depends on thorough subsurface characterization and modeling before injection. Advanced rock physics modeling techniques become essential to unravel the fundamental petrophysical properties and architectures to pinpoint optimal zones. This research uses rock physics methodologies to accurately classify lithofacies and characterize the reservoir rocks in the Deadwood Formation. The methodology integrates theory, analysis, and data for subsurface understanding. Additionally, incorporating composition and modulus data adds precision. This integrated approach is crucial given the complexities. Various analytical techniques are deployed, enabling determination of responses. Diverse scenarios are explored, acknowledging variability and heterogeneity. Results reveal nuanced coexistence of intraparticle and interparticle porosity associated with depositional facies. This suggests intraparticle nanoporosity likely enhances permeability by increasing interconnectivity. Larger pores may store fluids, influencing productivity. In summary, this research significantly advances understanding of fundamental parameters, relationships, and motifs. Findings facilitate identifying prospective units across the basin. Results should reduce uncertainties around potential, providing development guidance. This opens avenues for geothermal exploration and contributes to the energy conversation. 1. INTRODUCTION 1.1. Background The Deadwood Formation is a deep sedimentary unit in the Williston Basin, spanning parts of North Dakota, South Dakota, and Montana in the United States and in parts of Alberta, Saskatchewan, and the southwestern corner of Manitoba in Canada. This predominantly sandstone formation (Fig.1) with interbedded shales was deposited in a continental fluvial environment during the Late Cambrian to Early Ordovician periods (Lochman-Balk & Wilson, 1967). Deep Earth Energy Production Corporation (DEEP) has drilled five geothermal wells aimed at the Deadwood Formation at depths of approximately 11,500 feet and temperatures approaching 250°F (Fig.2) (Murphy, 2021). However, the intricacy of its depositional architecture, coupled with likely facies variability at both regional and local scales, poses challenges for geothermal reservoir characterization and development (Goldstein et al., 2011). A thorough analysis of fundamental petrophysical properties and internal lithofacies relationships across the Deadwood Formation is required to reduce subsurface uncertainties and enable the identification of optimal zones for sustainable geothermal energy production. Advanced subsurface modeling techniques integrating geology, geophysics, petrography, and rock physics will play a crucial role in unraveling the stratigraphic complexity and inherent heterogeneities of potential geothermal reservoir units within the Deadwood Formation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.374
Threshold uncertainty score0.744

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.218
Teacher spread0.206 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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