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Record W2997136202 · doi:10.11575/prism/37378

Three-Dimensional Geological Characterization and Modeling of Fine-Grained Petroleum Reservoirs: An Evaluation of the Montney Formation in Westcentral Alberta and Bakken Formation in Southeastern Saskatchewan, Canada

2019· dissertation· en· W2997136202 on OpenAlexaboutno aff
Sochi Iwuoha

Bibliographic record

VenuePRISM (University of Calgary) · 2019
Typedissertation
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPetroleumGeologyReservoir modelingFormation evaluationCharacterization (materials science)Petroleum systemPetroleum engineeringGeomorphologyPaleontologySource rockMaterials science

Abstract

fetched live from OpenAlex

In Canada, tight and shale plays such as the Montney, Bakken, and Duvernay (to mention but a few) have over the past decade become the focus of exploration and development activities. However, despite the successes recorded in drilling and completing multi-fractured horizontal wells (MFHws) in Canada and the United States of America (USA), the geological characteristics of shale and tight reservoirs remain poorly understood; evidenced by frequent production and development challenges faced by operators. These challenges include (but are not limited to) rapid well decline rates, parent-child well production interference, and difficulty in reconciling multi-scale geological heterogeneities. The challenge of unconventional reservoir (this terminology is used in this thesis to solely refer to shale and tight reservoirs) development is further compounded when there is a lack of data such as three dimensional (3D) seismic, microseismic, borehole imagery, or well logs suites in MFHw laterals (to mention but a few) to support integrated reservoir studies and dynamic simulation. Given the above, the aim of this dissertation was two-pronged: (1) to develop novel approaches for geologically characterizing tight reservoirs such that the importance of understanding nano to macro-scale heterogeneity is demonstrated through reconciliation with production data or validation using independent complementary datasets (2) to develop geological characterization and modeling techniques that can be used in the absence of traditional geological and geophysical datasets (as earlier mentioned). The Montney Formation in west-central Alberta and the Bakken Formation in southeastern Saskatchewan Canada were used as case studies to demonstrate how commonly acquired datasets such as well logs can be leveraged to improve the geological characterization of tight reservoirs. Seismic data available in the Bakken Formation was used to validate the applicability of a new well log to seismic inversion workflow that was developed and applied in the Montney Formation. New insights on the role of the dominant pore throat size control on fluid distribution and influence on production variation in tight reservoirs are presented. Furthermore, in the Bakken Formation, a natural fracture zone identification technique was developed, along with a new subdivision of the Middle Bakken producing interval into five geomechanical zones based on dynamic elastic properties. The Bakken natural fracture zone identification technique was facilitated by neural network modeling and a newly developed multi-log attribute relation. The natural fracture zones identified were shown to be consistent with independent results from seismic attribute analysis. Finally, this work expands the paradigm of unconventional resource exploitation (which is primarily driven by the intent to increase production) to include the consideration of exploration and development drilling pathways that can potentially reduce the pre-production greenhouse gas emission of MFHws. Where available, core and field data were used for quality-controlling and validating results.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.191
Teacher spread0.178 · 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 designSimulation or modeling
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

Citations0
Published2019
Admission routes1
Has abstractyes

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