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Record W2973303418 · doi:10.2118/196101-ms

Hydrodynamics, Geothermics and Spatial Variations in Hydrocarbon Fluid Distribution within the Montney Formation, Alberta and British Columbia, Canada

2019· article· en· W2973303418 on OpenAlexaboutno aff
Allison Gibbs, Kaush Rakhit

Bibliographic record

VenueSPE Annual Technical Conference and Exhibition · 2019
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsnot available
Fundersnot available
KeywordsGeologyFossil fuelPetroleum engineeringGeothermal gradientStructural basinPetrologyGeomorphologyPaleontologyEngineering

Abstract

fetched live from OpenAlex

Abstract Hydrodynamics and geothermics are important tools for understanding the complex distribution of reservoir fluids in the Montney Formation in Alberta and British Columbia, Canada. The Montney comprises a conventional system in the east and an unconventional, Deep Basin-style hydrocarbon system in the west, where an underpressured, oil-dominated fairway just west and downdip of the conventional system grades further downdip into overpressured liquids and gas fairways. The first part of this study addresses how these systems can be mapped from a pressure and temperature perspective. The Montney hydrodynamics system is explained using pressure versus elevation graphs. Key contours are taken from pressure-depth ratio maps to define the general boundaries between systems, noting that these boundaries change with depth. Geothermal gradient mapping is used to identify areas of prominent high or low geothermal gradients, which can have a significant effect on the positioning of gas liquids fairways. Key current day isotherms are also identified to represent the current phase windows by relating present-day formation temperatures to Tmax data. To evaluate how pressure and temperature affect liquids production within the Montney, liquids production trends need to be considered. The second half of the paper discusses how mapping gas composition, particularly C2+ Wet Gas Index (WGI), may serve as a good proxy for liquids yields. While the authors appreciate the complexities of phase behavior and the various factors influencing liquids production, the objective of this paper is to link trends that can be observed in liquids production to trends in pressure, temperature and gas composition. Ultimately, this paper examines ways in which hydrodynamics and geothermics can be used to help predict spatial variations in observed liquids production. By analyzing the co-relationships of the pressure, temperature and WGI data, the Montney segregates into two distinct domains which we term the Northern (British Columbia) Play and the Southern (Alberta) Play. This analysis can be tied in with other data sets for a better understanding of the reservoir such as: isotope geochemistry to gain insights into hydrocarbon migration; Special Core Analysis (SCAL) data to gain insights into fluid mobility; vapour-liquid equilibrium data to examine hydrocarbon fractionation during production; and completions data to provide a more complete picture of reservoir deliverability.

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 score0.024
Threshold uncertainty score0.171

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0020.001
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.004
GPT teacher head0.177
Teacher spread0.173 · 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

Citations9
Published2019
Admission routes1
Has abstractyes

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