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Record W2742761636 · doi:10.7122/485175-ms

Comparative Study for CO2-EOR and Natural Gases Injection-Techniques for Improving Oil Recovery in Unconventional Oil Reservoirs

2017· article· en· W2742761636 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueCarbon Management Technology Conference · 2017
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPetroleum engineeringEnhanced oil recoveryOil shaleHydraulic fracturingNatural gasGeologyReservoir simulationPermeability (electromagnetism)DiffusionKnudsen diffusionEnvironmental scienceWaste managementGeotechnical engineeringEngineeringThermodynamicsChemistry

Abstract

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Abstract Shale formations in North America such as Bakken, Niobrara, and Eagle Ford have huge oil in place, 100–900 Billion barrels of oil in Bakken only. However, the predicted primary recovery is still below 10%. Therefore, seeking for techniques to enhance oil recovery in these complex plays is inevitable. Although most of the previous studies in this area recommended that CO2 would be the best EOR-technique to improve oil recovery in these formations, pilot tests showed that natural gases performance clearly exceeds CO2 performance in the field scale. In this paper, two different approaches have been integrated to investigate the feasibility of three different miscible-gases which are CO2, lean gases, and rich gases. Firstly, numerical simulation methods of compositional models have been incorporated with Local Grid Refinement (LGR) of hydraulic fractures to mimic the performance of these miscible gases in shale-reservoirs conditions. Implementation of a molecular diffusion model in the LS-LR-DK (logarithmically spaced, locally refined, and dual permeability) model has been also conducted. Secondly, different molar-diffusivity rates for miscible gases have been simulated to find the diffusivity level in the field scale by matching the performance for some EOR pilot-tests which were conducted in Bakken formation of North Dakota, Montana, and South Saskatchewan. The simulated shale-reservoirs scenarios confirmed that diffusion is the dominated flow among all flow regimes in these unconventional formations. Furthermore, the incremental oil recovery due to lean gases, rich gases, and CO2 gas injection confirms the predicted flow-regime. The effect of diffusion-implementation has been verified with both of single porosity and dual-permeability model cases. However, some of CO2 pilot-tests showed a good match with the simulated cases which have low molar-diffusivity between the injected CO2 and the formation-oil. Accordingly, the rich and lean gases have shown a better performance to enhance oil recovery in these tight formations. However, rich gases need long soaking periods, and lean gases need large volumes to be injected for more successful results. Furthermore, the number of huff-n-puff cycles has a little effect on the all injected-gases performance; however, the soaking period has a significant effect.

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Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.916
Threshold uncertainty score0.868

Codex and Gemma teacher scores by category

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

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 head0.026
GPT teacher head0.282
Teacher spread0.256 · 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