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Record W2587609146 · doi:10.2118/185066-ms

Gas Selection for Huff-n-Puff EOR in Shale Oil Reservoirs Based upon Experimental and Numerical Study

2017· article· en· W2587609146 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSPE Unconventional Resources Conference · 2017
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsUniversity of Calgary
FundersU.S. Department of Energy
KeywordsPetroleum engineeringEnhanced oil recoveryOil shaleSeparator (oil production)Gas oil ratioTight oilEnvironmental scienceFossil fuelOil in placeShale oilPetroleumGeologyWaste managementChemistryEngineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Huff-n-Puff gas injection is a method originally used in heavy oil reservoir to reduce oil viscosity, increase mobility and displacement efficiency to enhance oil recovery. Now this method has been applied to enhance unconventional oil recovery in shale or tight reservoirs in recent years and proved to be effective in experiment study. N2, C1, CO2 or other rich gases are used in shale oil EOR. The purpose of this paper is to compare the EOR potential of different gas and provide a guide to choose gas based on the Wolfcamp shale oil reservoir. The composition of crude oil from Wolfcamp was analyzed by Gas Chromatography (GC). First, the core plugs from Wolfcamp with diameters of 1.5 inches were saturated with crude oil. Then gas huff-n-puff experiments using N2, C1, and CO2 were conducted in the laboratory with the same injection pressure of 2000 psi. Based on laboratory results, a compositional model is built and used to analyze the performance of gas huff-n-puff. The EOR capacity of gas mixture (N2, C1, CO2) and some solvents such as C3-CO2 mixture, the separator gases C1 to C4 from the field production were investigated using the simulation method. From the experiment results of the three kinds of gas injections, the oil recovery in the first two injection cycles were large. The incremental oil recovery decreased as the increase in number of injection cycles. Comparing the three kinds of gas EOR effects on Wolfcamp core samples, CO2 EOR result was the best, followed by N2 and C1. Coupling the equation of state method with GC analysis, 24 components of crude oil were achieved and then lumped into 5 pseudo components for simulation. The EOR effects of other gas mixture and solvents are investigated using the field model. The results show that the EOR effect of mixture of C1, C2 and C4 is the most favorite, followed by CO2-C3 mixture, produced gas, C1-CO2 mixture, and N2-CO2 mixture. Combined all of these results with economic factors, a comparison of gas capacity is proposed. This investigation is focused on injection gas selection for shale oil production and helps to provide us a screening criterion to choose effective and convenient gas when conducting huff-n-puff gas injection in shale oil development. Cheaper gas with higher EOR potential will reduce the production cost and bring huge economic benefits to oil company especially in this low oil price period.

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.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.781

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.044
GPT teacher head0.319
Teacher spread0.275 · 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