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Record W2085078612 · doi:10.2118/09-08-49

Enhanced Gas Recovery: Factors Affecting Gas-Gas Displacement Efficiency

2009· article· en· W2085078612 on OpenAlexaboutno aff
S. Sim, A.T. Turtata, A.K. Singhal, Blaine F. Hawkins

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2009
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPetroleum engineeringNatural gasNatural gas fieldFossil fuelDisplacement (psychology)Environmental scienceGeologyWaste managementEngineering

Abstract

fetched live from OpenAlex

Abstract This paper is part of a series of papers on the results of Enhanced Gas Recovery (EGR) research conducted at the Alberta Research Council during 2003 to 2007. In this Joint Industry Project (JIP), the soundness of the concept of gas-gas displacement for enhancing gas recovery was investigated via laboratory investigations, compositional modelling and economic analyses. The results of Phase I gas-gas displacement tests conducted at relative high pressure and temperature (6.2 MPa and 70 °C) in 4 cm diameter 30 cm long Berea core were recently reported(1, 2). In the second phase (2004–2005) of the JIP, the main targets were low pressure volumetric (closed) reservoirs in advanced stages of exploitation and also gas bearing strata overlaying oil sand intervals. Pressure maintenance of a depleting gas reservoir by waste gas injection can serve to:arrest the decline in gas production rate, prevent premature well abandonment and increase ultimate recovery;discourage the advance of an aquifer (if present) into the gas zone; andin the case of Gas-Over-Bitumen situations, mitigate declining reservoir pressure during natural gas production to enable exploitation of the underlying oil sands. One example of a field application of this EGR technology was the GRIPE Project operated by Paramount Resources during 2005 and 2006. A series of gas-gas displacement tests were conducted at room temperature and at pressures between 0.7 and 3.5 MPa in the presence of connate water in 5 cm diameter ? 2 m long sandpacks. Experimental parameters, such as nature of the injection gas, displacement pressure and displacement rate were systematically varied to study their effect on the displacement efficiency. Numerical simulations of the experimental results were also conducted to gain a better understanding of the interrelationship between the different variables. The laboratory results showed that during low velocity displacement of methane by flue gas in a homogeneous linear sandpack, molecular diffusion has a dominating effect on the recovery of marketable methane. Reasonable values of molecular diffusion coefficient for different gas-gas displacement conditions were obtained by matching the experimental test results with the numerical simulation. In spite of anticipated adverse effects of mixing between displaced and displacing gas due to molecular diffusion under low pressure and low flow velocity conditions, incremental recoveries of marketable methane under the experimental conditions were encouraging and suggest that EGR by gas-gas displacement can prolong the productive life and increase natural gas recovery from many volumetric gas reservoirs. Introduction Alberta currently has about 42,000 gas pools which are in different stages of exploitation, and many of them are approaching the end of their productive lives. The main goal for the second phase of our Joint Industry Project was to investigate enhanced gas recovery from low pressure volumetric (closed) reservoirs that are in advanced stages of exploitation and also, enhancing recoveries from gas bearing strata overlaying oil sand intervals. Pressure maintenance of a depleting gas reservoir by waste gas injection can serve to:arrest the decline in the gas production rate, prevent premature well abandonment and increase ultimate recovery;

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 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 score0.839
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.004
GPT teacher head0.197
Teacher spread0.193 · 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 teacher head, 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

Citations64
Published2009
Admission routes1
Has abstractyes

Explore more

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