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Record W1987698143 · doi:10.2118/2004-061

Estimation of Residual Gas Saturation From Different Reservoirs

2004· article· en· W1987698143 on OpenAlexafffund
Meng Ding, Apostolos Kantzas

Bibliographic record

VenueCanadian International Petroleum Conference · 2004
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of Calgary
FundersCanada Research Chairs
KeywordsResidualSaturation (graph theory)EstimationEnvironmental sciencePetroleum engineeringComputer scienceStatisticsGeologyMathematicsAlgorithmEngineering

Abstract

fetched live from OpenAlex

Abstract Residual gas saturation is a crucial number to estimate the gas recovery in gas reservoirs with active aquifers. Water influx in gas reservoirs has long been recognized as an important cause of gas trapping in water-wet reservoirs. In this study, the residual gas saturation to water influx was investigated in 47 core plugs, including 29 sandstone plugs from different areas, 2 Berea sandstone plugs and 16 carbonate plugs. Over 100 different experiments were performed, including spontaneous water imbibition and forced water imbibition tests, primary imbibition and secondary imbibition tests, counter current and co-current imbibition tests. Measurements indicated that the value of residual gas saturation depends on many factors, including reservoir properties, the capillary number, experimental procedures, fluid properties and also very strongly depends on the gas solubility and compressibility. The residual gas saturation value from primary and secondary imbibition tests was also used to compare against literature models. Modified models were developed in order to fit the experimental data better. The residual gas saturation results show that gas recovery should be high under spontaneous imbibition and extremely high under the forced imbibition. However, trapped gas in reservoirs with active aquifers remains as high as 90%. Hopefully this paper can provide some insight for enhance gas recovery in gas reservoirs with active aquifers. Introduction Recovery of natural gas from reservoirs with a naturally occurring underlying aquifer and aquifer gas storage are common projects in gas reservoir engineering. In both types of projects large volumes of gas become trapped and cannot be recovered. Once gas becomes trapped, conventional wisdom dictates that it is very difficult to remobilize. It is very important to calculate the optimum gas recovery and if residual gas saturation values are high, to further develop a strategy of enhancing gas recovery. Experimental research work was presented in Kantzas et al.1, in which experiments were performed in both sandstone and carbonates reservoirs and residual gas saturation was evaluated. Some factors such as wettability, imbibition rate and experimental procedures, which affect the residual gas saturation, were discussed. Also different residual gas saturation predictive models from the literature were applied in their work. Continuation of this research work was presented in Ding and Kantzas 2–4, in which the residual gas saturation evaluation from different reservoirs and at different conditions was addressed. The efficiency of gas recovery by water imbibition was described in Crowell et al5. The different factors, which affected residual gas saturation, were addressed. It was shown that gas recovery is a strong function of the initial gas saturation and that the maximum recovery is obtained at zero initial water saturation. Different sandstone plugs were used in the experiments. It was shown that similar residual gas saturation values were obtained by both free imbibition and imbibition at a constant flow rate. A slight increase in gas recovery with a reduction of interfacial tension was observed in Berea slabs. Suzanne et al.6, 7 evaluated the residual gas saturation through 60 relationships between initial gas saturation (Sgi) and residual gas saturation (Sgr), which covered a large set of sandstone plugs.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.106
Threshold uncertainty score0.937

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 head0.010
GPT teacher head0.214
Teacher spread0.203 · 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

Citations9
Published2004
Admission routes2
Has abstractyes

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