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Record W2051321216 · doi:10.2118/143907-ms

An Improved Methodology to Obtain the Arps’ Decline Curve Exponent (b) for Tight/Stacked Gas Reservoirs

2011· article· en· W2051321216 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsConocoPhillips (Canada)
Fundersnot available
KeywordsTight gasPermeability (electromagnetism)Petroleum engineeringReservoir simulationTransient analysisExponentComputer scienceEnvironmental scienceMathematical optimizationGeologyMathematicsEngineeringChemistryTransient response

Abstract

fetched live from OpenAlex

Abstract Production performance forecasting and the estimated ultimate recovery (EUR) evaluation are two of several uncertainties in the study of tight gas reservoirs. Using the Arps’ decline curve analysis to extrapolate the estimated ultimate recovery (EUR), based on the future production forecasting, is still a widely employed engineering tool in the oil and gas industry today. Tight gas reservoirs usually have a long transient/transitional flow period due to its low permeability. Simply by using the best fit "b" value to estimate the best EUR could generate an error as much as 100% from the true EUR. There has been a number of technical papers published dealing with this issue. This paper will present a new improved methodology to determine a more accurate "b" value to be used in the Arps’ decline curve analysis for tight gas reservoirs. This new approach, using a newly developed relationship between Qcum, Qcum, t→∞, qt and t, is more rigorous and much easier to use in comparison with all other existing ones. This new improved methodology will not only make future production forecasting more accurate, but also estimate the original-gas-in-place (OGIP) controlled by the well much easier. In Western Canadian Deep Basin, there are many extremely "tight" gas reservoirs that consist of multiple stacked shorefaces. Permeability in each shoreface varies very significantly, generally with the upper shoreface having a better permeability and the middle and lower ones having a permeability as poor as 0.01 mD. By using a reservoir simulator, a number of simulation runs have been performed by the author to answer the following questions: Could Fetkovich's b-values recommendation (mostly b<= 0.4 ~ 0.5 for single gas layer, b>=0.5 for multi-layer without crossflow) still be applied to the tight gas reservoirs? In a multi-stacked shoreface reservoir, does the lower perm zone contribute to the overall production? Moreover, how and when will it influence the production profile? When using the Arps’ decline curve analysis, what are the reasonable b-values to be used for the tight/stacked gas reservoirs? Both synthetic production decline profiles generated from simulation runs and a field example are presented in this paper to illustrate this new proposed methodology and to answer the above three questions.

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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)0.002
metaresearch head score (Gemma)0.001
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: Methods · Consensus signal: Methods
Teacher disagreement score0.104
Threshold uncertainty score0.688

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 science0.0010.000
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 head0.142
GPT teacher head0.349
Teacher spread0.207 · 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