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Record W1977227453 · doi:10.2118/80909-ms

A Generalized Hyperbolic Decline Equation with Rate-Time Dependent Function

2003· article· en· W1977227453 on OpenAlex
Shing-Ming Chen

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 Production and Operations Symposium · 2003
Typearticle
Languageen
FieldChemical Engineering
TopicRheology and Fluid Dynamics Studies
Canadian institutionsHusky Energy (Canada)
Fundersnot available
KeywordsHyperbolic functionFunction (biology)Production (economics)MathematicsHyperbolic partial differential equationProduction rateExponentMathematical analysisPartial differential equationEconomics

Abstract

fetched live from OpenAlex

Abstract Nearly all the decline curve equations used today are based on the Arps hyperbolic equation1, given as: Using this equation, the production rate ‘Q’ at anytime ‘t’ can be calculated from the hyperbolic exponent ‘b’, the initial production rate ‘Q0’ and its corresponding decline rate ‘D0’ at time zero. Although Equation (1) is easy to use, the variation of the decline rate with time (except b = 0) limits the applicability of the equation. For a hyperbolic decline curve (b>0), if a different production rate ‘Qi’ on the curve is used an initial rate, a different corresponding decline rate ‘Di’ needs to be identified for the equation to represent the same decline curve. Moreover, if there is a rate or reference time change in the production forecast period, the identified hyperbolic equation from the production history is no longer applicable. (1) Q(t)=Q 0 × (1+b × D 0 × t) − 1 / b In this paper, a generalized hyperbolic equation is derived to overcome the above limitations. Once a set of ‘Q0’, ‘D0’ and ‘b’ is identified from the production history, the equation can be used to predict the future rate regardless of the initial rate or time change.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.661
Threshold uncertainty score0.472

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 head0.211
Teacher spread0.201 · 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