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Record W2092691260 · doi:10.2118/169843-ms

Estimating Long Term Well Performance in the Montney Shale Gas Reservoir

2014· article· en· W2092691260 on OpenAlexaboutno aff
Vu P. Dinh, Brad A. Gouge, Aaron J. White

Bibliographic record

VenueSPE Hydrocarbon Economics and Evaluation Symposium · 2014
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPetroleum engineeringOil shaleExponential functionPermeability (electromagnetism)GeologyTerm (time)Soil scienceMathematicsMathematical analysisChemistry

Abstract

fetched live from OpenAlex

Abstract Establishing long term production decline in unconventional reservoirs is challenging due to the high degree of uncertainty associated with well and reservoir properties. When Arps’ hyperbolic rate decline method is applied in extremely low permeability reservoirs, we usually obtain b-parameter values higher than 1 which may lead to overestimation of future production. One practical way to constraint future production is to switch from hyperbolic to a terminal exponential rate decline at a specified time (a.k.a. "modified hyperbolic relation"). Since most Montney Shale Gas horizontal wells have not reached stabilized boundary dominated flow due to low matrix permeability, the difficulty with using this technique is that the terminal exponential decline rate cannot be established in advance and typically must be specified from experience. This paper presents one practical approach to establishing the terminal exponential decline rate for the Montney Shale Gas in Canada. Using the analysis techniques proposed by Blasingame and Lee (1986), important formation characteristics can be estimated analytically from post transient exponential decline. By evaluating long-term production trends of existing vertical wells, reservoir pore volume can be determined along with other naturally-fractured reservoir characteristics such as matrix) fracture permeability ratio and dimesionless fracture storage. Since horizontal wells’ production performance is strongly influence by the same natural fracture reservoir characteristics, this easy to use approach provides a reasonable estimate of terminal exponential decline parameters.

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)0.002
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 score0.033
Threshold uncertainty score0.540

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.011
GPT teacher head0.227
Teacher spread0.215 · 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

Citations8
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueSPE Hydrocarbon Economics and Evaluation SymposiumSame topicHydraulic Fracturing and Reservoir AnalysisFrench-language works237,207