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Record W2031693024 · doi:10.2118/137456-ms

Case Studies of a Simple Yet Rigorous Forecasting Procedure for Tight Gas Wells

2010· article· en· W2031693024 on OpenAlexaffabout
Morteza Nobakht, Michael D. Morgan, Louis Mattar

Bibliographic record

VenueCanadian Unconventional Resources and International Petroleum Conference · 2010
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsTight gasSuperposition principleFlow (mathematics)Petroleum engineeringShale gasHydraulic fracturingFracture (geology)GeologyUnconventional oilInverseMechanicsOil shaleMathematicsGeotechnical engineeringMathematical analysisPhysicsGeometry

Abstract

fetched live from OpenAlex

Abstract The dominant flow regime observed in many hydraulically-fractured tight/shale gas wells is linear flow. This flow regime may continue for several years, and will ultimately become boundary-dominated flow, at much later times. Nobakht et al. (2010) introduced a simplified method of production forecasting for tight/shale gas wells which exhibit extended periods of linear flow. The method is simple as it relies principally on a plot of inverse gas rate versus square root time, and it is rigorous in that it is based on the theory of linear flow and combines the linear flow transient period with hyperbolic decline during boundary-dominated flow. In the present work, this simplified method is reviewed and applied to almost 90 wells producing from the Montney formation in N.E. British Columbia, Canada. The vast majority of these wells exhibit linear flow for extended periods of time. The advantages of the simplified forecasting method are: (1) It is not biased towards any flow regimes, as no superposition time functions are used; (2) Reliable forecasts can be obtained without invoking pseudo-time and its associated complexities; and (3) The only parameter that needs to be specified externally is the drainage area. The method can be used for forecasting horizontal wells with multiple hydraulic fractures. By assigning different drainage areas to each fracture, a relationship can be developed between expected ultimate recovery (EUR) and original gas in place (OGIP) assigned to each fracture. This translates into recovery factor versus number of fracture stages. The resulting forecasts can be used directly to examine the economics of multi-stage fracturing.

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 score0.166
Threshold uncertainty score0.943

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 head0.028
GPT teacher head0.253
Teacher spread0.225 · 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
Published2010
Admission routes2
Has abstractyes

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