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Record W2324875906 · doi:10.2118/175888-ms

Normalization of Analog Horizontal Wells for Type Curve Generation in Tight Gas Plays

2015· article· en· W2324875906 on OpenAlexaffabout
Shaoyong Yu, Jim Gouveia

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsConocoPhillips (Canada)
Fundersnot available
KeywordsWellboreNormalization (sociology)Petroleum engineeringLinearityGeologyCompletion (oil and gas wells)EngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Abstract A mature reservoir is usually characterized with a number of both vertical and horizontal wells, which may both contribute to a significant recovery to-date. When considering forecasting for new wells in the geological similar areas (GSAs), it is common practice to generate a type curve by harnessing historical production data from analog wells. But, given varying well types and completion practices (e.g. different horizontal wellbore length), the analog assumption may be challenged. When working on type curve generation, the common questions frequently asked are:Do we need to normalize analog wells for the type curve generation?How to conduct the normalization on both wellbore length and completion parameters? It seems that the answer to the first question is a very obvious yes if the new wells will be designed differently from the analog wells – particularly if the lateral length and completion method is not similar. As a result, Estimated Ultimate Recoveries (EURs) and the Initial Production (IPs) will need to be normalized from those analog wells to a desired new wellbore length. After having investigated both analytically and numerically the impacts on Recovery Factors (RFs) and the IPs from those factors of horizontal wells that include horizontal wellbore length (L), fracturing spacing/stages (nf) and drainage area (A), or well spacing, it is found that: RF will be directly affected by the wellbore length. There is linearity between RF and L when the latter is less than a certain value. The fracturing spacing/stages and drainage area (well spacing), however, will affect RF when the reservoir matrix permeability is extremely low.Also, IP has a high positive correlation with lateral length. In fact, these two variables have high linearity. In addition, fracture spacing will have a large impact on IP rates; however, drainage area will not at all. Note that these conclusions presume that wellbore hydraulic considerations are not a constraint. Further, lateral length presumes an effectively stimulated horizontal section. The specific scope of this study is to provide a systematic normalization technique. Dry gas and wet gas case studies from the Western Canadian Sedimentary Basin (WCSB) have been adopted to demonstrate the workflow. Further, sequential accumulation statistical logic has been successfully applied to validate the premise of lateral length and fracture spacing as the key normalization variables. It is believed that this methodology is rigorous for dry and wet gas reservoir systems. Moreover, this methodology is also applicable to richer gas-condensate and oil plays; however, broader relationships need to be established and tested before any conclusions can be drawn with wellbore hydraulic dynamics being taking into account as an effective factor.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.033
GPT teacher head0.256
Teacher spread0.222 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2015
Admission routes2
Has abstractyes

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