MétaCan
Menu
Back to cohort
Record W2007594740 · doi:10.2118/2005-217

Field Results: Effect of Proppant Selection on Well Productivity-Cardium Formation

2005· article· en· W2007594740 on OpenAlexaboutno aff
T.T. Leshchyshyn, M. C. Vincent, Claude M. Rightmire

Bibliographic record

VenueCanadian International Petroleum Conference · 2005
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsCitationProductivityDownloadLibrary scienceSelection (genetic algorithm)Computer scienceOperations researchArchaeologyEngineeringGeologyWorld Wide WebGeographyArtificial intelligenceEconomics

Abstract

fetched live from OpenAlex

Abstract Effects of proppant selection on well productivity are demonstrated in a large case study covering 6,000 square kilometers (2,300 square miles) in Alberta, Canada. In 9 of 11 cases studied, wells fractured with ceramic proppant provided significantly higher gas production rates compared to wells propped with sand or other materials. The most frequently stimulated formation in the Western Canadian Sedimentary Basin is the Cardium formation of the Late Cretaceous period. Records indicate that across the basin the Cardium formation has received over 12,500 fracture stimulations during the last 50 years. This study includes a review of 1,600 wells currently operated by 96 companies. On average, 156 new wells have been drilled annually since 2000. Record numbers of new wells were completed in 2004, and the number of Cardium wells completed in the last four years exceeds the total from the preceding two decades. A detailed database containing available fracture treatment and production data was compiled from government records and service industry sources. This paper summarizes a study of over 750 well stimulations. Various stimulation strategies have been employed in the Cardium development. This paper examines productivity of hydraulic fractures propped with various materials and placed with a variety of fluid systems. Wells in this study were stimulated with as low as one tonne (2,200 lbm) to nearly 185 tonnes (407,000 lbm) of proppant per well in one to five stages. Analyses suggest that significantly greater economic return can be achieved when fracture designs are optimized. In this study, the most common design was 60 tonnes (132,000 lbm) of proppant placed with a hydrocarbon-based fluid. For this treatment design, the average first year production for wells receiving 60 tonnes of sandwas 8.5(106) m3 (302 MMscf) of gas. Wells stimulated with 60 tonnes of ceramic proppant averaged 11.9(106) m3 (420 MMscf) production during the first year. Benefits vary with job size, fluid type, and other factors. The incremental cost of ceramic proppant is usually recovered within 30 days, generating a significant increase in profitability. At current gas prices, average return on investment achieved by optimizing proppant selection greatly exceeds 100%. Additional information is provided to assist fracture optimization strategy in the Cardium development. Introduction Hydraulic fracturing is required to achieve economic production rates from most Cardium gas wells; in fact, records indicate over 12,500 stimulation treatments have been performed within this formation throughout the Western Canadian Sedimentary Basin (WCSB). Despite this extensive experience, no clear consensus has emerged from the various operators on fracture design optimization. The purpose of this paper is to evaluate the productivity achieved with various treatments. A detailed database was compiled from government and service industry records. Stimulation treatments are classified by fracturing fluid type, proppant type, and proppant quantity placed. Description of Well Population The study area includes 1,600 wells located between 52– 18W5 and 56–23W5. This area encompasses portions of the Ansell, Cecelia, Dalehurst, Edson, Lambert, Medicine Lodge, Nosehill, Obed, Oldman, Peppers, Pine Creek, Sundance and Wild River fields.

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 score0.410
Threshold uncertainty score1.000

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 head0.006
GPT teacher head0.209
Teacher spread0.202 · 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

Citations1
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueCanadian International Petroleum ConferenceSame topicHydraulic Fracturing and Reservoir AnalysisFrench-language works237,207