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Record W1975118610 · doi:10.2118/96962-ms

The Effects of Proppant Selection Upon Well Productivity—A Review of Over 650 Treatments in the Cardium Formation

2005· article· en· W1975118610 on OpenAlexaboutno aff
C. M. Rightmire, T. T. Leshchyshyn, M. C. Vincent

Bibliographic record

VenueSPE Annual Technical Conference and Exhibition · 2005
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsProductivityTonneGeologyStructural basinPetroleum engineeringCretaceousWell stimulationFracture (geology)Sedimentary rockEnvironmental scienceReservoir engineeringGeochemistryGeotechnical engineeringPaleontologyEngineeringWaste managementPetroleum

Abstract

fetched live from OpenAlex

Abstract Effects of proppant selection on well productivity are demonstrated in a large case study covering 2,300 square miles [6,000 square kilometers] in Alberta, Canada. In 80% of 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 (WCSB) 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 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 2,200 lbm [1 tonne] to nearly 407,000 lbm [185 tonne] of proppant per well in one to five stages. Analyses suggest that significantly greater economic return has been achieved when fracture designs are optimized. In this study, the most common design was 132,000 lbm [60 tonne] of proppant placed with a hydrocarbon-based fluid. For this treatment design, the average first year production for wells receiving 132,000 lbm [60 tonne] of sand was 302 MMscf [8.5 × 106 m3] of gas. Wells stimulated with 132,000 lbm [60 tonne] of ceramic proppant averaged 420 MMscf [11.9 × 106 m3] 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%. Production from Cardium oil wells was also found to increase with proppant concentration and with proppant size. A preliminary review suggests that oil production has been significantly improved with higher conductivity fractures. While a full statistical review remains underway, the initial comparisons suggest that further increases in proppant conductivity should be considered. Additional information is provided to assist fracture optimization strategy for both oil and gas wells in the Cardium formation.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.912
Threshold uncertainty score0.176

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.006
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.236
Teacher spread0.229 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations6
Published2005
Admission routes1
Has abstractyes

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