Optimized Gas-Well Stimulating Using CO2-Miscible, Viscosified Hydrocarbon Fracturing Fluids
Bibliographic record
Abstract
Optimized Gas-Well Stimulating Using CO2-Miscible, Viscosified Hydrocarbon Fracturing Fluids Robert Taylor; Robert Taylor Halliburton Search for other works by this author on: This Site Google Scholar Abe Khallad; Abe Khallad PetroCanada Oil and Gas Search for other works by this author on: This Site Google Scholar Alick Cheng; Alick Cheng Halliburton Search for other works by this author on: This Site Google Scholar Bob Barree; Bob Barree Barree & Associates Search for other works by this author on: This Site Google Scholar Jennifer Byrnes; Jennifer Byrnes PetroCanada Oil and Gas Search for other works by this author on: This Site Google Scholar Karen Kelly; Karen Kelly PetroCanada Oil and Gas Search for other works by this author on: This Site Google Scholar Mike Conway; Mike Conway Stim Lab., Inc. Search for other works by this author on: This Site Google Scholar Ray Caufield; Ray Caufield PetroCanada Oil and Gas Search for other works by this author on: This Site Google Scholar Sheri Tourigny Sheri Tourigny PetroCanada Oil and Gas Search for other works by this author on: This Site Google Scholar Paper presented at the SPE Gas Technology Symposium, Calgary, Alberta, Canada, April 2002. Paper Number: SPE-75666-MS https://doi.org/10.2118/75666-MS Published: April 30 2002 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Taylor, Robert, Khallad, Abe, Cheng, Alick, Barree, Bob, Byrnes, Jennifer, Kelly, Karen, Conway, Mike, Caufield, Ray, and Sheri Tourigny. "Optimized Gas-Well Stimulating Using CO2-Miscible, Viscosified Hydrocarbon Fracturing Fluids." Paper presented at the SPE Gas Technology Symposium, Calgary, Alberta, Canada, April 2002. doi: https://doi.org/10.2118/75666-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE Unconventional Resources Conference / Gas Technology Symposium Search Advanced Search Abstract The use of CO2-miscible, viscosified hydrocarbon fracturing fluids has been proven to be a very effective gas-well stimulation technique in Canada and the United States. Fluid recovery is enhanced after stimulation by this process. Anderson and Gruber presented the original concepts used in paper CIM 95-45, entitled "Carbonated Hydrocarbons for Improved Gas Well Fracturing Results." 1This paper provides results of wells recently stimulated. Simplifications to the fluid and job design processes, and additional design considerations are introduced. New CO2 solubility data generated at 50°C, 85°C, and 120°C with 30%, 40%, and 50% CO2 in four different fracturing fluids are presented to provide a wider range of design data. Compositional data for a number of specialty fracturing fluids are introduced to illustrate their enhanced fluid-recovery capability compared to heavier fluids such as kerosene and diesel. Keywords: barree, optimized gas well stimulation, khallad, carbonated hydrocarbon, hydraulic fracturing, stimulation, methane, bubble point, society of petroleum engineers, proppant Subjects: Hydraulic Fracturing, Fluid Characterization, Fracturing materials (fluids, proppant), Well Intervention Copyright 2002, Society of Petroleum Engineers You can access this article if you purchase or spend a download.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".