Morrow Production Enhanced by New, Foamed, Oil-Based Gel Fracturing Fluid Technology
Bibliographic record
Abstract
Abstract Oil-based fluids were first used in the late 1940's to fracture stimulate a variety of wells, the first of which was in Hugoton Field. Subsequently, fluids evolved with soap and aluminum phosphate esters being employed as gellants. Because gelling, gel stability, and ease of mixing have been historically problematic, a new phosphate ester system has been developed for use in formations which may be damaged by water contact. The Morrow Formation in the Mid-Continent region is known for its sensitivity to fresh water. Due to this fact, treatment designs have been implemented to reduce the amount of water placed into the formation. Foams and gelled oil treatments have been the options available. Foams, while reducing the amount of water placed in the formation, still have a water phase to deal with. Gelled oils eliminate the water phase, but do not have the benefit of the load recovery assistance provided by the gaseous phase of the foam. This paper details the treatment of the Morrow Formation in Southern Oklahoma in which carbon dioxide is introduced into the gelled oil phase. This practice, while common in Canada, has not been utilized in the United States to a great extent. This paper deals with the development and laboratory testing of the fluid system, the treatment design, and the results of such treatments in Grady County in Oklahoma.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".