Improved Drilling Performance and Economics Using Hybrid Coiled Tubing Unit on the Chittim Ranch, West Texas
Bibliographic record
Abstract
Abstract A significant performance and cost improvement was achieved with the application of hybrid Coiled Tubing (CT) drilling equipment and techniques on the Chittim Ranch in Maverick county Texas. During this drilling program, 233 wells were grass-root drilled using the hybrid CT unit. Coiled tubing drilling equipment and techniques reduced the average time to complete a well by 60% when compared to conventional rotary rig drilling. This increase in drilling performance coupled with a turnkey contract resulted in a 14% (33% adjusted for inflation) cost reduction per well when compared to the most recent conventional drilling data from this area. This paper will review the process used in choosing a CT solution, the hurdles overcome, the problems encountered, and the lessons learned in managing and operating this CT Drilling (CTD) campaign. The paper will also provide an overview of CT coring performed in one of the wells during the CTD campaign. The drilling performance increase was realized using a top-set rig to set surface casing and a hybrid "Big Wheel" CT rig to drill the production hole. Finalized project data demonstrated that field performance using CTD met and in some areas exceeded project goals.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".