A Novel Strategy for Reducing Well Cost in the Piceance Basin by Using ECD Measurements and Air Injection Through a Parasite Aerating String
Bibliographic record
Abstract
Abstract Drilling on top of the Mesa in the Piceance Basin presents a significant loss circulation and stuck pipe challenge to operators wanting to exploit the huge gas reserves in the area. Operators have experienced losses that exceed 4000 barrels of mud when the intermediate section is drilled using conventional techniques. This is due to a combination of natural fractures and weak rock. Various strategies have been deployed to tackle the problems, including under-balance drilling operations and Direction Casing While Drilling (DcWD). A new technique described in this paper is now the best practice for ConocoPhillips in the area. It involves acquiring realtime circulating density (ECD) measurements and control of mud weight in the annulus, using direct air injection through a parasite aerating string (PAS). During the development stages of this new process an annular pressure sub (APWD) was run to gather diagnostic data. Analysis of the data shows conventional drilling practices often yield up to 3 ppg variation in circulating density exposed to the formation. Analysis of the data also suggests there is a fracture reopening gradient of approximately 8.3 ppg and there are huge circulating density variations during connections. The new strategy shows these wells can be drilled with an ECD in the range of 5–7 ppg using conventional water based mud systems. This strategy allows wells with a very narrow mud weight window to be drilled safely. This simple approach avoids the use of complex multiphase models, giving the flexibility to quickly deploy the technique to the well site without the need for expert personnel. The information enables the driller to control and keep the ECD within recommended limits, delivering a safe and productive well. The alternative approach of using conventional well design techniques would result in multiple casing strings and cost overruns, while more advanced techniques such as DcWD and under-balance drilling would require specialized equipment and crews. This new technique uses existing and common drilling technologies along with new software tools for geomechanics analysis and drilling surveillance to achieve excellent results. This paper presents a simple risk management technique using today's conventional technologies to successfully manage loss circulation risk in the Piceance basin.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".