Success of Deepening Workovers in Permian Basin Carbonates
Bibliographic record
Abstract
With the growing numbers of aging wellbores, recompletion programs help to reduce temporarily abandoned well counts, identify and prove future development potential, boost production and maximize return on investment. Typically base well completion programs have been composed of four main project types: recompletions, rework in the existing zone, sidetracks, and deepenings. This paper summarizes the lessons learned and recommendations from an active deepening campaign in the Permian Basin. Deepening projects provide a cheaper and lower risk alternative to drilling new wellbores and can achieve the same objectives of zone testing and infill drilling potential evaluation. Once infill potential is proved up through deepening projects further value and scale can be exploited through infill drilling programs. Two types of deepenings were executed, open hole and cased hole. Casedhole deepenings are more complex and costly, but must be attempted in the cases where openhole deepening is not feasible. Such cases may include deepening across water-bearing or depleted zones, deepening in prolonged pay intervals (>350ft), zones with poor hole stability or incompatible formation fluids. In a stacked pay carbonate rock the stimulation is an important part of the completion process. Therefore deepening projects may have different scopes which may include some isolation work in open or casedhole, single-stage or multi-stage acidizing and fracturing. The three key pillars which must be in place to deliver a successful deepening program are sound wellbore conditions, reliable well history, and proper equipment selection. Given that deepening projects are implemented less frequently than other well intervention activities and new drilling activity, the equipment selection and the understanding of how to operate that equipment become critical success factors in these projects. It was observed that the time and cost associated with wellbore cleanouts before deepening may drastically change the project economics. The risks associated with poor well integrity and the presence of junk or fish in the wellbore need to be quantified in the candidate selection phase. Changing from roller-cone to PDC bits and adjusting drilling parameters increased the deepening ROP by 300%. This paper compares the historical performance of 10 deepening projects. It contains a list of operational risks and follow-up plans needed to ensure cost effective and safe execution.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".