Methodological Approach for Optimization of Completion Practices in Mature Carbonate Fields
Bibliographic record
Abstract
Abstract Technology advances, coupled with favorable commodity prices, have made it possible for operators to revive mature oil fields once considered depleted. This paper intends to illustrate how using existing wellbores to prove development potential and refine completions practices jump started one such revival in the Permian Basin. The team used a methological approach to prioritize projects, optimize stimulations, minimize the impact of wellbore integrity issues, and drive down costs. Though this paper focuses on work implemented in a mature field environment, any field with a major review of the existing completion strategy can use the methodology outlined. Technical discussions focus on stimulation optimization techniques and wellbore integrity assurance. The workflows for project prioritization and cost reduction are also addressed. The methological approach led to reduction of fracture stimulation expenses by fifty %, reduction in rig time, and reduction of contingency cost associated with well integrity. As a result, otherwise uneconomic resources were added and future development opportunities were identified. Various sand-fracturing stimulation placement and diversion techniques were field tested and compared using post-stimulation production results and after-frac radioactive tracers. Despite the savings offered by diversion with ball sealers, isolation using mechanical tools was significantly more effective and justified the additional costs. Resin-coated sand concentration for flowback control was analyzed and reduced from 50% to 10% based on findings. The team was able to reduce pad volumes and increase sand concentrations resulting in improved fracture conductivities and reduced costs. Field supervisors were trained to analyze net pressure response and adjust pumping schedule on the fly. The well integrity communication and information workflow was implemented, reducing costs and operations downtime. Major workover risks were identified, recorded, and quantified, improving project contingency planning and resource allocation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".