Optimized Cased and Perforated Completion Designs Through the Use of API RP-19B Laboratory Testing to Maximize Well Productivity
Bibliographic record
Abstract
Abstract A detailed perforating study was conducted for a high pressure and high temperature (HPHT) reservoir which provided challenging conditions for conventional perforating. To maximize productivity, a series of API RP-19B Section 2 and Section 4 experiments was conducted to optimize perforation conditions. These results were integrated with mud testing and operational considerations to create an overall completion design for the field. Section 4 tests were performed at static overbalance, static underbalance and dynamic underbalance conditions scaled to field conditions using either mud or base oil as the completion fluid. The core flow efficiency and perforation geometry were evaluated to determine the optimum perforation method to achieve the target skin. The Section 4 apparatus could not achieve absolute field pressure and temperature conditions, therefore to ensure the required perforation geometry could be achieved downhole, Section 2 tests were conducted at the HPHT field conditions of reservoir overburden stress (13,000 psi), reservoir pressure (>11,000 psi) and temperature (>300 F). The results showed that dynamic underbalance removed significant portions of the perforation crushed zone and resulted in high productivity flow even when perforating in mud. Static underbalance was significantly less effective in removing crushed zone damage and overbalanced perforating in mud yielded poor results. Perforation geometry was radically altered upon going from relatively low stress conditions to full HPHT reservoir conditions when the cores were saturated in a light mineral oil. This change in perforation geometry was not observed when the cores were saturated in water, indicating that the fluid compressibility may have a significant impact on perforation geometry under high stress conditions. These results point to the value of conducting Section 2 and Section 4 experiments early in a project's timeline so that the best completion designs can be pursued and ultimately used in the field to maximize well productivity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".