Bibliographic record
Abstract
Technology Focus Like a fingerprint, every completion is unique and gives each well its own identity. The hardware selection, its deployment method, and the associated stimulation technique combine to create a one-of-kind completion, custom tailored for each application. It is this infinite variation that constantly fuels a collective desire to bring stability to the completion process. More so than ever before, the themes of efficiency and optimization serve as a backbone for completion design and execution. These themes remain valid under any market condition but become all the more significant in challenging or volatile climates. Well completions play a critical role in the overall productivity of a reservoir, which inherently drives the decision-making processes toward well efficiency and production optimization. While there are many gains to be had around more-efficient hardware and stimulation deployment, improving the overall well efficiency and optimizing the production profile through carefully designed and implemented completion strategies represent the true endgame. The challenge lies in accessing the key ingredient that enables harmonization of all the various components: data. Today, full-field studies comparing varying completion techniques with associated production performance are commonplace to evaluate and home in on optimum completion methods. Sensing technology, common to drilling and evaluation, continues to be integrated into completions, providing a front-row seat for the main event, production. The adage “knowledge is power” could not be more relevant in this context. Data collection and analysis, and subsequent controls, are what ultimately will enable the most-efficient and -optimized wells. The papers highlighted in this feature speak to these themes that have become ever-present within the completions community. This selection of case histories and new technology reinforces the inherent desire to achieve stability through efficiency and optimization, even under the most challenging of market conditions. JPT Recommended additional reading at OnePetro: www.onepetro.org. SPE 170264 Electronic-Set Openhole Packer Installation in Campos Basin, Offshore Brazil: A Case History by G.D. Mendes, Baker Hughes, et al. SPE 170694 Acid-Soluble Plugs— Pressure-Tight Solution for a Preperforated Liner by E. Livingston, ConocoPhillips, et al. SPE/IADC 170547 Innovative Intelligent Multizone Gravel-Pack Completion Revives Production in Malaysian Brownfield by T.U. Ceccarelli, Schlumberger, et al. SPE 170738 Composite-Plug-Milling Efficiency Improvement Through Rheology Control—Lessons Learned From the Horizontal Completions in the Devernay Shale by Darren Huynh, Shell Canada, et al.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".