Bibliographic record
Abstract
Technology Focus I suppose that many of us are taking a deep breath just now. Many of us could be revisiting how we have been completing wells and what we might be able to improve. These improvement areas often involve some sort of trade-off between well deliverability and well/completion costs (in terms of equipment and rig time to deploy these various alternatives). I suspect that we all have been involved with completions where these two areas are debated. In my experience, it seems that much of our discussion revolves around what the various participants “feel” is the best approach. Much of the decision eventually hinges on what we will do in the short term (deployment) rather than in the long term (deliverability). The reason is, in my opinion, that we are fairly sure about the near-term items (related to cost) but often very uncertain about the longer-term items (deliverability as a result of how well we deployed the lower completion). Why is it that many of our completion quality decisions are focused on cost and not deliverability? I can think of two primary reasons: (a) We lack the metrics to support our decisions and (b) we do not have consistent practices (e.g., laboratory and design work, deployment processes) across our wells to allow us to compare our results. I suspect that you can think of others. Both of these areas offer improvement opportunities. For those of us who have a robust set of metrics to evaluate our overall sand-control planning and deployment process, if the preparation work (e.g., core testing, compatibility testing, equipment selection) is not carried out in a consistent fashion, the variation in results as depicted in our metrics would not lead us to a specific course for improvement because the variation from well to well might simply be explained away by the differences in planning and execution. Given the preceding idea, could the development of consistent practices be a critical first step on our journey toward achieving improvements in completion quality? The list of those practices that we should carry out in a consistent manner is quite long. For sand-control applications, we could start with those activities that occur early in the design process. After reviewing the many high-quality technical papers written over the past year, I have found a few that I think offer a good place for you to start your journey toward consistency. The three summarized papers are all related to the selection of proppant and screens in your sand-control completions. I am not promoting any one of these papers over the others. However, I am suggesting that whatever your organization does in this area, your organization should do it consistently. You may find your organization’s new, preferred approach to proppant and screen selection in one or more of the presented articles. JPT Recommended additional reading at OnePetro: www.onepetro.org. SPE 178966 Sand-Retention Testing: Reservoir Sand or Simulated Sand—Does It Matter? by Tracey Ballard, Weatherford, et al. SPE 179036 Sand-Screen Design and Optimization for Horizontal Wells Using Reservoir Grain-Size-Distribution Mapping by Mahdi Mahmoudi, University of Alberta, 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 | 0.001 | 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".