Technology Focus: Reservoir Performance and Monitoring (September 2017)
Bibliographic record
Abstract
Technology Focus Since the last Reservoir Performance and Monitoring feature in September 2016, the industry trends of significantly improving efficiency and reducing operational costs have continued to be implemented. For instance, at the time of writing, in North America, the US oil rig count has risen impressively for 23 straight weeks and the big players have greatly reduced their exposure to Canada’s oil sands. However, while many efforts are focusing on the optimization of current technologies and the study of past reservoir performance to improve future developments, with fewer capital resources and personnel available, these efforts may yield only incremental improvements. Technology and innovation are seen industrywide as critical to long-term radical efficiency and productivity. Once the industry becomes less concerned about cost savings and more about investing in future technologies and long-term performance, the nonrisk-averse innovation culture from other industries could help us develop new disruptive technologies and implement them in the field. For instance, with the proper resources in place, automated reservoir-performance modeling and monitoring may no longer be a science-fiction scenario. Once this downturn appears in the rear-view mirror, our industry will need to change its model disruptively to thrive sustainably in the next growth cycle. During the past 12 months, 160 technical papers were presented at various conferences and meetings with reservoir-performance-and-monitoring programs and were reviewed for this feature, displaying further advances in reservoir-performance monitoring, analysis, and optimization. The papers selected and recommended as additional reading are representative samples of the reviewed papers. They are a geographically diverse mix of academic work, industrial research and development, and field applications, describing numerical simulation and laboratory research, field-data-acquisition and -interpretation studies, new-technology development and field trials, and multi-year reviews of current technologies and work flows. Recommended additional reading at OnePetro: www.onepetro.org. SPE 181550 Current State and Future Trends in the Use of Downhole Fluid Analysis for Improved Reservoir Evaluation by H. Elshahawi, Shell, et al. SPE 184131 Production Optimization Through Voidage Replacement Using Triggers for Production Rate by Cenk Temizel, Aera Energy, et al. SPE 183195 Development of Crosswell Electromagnetic Monitoring System Using the HTS-SQUID Magnetometer by Makoto Harada, Japan Oil, Gas, and Metals National Corporation, et al.
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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 | 0.001 | 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".