Bibliographic record
Abstract
Technology Focus The Future Is Already Here, Today! What are the future trends in reservoir management and monitoring, and how will new technology and new ways of communicating change the way we manage our reservoirs? Over the past couple of decades, we have seen significant progress in surveillance, data gathering, and computing capabilities. What are the biggest challenges and opportunities going forward? As the light-oil component of global production decreases, increasing the emphasis on heavy-oil, oil-sand, and natural-gas production will pose new challenges. The number of sensors is increasing, the amount of data is increasing, real-time data are common, and automated analysis is becoming commonplace. How do reservoir-management practices change to accommodate this trend? As technology advances and as nanotechnology enters the stage, our ability to translate data into information and our ability to make decisions on the basis of this information may improve. How will automation and closed-loop reservoir management influence our risk-handling and management decisions? These were among the questions discussed at the SPE Reservoir Surveillance and Data Acquisition 2020 Forum in Santa Fe, New Mexico, May 2011. Automated adjustment of chokes and automated choking of zones are possible today. To use this automation fully, we need to trust the data flow, the analysis of the information, and that the correct action is implemented. Safety concerns are another key aspect to consider. Regardless of the challenges, automation is an emerging trend. Spatial gathering of data also is getting increasingly more focused. This gathering could be crosswell information, data along the well path, or a collection of time-lapse (4D) or electromagnetic data. Integration of different information and data is key to future success. Geological and reservoir models still are the most common way of integration, but generation of data-driven models honoring the physics is one of the new trends. Integration is mostly about the integration of people, making all data available to everybody at the same time and in a format that can be understood by everyone. The selected papers are excellent examples of emerging trends and represent the shape of things to come in reservoir management and performance monitoring. Reservoir Performance and Monitoring additional reading available at OnePetro: www.onepetro.org SPE 131370 • “Using Downhole-Temperature Measurements To Assist Reservoir Characterization and Optimization” by Zhuoyi Li, SPE, Texas A&M University, et al. SPE 134313 • “Large-Scale Laboratory Testing of Petroleum-Reservoir Processes” by David P. Yale, ExxonMobil, et al. SPE 136378 • “Selection of Decision Variables for Large-Scale Production-Optimization Problems Applied to Brugge Field” by Masoud Asadollahi, IRIS/NTNU, et al. SPE 137750 • “Unconventional Imaging for Unconventional Reservoirs” by C.J. Leskiw, University of Calgary, 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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.041 | 0.017 |
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".