Bibliographic record
Abstract
Technology Focus Simulation is about building a mathematical model of the reservoir and then using it to predict, for the purpose of making decisions. That may be restating the obvious, but there are a few points that bear a little re-emphasis. First, it is only a model. Second, if the model does not predict adequately, it does not serve its purpose. There is a growing tendency in some quarters to use very simple models—mainly because they are faster. As my colleague Ed May recently put it, “I know everyone pushes for faster, faster, but doing it right has definite advantages.” Following my recent move into well testing, I have been rereading the late Laurie Dake’s The Practice of Reservoir Engineering. Dake often is regarded as hostile to simulation, but what he really was about was understanding your assumptions, and using the most appropriate model for the decision to be made. At the 2011 SPE Reservoir Simulation Symposium, many papers focused on re-examining original basic assumptions. Correcting or removing those assumptions (improving the model) often led not only to better predictions, but to faster simulation. For example: Is the most recent solution of the well model the best starting point for the next solution? As the industry develops more-complex reservoirs (e.g., shales, naturally fractured, and heavy oil), we are called on to apply our simulation tools to new environments, and we must check constantly that the assumptions that are implicit in those tools are still valid. For example: Do assumptions for light-oil flow in homogeneous sandstone also apply to gas flow in shale? The need to check assumptions applies as much to the data as to the tools. In simulation support, we often see data sets in which hundreds of warning messages about bad data are ignored or even suppressed, and the complaint is about the final failure to converge. Is that discontinuity in the pressure/volume/temperature data really a property of the fluid, or just a measurement artifact? A good way to consistently challenge your assumptions is to read the best of the best technical articles: JPT is here for you! Reservoir Simulation additional reading available at OnePetro: www.onepetro.org SPE 144401 • “Design and Examination of Requirements for a Rigorous Shale-Gas-Reservoir Simulator Compared to Current Shale-Gas Simulators” by Juan Andrade, SPE, University of Oklahoma, et al. SPE 140882 • “Multilateral Complex-Well Optimization” by P.I. Crumpton, Schlumberger, et al. SPE 137507 • “Reservoir Characterization and Flow Simulation of a Low-Permeability Gas Reservoir: An Integrated Approach for Modeling Tommy Lakes Gas Field” by H. Deng, University of Calgary, et al. (See Journal of Canadian Petroleum Technology, May 2011, page 32.) SPE 141935 • “An Adaptive Newton’s Method for Reservoir Simulation” by Pengbo Lu, ExxonMobil, et al. SPE 142093 • “A New Method To Model Relative Permeability in Compositional Simulators To Avoid Discontinuous Changes Caused by Phase-Identification Problems” by Chengwu Yuan, SPE, University of Texas at Austin, 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".