Sandstone reservoir quality prediction: The state of the art
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Joanna Ajdukiewicz joined Exxon Production Research Company in 1980. She was Reservoir Quality Assessment and Prediction team lead there from 1991 to 1995 and at Imperial Oil Research Centre in Calgary from 1995 to 1997. Subsequently, she has worked a variety of Exploration Company assignments in the North Sea, Gulf of Mexico, and Middle East. Her current interests are in predicting the distribution of early diagenetic controls on deep reservoir quality. Rob Lander develops diagenetic models for Geocosm LLC. He obtained his Ph.D. in geology from the University of Illinois in 1991, was a research geologist at Exxon Production Research from 1991 to 1993, and worked for Rogaland Research and Geologica AS from 1993 to 2000. He is also a research fellow at the Bureau of Economic Geology. To guess is cheap; to guess wrongly is expensive (Chinese proverb). Reservoir-quality predictive models will be a useful element of risk analysis until remote-sensing tools are invented that accurately measure effective porosity and permeability ahead of the bit. This issue of the AAPG Bulletin highlights recent advances in a new generation of reservoir quality models that have successfully predicted porosity and permeability in diverse siliclastic reservoirs under many different burial conditions. Most previous attempts at predrill reservoir quality prediction have relied on empirical correlations or on first-principle geochemical simulations that incorporate laboratory-derived input parameters (Wood and Byrnes, 1994). The new reservoir quality models differ from previous approaches in that, although incorporating theory-inspired algorithms, they include terms with values that are explicitly designed to be calibrated by, and tested against, data sets of high-quality petrographic analyses that are linked to thermal and effective-stress histories. Petrographic observations therefore provide essential constraints in these models on the types, timing, and rates of key geologic processes affecting sandstone pore systems. This approach avoids the pitfalls inherent …
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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 it