Correction of Bound and Free Fluid Volumes in the Timur-Coates Permeability Equation for the Presence of Heavy Oil—A Case Study From the Golfo San Jorge Basin, Argentina
Bibliographic record
Abstract
Abstract In the Golfo San Jorge basin, Argentina, the Timur-Coates permeability index obtained from the T2 distributions is considered as a good indicator of reservoir quality and has been used as a very important variable for production forecast. However, when heavy oil is present, having a relaxation time below the standard free fluid-bound fluid cut-off of 33 ms, it is conventionally counted as part of the bound fluid, independently of its mobility. For this reason, in case of movable heavy oil, the standard Timur-Coates permeability index using 33 ms tends to be always pessimistic, in eventual disagreement with other reservoir quality indicators as the SP curve, depositional environment, cuttings and production data. In order to perform a correction of the permeability index for the presence of heavy oil, two layers of one well from the Diadema field, with a complete set of SP, Resistivity, MREX and production data was selected and evaluated using 2DNMR (T2intrinsic - Diffusion) maps, which uses the diffusivity contrast for discriminating between capillary bound water and heavy oil, within the bound fluid window (BVI). The clay bound water (CBW) cut off has been chosen to be 6 ms. The results show that the corrected Timur-Coates permeability can increase by an order of magnitude in the tested zone of the reservoir layers, but can become even higher within the whole layers, which is a reasonable estimation for the corresponding channel depositional environment. The production data also support the interpretation, indicating that the NMR rock quality estimation can be performed more accurately even in the presence of heavy oil. The corrected Timur-Coates permeability values can be used in a future update for forecasting well production.
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 | 0.001 | 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) | 0.000 | 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".