Nuclear Magnetic Resonance NMR, A Valuable Tool for Tar Detection in a Carbonate Formation of Abu Dhabi
Bibliographic record
Abstract
Abstract During the geological history, water borne bacteria may react with oil and result in the formation of tar (synonymous of bitumen in this paper). The dynamics of the reservoir over its history may also leave some tar within the hydrocarbon zones. Tar occupies a portion of the pores and may plug partially or fully the pore throats, significantly affecting the fluid flow in the reservoir. Detection of tar is of high significance in the field development for understanding the recovery and effectiveness of water/gas injection. Tar can be easily identified in the water zones using the resistivity response. In the oil zone, it is difficult to separate tar and hydrocarbons by using exclusively a resistivity log. NMR transverse T2 relaxation contains useful petrophysical and geological information. The T2 histogram is a function of both fluid properties and pore size distribution. Tar is almost solid and the hydrogen it contains relaxes very fast because of its strong binding forces. The shortened T2 in the presence of tar results in lower NMR porosity, compared to the conventional Density-Neutron porosity. The missing porosity from NMR along with other conventional logs and wireline formation tester can be reliably used in the evaluation of tar in the formation. The results can be quantified with confidence after calibrating with core results. Two examples are presented from the carbonate formations of Abu Dhabi, in the Middle East, where tar evaluation was successfully performed using NMR, Density-Neutron and MDT data.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".