Integrated Reservoir Characterization with Spectroscopy, Dielectric and NMR T1-T2 Maps in Freshwater Environment, Case Studies from Alaska
Bibliographic record
Abstract
Abstract Hydrocarbon identification and evaluation with conventional triple-combo logs and Archie-based equations in freshwater environments has been a challenge for petrophysicists. In the first case study from Alaska, shallow reservoir information from non-Archie based technologies including total organic carbon (TOC) from spectroscopy measurements, hydrocarbon volume derived from dielectric and NMR logs were used for quick hydrocarbon bearing zones identification, decision making on fluid sampling and early determination of oil properties. The high-resolution NMR T1-T2 maps, derived from the latest inversion, revealed the presence of viscous oil. Another targeted formation is a laminated shaly sand sequence, also in a freshwater environment. The array resistivity logs show a low contrast profile and it is more challenging for fluid identification and evaluation. Relatively high resistivity anisotropy from 3D induction suggest the formation is potentially hydrocarbon bearing. TOC from spectroscopy and water filled porosity from dielectric compared to the total porosity are direct indicators of the presence of hydrocarbons in the formation, but the interpretation is complicated by water-based mud (WBM) filtrate invasion. The stacked T1-T2 maps generated immediately after NMR logging for selected intervals, clearly showed the presence of light oil. This was confirmed by the latest wireline formation testing in the next logging run. Instead of quantitative formation properties evaluation this paper focuses on qualitative fluid identification with non-resistivity-based technologies, particularly with T1-T2 maps. NMR T1-T2 fluid mapping has been traditionally used for unconventional reservoir and is well discussed in various literatures. In our case, this technique has been applied for conventional reservoirs in the freshwater environment.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".