Evaluating a Complex Low-Resistivity Pay Carbonate Reservoir Onshore Abu Dhabi: From Model to Implementation
Bibliographic record
Abstract
Abstract Early identification of low resistivity pay (LRP) reservoir is vital in assessing its prospect and capability. Productive reservoirs may exhibit low resistivity and consequently, their potential is simply overlooked. Remapping these intervals can have significant production and reserve implications. Traditionally, resistivity logs are used to identify pay intervals due to the resistivity contrast between oil and formation water. However, when pay intervals exhibit low resistivity, such logs return low confidence in defining hydrocarbon potential. Due to the complexity of low resistivity pay (LRP), its cause and proper mitigation should be determined prior to applying a solution. Researchers have identified several reasons responsible for this occurrence; among which are the presence of heterogeneous pore structures specifically micro-porosity, fractures, paramagnetic minerals, and deep conductive mud invasion. Almost all preceding publications assume a technique will work but not the other. However, this is the first time, to our knowledge; an integrated approach is used to develop LRP assessment workflow. We have integrated the information coming from geology (e.g., thin-section, XRD), formation pressure and well tests, NMR, MICP, and dean stark data. The integration successfully identified and remapped the carbonate low resistivity reservoir. This model was validated in an appraisal well on Abu Dhabi mainland, for that an extended data was acquired. Thereafter, the integrated LRP model was compared with the computed water saturation from conventional resistivity tools. The validation was successful in terms of confirming the prognosis. Interpreting the results from the multidisciplinary integrated model confirms a deeper Free Water Level (FWL), hence oil pool extension. Further analysis showed that the causes of LRP in this considered formation was limited to presence of micro-porosity and high saline mud invasion.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".