Estimation of Oil-Water Contact Level Using Different Approaches: A Case Study for an Iraqi Carbonate Reservoir
Bibliographic record
Abstract
In petroleum industry, an accurate description and estimation of the Oil-Water Contact(OWC) is very important in quantifying the resources (i.e. original oil in place (OIIP)), andoptimizing production techniques, rates and overall management of the reservoir. Thus,OWC accurate estimation is crucial step for optimum reservoir characterization andexploration. This paper presents a comparison of three different methods (i.e. open holewell logging, MDT test and capillary pressure drainage data) to determine the oil watercontact of a carbonate reservoir (Main Mishrif) in an Iraqi oil field "BG”. A total of threewells from "BG" oil field were evaluated by using interactive petrophysics software "IPv3.6". The results show that using the well logging interpretations leads to predict OWCdepth of -3881 mssl. However, it shows variance in the estimated depth (WELL X; -3939,WELL Y; -3844, WELL Z; -3860) mssl, which is considered as an acceptable variationrange due to the fact that OWC height level in reality is not constant and its elevation isusually changed laterally due to the complicated heterogeneity nature of the reservoirs.Furthermore, the results indicate that the MDT test can predict a depth of OWC at -3889mssl, while the capillary drainage data results in a OWC depth of -3879 mssl. The properMDT data and SCAL data are necessary to reduce the uncertainty in the estimationprocess. Accordingly, the best approach for estimating OWC is the combination of MDTand capillary pressure due to the field data obtained are more reliable than open hole welllogs with many measurement uncertainties due to the fact of frequent borehole conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".