Application of Inflow Tracers to Complement Production Logs in a Carbonate Field
Bibliographic record
Abstract
Abstract The use of classical production logging tools (PLT) for the purpose of inflow profiling has occurred for decades in the Greater Ekofisk Area (GEA) wells. Recently, several of the newly drilled wells have been completed with inflow tracer (IFT) technology to trial an alternative means of acquiring an inflow profile. The results and lessons learned from employing both methods on the 2/7-S-35 well in the Eldfisk field are shared in this paper. The IFTs are directly installed on the outside of the reservoir liner adjacent to each sleeve. The initial acid stimulation is key for releasing and displacing the tracers into the reservoir. Upon production start, the tracers are flowed back continuously with the produced fluids and sampled at surface. The samples are analyzed, interpreted, and a continuous inflow profile for the well is generated. The inflow profiles derived from both IFT and PLT methods revealed differing results in the subject well. The most notable deviation was in the bottom of the well (Hod formation) where the PLT spinner did not show flow while the IFT showed a significant flow contribution (30% of total). Upon closer analysis of the PLT run, the temperature sensor data suggested flow from the bottom of the well which conflicted with the spinner sensor data. Overall, the IFT data and PLT temperature sensor data confirmed flow from the Hod formation which was also consistent with performance from offset analog wells. Both methods offer an inflow profile solution at a similar cost but come with their own strengths and weaknesses. Careful design and application are recommended to increase the chances of capturing a representative inflow profile for a given well. The novelty of this paper is in the implementation of the patented rate-dependent method for generating IFT-based continuous inflow profiles for the subject well. The IFT provided a more credible inflow profile than the PLT and this data supported the decision to drill a dedicated well to further explore the Hod formation potential in the Eldfisk field.
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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) | 0.000 | 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 | 0.000 | 0.001 |
| 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".