Gas Flood-Flow Pattern Evaluation: A Successful Interwell Field Study
Bibliographic record
Abstract
Abstract A successful reservoir flood depends on a properly designed injection/production pattern. Knowledge of the flow pattern within a reservoir leads to a better design of the injection pattern, which in turn enhances overall recovery. To achieve this goal an interwell tracer program was used to characterize the flow stream within the current injection/production pattern of the central part of the Mesozoic Chiapas-Tapasco Basin. The field includes one injector and four producers. Due to production, the average reservoir pressure fell and approached the dew point at reservoir conditions. Natural gas was injected to maintain reservoir pressure and optimize condensate production. A chemical interwell tracer was also injected to detect and evaluate the magnitude of any communication between the injection and production wells and to track the areal flow pattern within the field. The study was designed for 18 months and commenced in December 2000. The results from the sample analyses provided information on the magnitude of highly permeable channels between the injector and producers as well as flow pattern within the field. Furthermore, the results provided valuable information on breakthrough time, which in turn shall lead to a more efficient gas injection design.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".