An Interwell Water Flood Study: Flow Pattern and Reservoir Heterogeneity Evaluation of a Northern Field Formation in Mexico, Part I: Upper Section of the Field
Bibliographic record
Abstract
Abstract A successful reservoir flood depends on prior knowledge of the flow stream within a reservoir and a proper injection/production pattern. A northern field formation in Mexico, which currently includes 44 injectors and more than 150 producers, has been under water flood since early 1965. Due to the loss of injection fluid and low sweep efficiencies a chemical interwell tracer program was designed and implemented to fully investigate the flow stream within the current injection/production pattern and to evaluate reservoir heterogeneity. However, based on the results of seismic and well test analysis and due to the large number of active injection and production wells, the entire field was divided into four sections for this interwell tracer study. This paper presents the interwell study results for the upper section of the north field, which includes seven injectors (A to G) and 36 producers (1 to 36). A detailed interwell study over a 10 month period indicated the existence of massive high-permeability channels as well the existence of a major fault initiating from this section and extending downward into the lower section of the field. Also, the results indicate the existence of several transverse faults along the main fault. Detailed graphical results to identify the flow stream, a comprehensive interpretation of the heterogeneity of the formation, and tracer travel time are also presented.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".