Estimation of Interwell Connectivity in the Case of Fluctuating Bottomhole Pressures
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Bibliographic record
Abstract
Abstract Injection and production rates are often the most readily available data in a waterflood. Yousef and coworkers have shown that these data can be analyzed using a capacitance model (CM) to determine interwell connectivity and provide a tool for waterflood management. Although the existing CM performs well when tested with data from a reservoir simulator and in some field cases, it is less reliable in cases where bottomhole pressures (BHPs) are changing but not measured and/or where producers have been shut in for extended periods. This paper presents two new important developments of the CM to address these shortcomings: the segmented CM and the compensated CM. The segmented CM can be used where BHP data are unknown. The compensated CM overcomes the problem of the requirement to rerun the model after adding a new producer or shutting-in an existing producer. If both BHP changes and shut-in periods occur, both the segmented and compensated CMs can be used simultaneously to construct a single model for a longer period of data. In several simulated cases with fluctuating BHPs, the segmented and compensated CM successfully determined the true interwell coefficients. The conventional CM gives up to 70% error. In a field case, the segmented and compensated CM improved the prediction R2 by 15% compared to the existing CM. In this field example, there is good agreement between seismic impedance and results from the combination of the segmented and compensated CMs. The existing CM, which is a good beginning, requires enhancements to be more versatile. The segmented and compensated CMs will make connectivity estimation more robust because pressure data are often unavailable or human interventions (e.g., work-overs) cause the flow rates to vary. Adapting the CM to tolerate BHP changes and shut-in wells, as we have done, provides this versatility.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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) | 0.000 | 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 it