On determination of interwell connectivity under immiscible gas injection process: Modified capacitance‐resistance model
Bibliographic record
Abstract
Abstract Interwell connectivity is an important parameter in reservoir management and optimization during water/gas injection. As an analytical approach, the capacitance‐resistance model (CRM) is a rapid tool that only needs some common and available data for field performance prediction. The non‐linear signal processing technique is used with the CRM to determine reservoir continuity between production and injection wells. Current CRMs are applicable in water flooding systems. In this study, a modified capacitance‐resistance model (M‐CRM) for interwell connectivity calculation in immiscible gas flooding projects is developed based on the mass balance equation. Slightly compressible flow is one of the main assumptions in CRM development while gas is compressible and the gas property variation with pressure should be scrutinized in the equations. Therefore, new equations need gas PVT properties to consider the effect of gas compressibility. Moreover, the productivity equation for oil and gas production should be revised. The constructed model that considers mass balance and the productivity equation is applied in two synthetic models and one real sector reservoir model. The genetic algorithm, as an optimization tool for solving the new model, is used and streamline simulation is selected as a validation tool for interwell connectivity parameter calculation. Based on the streamline results, it was observed that a M‐CRM is able to predict the reservoir behaviour better than a common CRM for pre/post breakthrough conditions in gas injection scenarios. Also, an analysis is performed for different parameters that affect the new model in immiscible gas flooding. Results show that modification on the mass balance equation has a greater influence than the productivity equation.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".