Improved Waterflood Analysis Using the Capacitance-Resistance Model Within a Control Systems Framework
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Bibliographic record
Abstract
Abstract The Capacitance Resistance Model (CRM) is a fast way for modeling and simulating gas and waterflooding recovery processes, making it a useful tool for improving flood management in real-time. CRM is an input-output and material balance-based model, and requires only injection and production history, which are the most readily available data gathered throughout the production life of a reservoir. In this work, the CRM input-output relationship is explored by representing the CRM with state-space (SS) equations. The linear system SS equations define the relationship between inputs, outputs and states to completely describe system dynamics. The SS-CRM is a multi-input/multi-output (matrix) representation, which provides more insight into reservoir behavior than analyzing performance on a well-by-well basis. Thus, it is computationally faster and easier to apply in fields with large numbers of wells. The CRM parameters are estimated using a grey-box system identification algorithm. The matrix form of the CRM history matching and a sensitivity analysis to the CRM parameters estimates are presented. Minimal realizations and reduced order models are easily obtained with the SS-CRM approach. The performance of three CRM representations are analyzed: integrated (ICRM), producer based (CRMP) and injector-producer based (CRMIP). The methodology developed here is tested in two reservoir systems, homogeneous with flow barriers and channelized. We find that the ICRM does not reproduce the rate fluctuations as well as the CRMP and CRMIP. The CRMP works well for wells in low heterogeneity regions but not as well as the CRMIP in more heterogeneous areas, e.g. near the flanks of channel deposits. This new approach facilitates closed-loop reservoir management by enabling CRM's use for linear control algorithms, which can improve tracking performance and predictability, and is amenable to real-time optimization.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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