Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations — Theoretical Considerations and Practical Applications
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Well performance analysis in unconventional reservoirs is a challenging task because of the non-uniqueness associated with estimating well/formation properties. In addition, estimation of reserves is often uncertain due to very long transient flow periods. Recently, new semi-empirical rate-time relations (Ilk et al. 2008 and 2010) have been shown to properly model the rate-time behavior for wells in unconventional reservoirs. The success of these new rate-time relations has led us to focus on finding theoretical and empirical relationships between rate-time model parameters with well/formation properties. This work attempts to integrate model-based production analysis (i.e., semi-analytical/analytical solutions) and rate-time analysis by using parametric correlations. We perform production analysis and rate-time analysis for various tight gas and shale gas wells, and then correlate the various model parameters from the rate-time equations with the well/formation properties estimated using full (model-based) production data analysis. We demonstrate the application of the proposed methodology by using a sample of wells producing in tight gas and shale gas reservoirs. We can show that the integration of production analysis and rate-time analysis via parametric correlations is highly-dependent on the size of data sample (i.e., the number of wells) and the data quality. When high-quality data and ample production data are available, formation permeability and fracture half-length are well-correlated with the model parameters of the rate-time relations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it