Integration of Production Analysis and Rate-Time Analysis via Parametric Correlations — Theoretical Considerations and Practical Applications
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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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".