Characterization of Well Performance in Unconventional Reservoirs using Production Data Diagnostics
Bibliographic record
Abstract
Abstract Production analysis and forecast in unconventional reservoirs are challenging tasks due to high degree of uncertainty and non-uniqueness associated with well/reservoir properties. At this point the importance of diagnosis of production data to check the data consistency and to identify flow regimes has become significant. In addition, we believe that producing wells in various unconventional reservoirs exhibit unique production performance behavior due to geology, phase behavior, and completion practices. Therefore, the identification of the related performance behavior is crucial for development and forecast purposes. The primary objective of this work is to apply diagnostic methods to investigate and understand production performance characteristics of the wells producing in unconventional reservoir systems. For our purposes, we present examples from various shale gas plays. We propose the use of various forms of rate-time and rate-time-pressure plots for production data diagnostics. In particular, this work presents the utilization of the dimensionless βq,cp-derivative formulation (i.e., logarithmic derivative of the rate function with respect to logarithm of time) to identify production behavior characteristics. We also propose the use of several diagnostic plots to identify performance characteristics and data consistency. In addition the use of average rate functions are presented for better resolution.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".