New Type Curves for Horizontal Well Rate Transient Analysis and Their Field Application
Bibliographic record
Abstract
Abstract This paper presents three sets of new integrative production decline type curves for the analysis of horizontal well production data in order to systematically estimate reservoir flowing capacity (kh), effective horizontal wellbore length and original oil/gas in place. In addition, diagnostics of dynamic flow regimes possible for the entire flow duration from early time transient to late time boundary dominated flow gives the proposed type curves unique value in field data analysis through using different idealized flow regimes as the reference systems. Comparing to the traditional classic type curves, these three sets of type curves offer more practical implemental strategy for well performance evaluation and reservoir formation characterization, resulting in less ambiguity in the outcome of data interpretation. The effects of horizontal wellbore length and reservoir geometry on the appearances and the characteristics of these three sets of new type curves are studied and documented. A detailed curve matching process has also been comprehensively developed to achieve systematically proper matching with field data using all of the three sets of type curves synchronously. To showcase this work in field application, the production dynamics of a gas field containing two vertical non-commingled units, each being produced by a horizontal well independently, is analyzed using the three sets of new type curves. With successful estimation of the kh and the effective horizontal wellbore length, it helps geologists and geophysicists locate the "sweet spot" with more confidence and offer the potential to extend the seismic response pattern to support further exploration activity in this geology unit. The outcome of this work helped identify the existence of an untapped gas reserve in the lower unit, thus to promote the potential of infill drill to enhance ultimate gas recovery.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".