Pressure Normalized Decline Curve Analysis for Rate-Controlled Wells
Bibliographic record
Abstract
Abstract Decline curve analysis based on rate-time data is the standard method of evaluating on-shore North American oil and gas reserves. Aside from the classical hyperbolic curve fitting methodology originally proposed by Arps (1945), there have been several new empirical techniques developed for unconventional shale and tight gas reservoirs such as the stretched exponential method (Valko et al., 2010) and Duong method (Duong, 2011). The successful application of methods based only on rate data, however, is usually limited to wells with constant (or close to constant) flowing pressure. High deliverability unconventional plays such as the Haynesville and Eagle Ford are characterized by rate-controlled flow for extended periods of time. Interpretation of these flow periods using rate-time techniques can be misleading, as the bulk of the reservoir response is contained in the flowing pressure data. In this paper, we propose a straightforward methodology for forecasting the production of high pressure unconventional wells under controlled drawdown. The methodology involves the use of a pressure normalized decline curve, and therefore requires measurement of wellhead flowing pressures. As we will show, the results are consistent with those of more complex analytical and numerical models, but the method does not require any knowledge about, nor does it make any assumptions about the physics of fluid flow in the reservoir. The approach is systematic and repeatable, making it an ideal reserves evaluation tool. The method is validated using synthetic and field data.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 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".