An Investigation of Pressure and Production Data Using Decline and Type Curve Analysis
Bibliographic record
Abstract
Decline curve analysis is one of the most widely used production data analysis technique for forecasting whilst type curve analysis is a graphical representation technique for history matching and forecasting. The combination of both methods can estimate the reserves and the well/reservoir parameters simultaneously. The purpose of this study is to construct the new production decline curves to analyze the pressure and production data. These curves are constructed by combining decline curve and a type curve analysis technique that can estimate the existing reserves and determine the other well/reservoir parameters for gas wells. The accuracy of these parameter estimations depends on the quality and type of the pressure and production data available. This study illustrates the conventional decline curve that can be used to analyze the gas well performance data with type curves based on pseudo time function. On the other hand, log-log plots are used as a diagnostic tool to identify the appropriate reservoir model and analogous data trend. Pressure derivative and type curves are used to construct a radial model of the reservoir. In addition, Blasingame and Fetkovich type curves analysis are also presented in a convenient way. The decline curve analysis shows steady state production for a long time, then a decline is observed which indicates a boundary dominated flow. The Blasingame type curve matching points is going downward, which indicate the influence of another nearby well. The reservoir parameters that are obtained by using the decline curve and type curves analysis show a similar trend and close match for different approaches. These observations closely match results of different analysis. This analysis improves the likelihood of the results being satisfactory and reliable, though it changes with time until the end of the production period. This analysis technique can be extended to other type of well/reservoir system, including horizontal wells and fractured reservoirs.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".