Evaluation of Pressure Derivative Algorithms for Well-Test Analysis
Bibliographic record
Abstract
Proposal Pressure well tests performed during any stage of the life of a well are a very important tool to obtain fundamental reservoir characteristics. Also, they are one of the closest views obtained from the reservoir. Well testing is so important that an estimative of hydrocarbon reserves can be obtained. Also, it is possible to determine reservoir flow capacity and distance to discontinuities, among several applications. Currently, pressure derivative analysis used in well testing is a very versatile procedure for reservoir characterization. However, this parameter is affected by the noise produced by turbulence, the tool itself, and the mathematical procedure involved. Therefore, it is important to properly use numerical tools to filter data. The pressure derivative function has become the most popular technique to interpret well pressure data but it suffers of noise since it is based on numerical differentiation on discrete pressure data points. The three-point central-finite different scheme has been widely used to estimate pressure derivatives. Noise occurs when consecutive points are used. It makes difficult to carry out an interpretation. Then, it is convenient to choose points further separated from each other to reduce the noise. However, if the points are so apart, the pressure derivative will be distorted. The Spline function is a powerful tool to calculate pressure derivative data for well test applications. Its continuous character and its polinomial behavior make it very effective to mitigate noise. It matches well to the expected form of the curve. In this paper, an evaluative analysis of the pressure derivative is presented. It is performed by comparing the results from different algorithms (Spline, Bourdet, Horne, Simons, Clark and Van Golf-Racht, first degree and second degree polynomials), and observing theoretical pressure vs. time data with those provided by the chosen analytical solutions. It was found that the Spline function provides the best results to estimate pressure derivative data. Besides, the recommended procedure consists of differentiating the data and, then, filtering and/or smoothing them.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".