Buildup Evaluation of a Tight Overpressured Naturally Fractured Carbonate Reservoir with the Use of a Semi-Empirical Model
Bibliographic record
Abstract
Summary This paper examines the buildup (BU) pressure response of a vertical well that penetrates an unconventional tight naturally fractured carbonate reservoir in Mexico. Four BUs in the same well over a period of 4 months, with intermediate flow periods, suggest partial closure of natural fractures. Radial flow is dominant in the four BUs. This is recognized in semilogarithmic and pressure derivative crossplots. However, the formulations require a consistent empirical component to match the BU data. The four BU tests are evaluated with a semi-empirical dual porosity model with restricted interporosity flow. The restricted flow between matrix and fractures is the result of partial secondary mineralization (cementation) within the fractures, which can be visualized as a natural positive skin that reduces the oil flow from the matrix to the fractures. The empirical part of the method is provided by a severity exponent (SE), which helps improve the match between the BU semilog and derivative plots. The BU evaluations permit estimating several parameters of interest, including fracture capacity (k2·h), skin, storativity ratio (ω), and the extrapolated pressure (p*). Results suggest that although natural fractures are present, they tend to close once the well goes on production. Thus, the conclusion is reached that the carbonate reservoir is tight and likely stress dependent. The calculated skin goes from an improved condition around the wellbore to slightly damaged conditions, probably due to fracture closure. The value of ω increases continuously, suggesting a tendency of the reservoir to move from dual to single porosity behavior. The reservoir is overpressured (0.87 psi/ft) and the extrapolated pressures (p*) decrease because of the tight characteristics of the reservoir. However, given the large size of the reservoir, the likelihood of depletion is low. The novelty of this study is the development of a new easy-to-use semi-empirical well testing model for matching the BU pressure response of four tests performed in a well that penetrates an overpressured, unconventional, tight, naturally fractured carbonate reservoir. The tests could not be matched with conventional methods currently available in the literature.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".