Step Rate Test as a Way to Understand Well Performance in Fractured Carbonates
Bibliographic record
Abstract
Abstract Step Rate Test (SRT) is commonly used to estimate formation parting (or fracture opening) pressure for stimulated wells. SRTs may also focus on changes of well performance at different rates / pressures that is of special interest for stress-sensitive reservoirs such as fractured carbonates. Installation of permanent downhole gauges (PDG) and running SRTs on injection wells at the Ekofisk field gave a chance to improve the understanding of reservoir and well performance. Analysis of these SRTs also resulted in further development of SRT interpretation techniques. An approach to SRT interpretation, combining analytical pressure-rate (p-Q) curve analysis and step-by-step Pressure Transient Analysis (PTA) with numerical simulation of SRT, was suggested and tested. Applying this approach to Ekofisk SRTs has shown that the p-Q analysis may be used for diagnostics of well performance changes, while step-by-step PTA enables decomposing of well (skin factor) and reservoir (conductivity) effects with estimation of corresponding well-reservoir parameters as pressure or rate functions. Numerical simulation confirmed that pressure dependent conductivity estimated from the step-by-step PTA is the governing factor in matching SRT history. Special attention was paid to the uniqueness of SRT interpretation using suggested approach. Reaching infinite acting radial flow (IARF) regime at each step of a test provided unique parameter estimates as shown by example of a stimulated slanted injector. Being too far from IARF at end of each step will make the interpretations more uncertain. Different sets of changing parameters estimated from SRT interpretation could provide satisfactory match in numerical runs as was illustrated by example of a horizontal injector with multiple induced fractures. Comparison of interpretation results for different wells integrating additional field data is a possible way to reduce this uncertainty. Finally, some hints to designing, conducting and interpreting SRTs of different types of wells in fractured carbonate fields are given, using Ekofisk field experience.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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.
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