Port-Opening Falloff Test: A Complementary Test to Diagnostic Fracture Injection Test
Bibliographic record
Abstract
Summary Conventional fracture injection/falloff tests, such as minifrac or diagnostic fracture injection test (DFIT), are commonly used to characterize shale and tight reservoirs. For ultralow-permeability reservoirs, a reliable DFIT requires a long falloff period after a short injection period. A long falloff observation period of weeks or months is often not economically viable. In addition, the recent economic downturn requires operators to seek cost-effective alternatives to further optimize expenditures. An alternative to a DFIT is a port-opening falloff test (POFOT). Many horizontal completions use a pressure-activated sleeve in the toe of the well to provide formation access after the casing integrity test. Most sleeves open at a pressure in excess of the formation breakdown pressure, after which the wellbore pressure declines toward reservoir pressure. This study first introduces the concept of the POFOT as a novel physical test. A new test method must demonstrate that it accesses the formation of interest and that the data obtained are applicable. A workflow is developed to demonstrate this and is applied to falloff data from POFOTs conducted in five horizontal wells completed in five formations. The results show that the fluid leaving the port is highly likely to break down both the cement sheath and the matrix and create a fracture which then closes. Observation of the well pressure after port opening resembles that from a DFIT. However, without a fixed-duration and constant-rate injection period, there is no accepted method to apply. Nevertheless, both qualitative and quantitative analyses of the falloff data provide a good estimation of reservoir pressure with a reasonable approximation of fracture closure when compared with the estimates from DFIT analysis from offset wells. The key challenges in parameter estimation, besides the development of an appropriate analysis method, are short falloff data and noisy early-time data due to wellbore resonance (WBR), low-resolution gauges, and change in sampling frequency.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".