A Method To Perform Multiple Diagnostic Fracture Injection Tests Simultaneously in a Single Wellbore
Bibliographic record
Abstract
Summary This paper presents a unique process for deriving reservoir properties (i.e., minimum horizontal stress, kh/u, and reservoir pressure) in isolated reservoir layers intersected by the same vertical wellbore. It is based on simultaneously performing multiple diagnostic fracture-injection tests (DFIT) with extended multiday shut-in periods using downhole shut-in tools and bottomhole memory gauges. A case study of 58 vertical wells completed in the Mesaverde and Dakota sandstones of the San Juan basin is used to describe and assess the above application. These intervals are gas productive, slightly to significantly subpressured, and possess a very low permeability pore network enhanced by natural fracture networks. As many as seven individual intervals per well were tested using the simultaneous process in an area spanning the entire San Juan basin. Large-scale, multistage hydraulic fracturing is necessary to establish commercial production from these intervals. The diagnostic tests were done before the large-scale fracture treatments, yet did not impede the subsequent implementation of the treatments. In the Dakota interval, the diagnostic testing results agreed well with the kh derived from post-fracture production analysis and led to a process of treatment design optimization. In the Mesaverde interval, less agreement was found between diagnostic-test and production-analysis results. Despite the lack of validation, fluid leakoff rates measured during the diagnostic testing provided insight into fracture half-length differences documented in a previous study of microseismic mapping. As part of the case study, procedural guidelines and best practices developed in the process of performing over 200 tests will be discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".