Diagnostic Fracture Injection Testing Tactics in Unconventional Reservoirs
Bibliographic record
Abstract
Abstract To achieve optimal production from unconventional reservoirs, it is useful to determine the permeability, pore pressure, and state of stress of rock strata. Doing so will lead to properly designed treatments, realistic predictions of well performance, and a basis for normalizing reservoir contribution when evaluating completion and stimulation effectiveness. An effective way to derive the necessary reservoir information is to conduct in-situ pressure transient tests. Since it is difficult to inject fluid into or withdraw fluid from the pore network of tight rock, diagnostic fracture injection tests (DFIT) have been employed to create an analyzable pressure decline response, as well as to derive the minimum horizontal stress via fracture closure identification. This paper is a study of numerous DFITs conducted in unconventional reservoirs throughout the world to evaluate the reservoir and geomechanical characteristics of the pay zone and bounding intervals. Within this body of work, experiments were implemented to study the impact of testing methods on the test response and various types of analysis methods documented in the literature were implemented and compared. The paper summarizes findings and introduces tactics for planning/conducting tests and evaluating results in a variety of unconventional reservoir types. Topics covered in the paper include: Defining test objectives Test planning and strategies ○ Tactics for selecting injection rates and volumes ○ Downhole shut-in techniques for hastening fracture closure and radial flow regime development. ○ Vertical vs. horizontal wells ○ Multiple injection-falloff cycles. ○ Multi-interval tests ○ Multiple non-communicating pressure gauges. ○ Tactics for understanding test height Pre-test fracture modeling for selecting injection volumes and rates and test intervals. Procedure/execution Reservoir and geomechanical considerations ○ Sub-pressured reservoirs ○ Fracture closure ○ Impact of natural fractures ○ Near-wellbore fracture complexity
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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.
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".