Interpretation of Mini-Frac and Flowback Pressure Response: Application to Unconventional Reservoirs in the UAE
Bibliographic record
Abstract
Abstract The main objective of the research presented in this paper was to develop a working knowledge of the unconventional shale in the UAE Diyab formation which includes reservoir engineering evaluation of the UAE Diyab Upper Jurassic gas condensate and Shilaif Middle Cretaceous light oil shale development. To achieve this objective, (1) we measured core permeability of a couple of Diyab cores with and without fractures, (2) we analyzed the pressure fall-off data from a Diagnostic Fracture Injection Test (DFIT) to determine in-situ matrix permeability for use in reservoir evaluation, modeling, and forecasting reservoir performance, and (3) we determined the effective permeability (that is, combined permeability of matrix and microfractures) of a Diyab stimulated well using rate transient analysis (RTA). Furthermore, we put together both analytical and numerical models for single-phase and two-phase flows in support of the interpretation of the field pressure falloff DFIT data, and the data from a laboratory DFIT conducted in a granite core by Frash in 2014 to shed light on enhanced geothermal reservoirs. Finally, we calculated the depths of filtrate invasion and the cooled region surrounding the hydraulic fracture surfaces to determine the net stress change near the surface of hydraulic fractures, which is commonly referred to as the ‘stress shadow' effect. We concluded that our research effort was both informative and instructive in determining the effectiveness of the stimulation efforts for the wells used in this study, and the process can be similarly utilized in any shale stimulation effort elsewhere.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".