Determination of ISIP of Non-Ideal Behavior During Diagnostic Fracture Injection Tests
Bibliographic record
Abstract
Abstract The instantaneous shut-in pressure (ISIP) serves as an indication of the excess pressure in the hydraulic fracture due to the effect of fluid viscosity and pressure required to break the formation at the fracture tip. The ISIP value will be close to or at the fracture propagation pressure and will be greater than the fracture pressure. The ISIP is often estimated to be the pressure after the pumps are shut down, and the beginning of a pressure decline. Many approaches have been developed to estimate the ISIP from the falloff data. The development of these approaches is attributed to the persistent trials due to the difficulty of quantifying the ISIP value accurately. Giving bottomhole pressures, ISIP can be estimated by subtracting the friction pressure drop from bottomhole pressure. This approach tends to overestimate the value of ISIP as it doesn't account for friction near the wellbore or through the perforations. Another common approach to estimate ISIP is by drawing a straight line on the early falloff portion of the Diagnostic Fracture Injection Tests (DFIT). Previous studies show that the choice of ISIP affects the net pressure calculations, but not the slope of the derivative curves and the flow regime identification. This paper presents field cases where the values of ISIP affects the interpretation of the reservoir characteristics. Thus, the determination of accurate ISIP is very crucial. This paper reviews the previously proposed approaches for determining the ISIP and provide a state of the art simple method to determine ISIP from non-ideal falloff data. The ISIP determined from the proposed method is verified by examination of the semi-log derivative plot, and the interpreted reservoir characteristics were found to be consistent with both field and lab observations. The method was validated using field DFITs falloff data from high-pressure dependent leakoff formations as well as formations that yield normal leakoff pressure dependent. The novelty of the proposed method is in the simplicity of determination of ISIP and the consistency with the field observations. A number of field examples from the Barnett shale are illustrated using mechanisms previously proposed in the literature as well as the method presented in this paper. The later provided consistent ISIP values after multiple iterations. Subsequently, the reservoir characteristics and calculated parameters were uniform within the same pad of wells.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".