Modern Concepts in Perforation Inflow Diagnostic (PID) Testing: A Safe, Green, and Cost-Effective Technique for Evaluating Pre-Frac Reservoir Parameters
Bibliographic record
Abstract
Abstract Conventional completions and testing methods of low permeability gas reservoirs involve the cost and logistics of balanced and underbalanced perforating, next day stimulation treatment, surface production equipment, and the need for flaring during clean-up operations. In Canada, due to government regulations, operators will conduct the buildup test immediately after one or two day of clean-up operation. This practice has resulted in post-frac welltest analysis being masked by fracture fluid still present in the proppant pack and formation, resulting in misleading estimates of reservoir and fracture parameters important for production forecasting and completion evaluation. Perforation Inflow Diagnostic, referred as PID testing, is a modern testing technique designed to deliver in a cost-effective manner valuable reservoir information such as: reservoir pressure, formation flow capacity, unstimulated gas inflow rate potential and near wellbore damage conditions prior to the fracture treatment. The advantages of PID testing are numerous: capability of accurate measurement of very low gas rates in low permeability (tight) gas wells (often reported as too small to measure), provides a safe testing environment, ensures secrecy and it defines itself as a green well testing procedure since it does not require flaring or venting of natural gas. PID testing is simply the surface and/or subsurface monitoring of the pressure response following extreme underbalanced perforating conditions, using electronic pressure recorders capable of high sampling rate. Unlike conventional testing procedures, the surface valve is closed during the entire test period and the formation fluids are produced into the closed chamber (casing and/or tubing volume). The measured pressures are converted to corresponding gas rates, based on the well-established closed chamber theory. PID testing therefore allows the collection of pressure and rate data required to derive the in-situ matrix permeability, wellbore skin and reservoir pressure.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".