Production Data Analysis of Overpressured Liquid-Rich Shale Reservoirs: Effect of Degree of Undersaturation
Bibliographic record
Abstract
Abstract Liquid-rich shale plays (LRS) in North America have recently gained a lot of attention. Commercialization of these plays is now possible due to new technology, such as multi-fractured horizontal wells (MFHW). Along with such developments, there is an increased requirement to develop consistent reservoir-engineering methods to analyze multi-phase production data in such reservoirs. Large drawdowns required to produce these very low permeability formations complicate production analysis due to condensate drop-out near the wellbore or fracture face. Hydraulically-fractured vertical and horizontal wells completed in tight formations typically exhibit long periods of transient linear flow. This paper discusses a novel production data analysis technique for constant flowing bottomhole pressure (pwf < pdew) wells producing from fractured LRS reservoirs. Our focus in this work is on cases where the initial reservoir pressure is well above the dew point pressure, as occurs in highly-undersaturated portions of the Eagle Ford Formation. A theoretical basis is developed for analysis of the transient linear flow period for these cases, and the effect of initial pressure on well performance is studied. The governing flow equation is linearized using appropriately defined two- phase pseudopressure and pseudotime functions, where the liquid solution analogy can be applied. This approach provides an accurate estimation of the linear flow parameter (xf√k) in multi-phase flow situations. Fine-grid compositional and black oil numerical models are used to validate the results. This work provides a robust analytical framework for production analysis of liquid-rich shale reservoirs as well as practical guidelines for real world applications.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".