Case Studies in Quantitative Flowback Analysis
Bibliographic record
Abstract
Abstract Recently, several authors have explored new methods for quantitatively analyzing multi-phase flowback data from multi-fractured horizontal wells (MFHW) to extract both fracture and reservoir parameters. These techniques provide much of the same information as long-term rate-transient analysis (RTA), although in a much shorter period of time. Flowback analysis is complicated by a rapidly changing fracture network and wellbore environment, multi-phase flow in the fractures (and possibly the reservoir), completion heterogeneity, as well as other effects which are often not present, or are ignored, when analyzing long-term (online) production data. For quantitative flowback analysis, the current authors have previously presented data-driven, pseudo-analytical methods for estimating key fracture properties (i.e. conductivity and half-length) from high-frequency, short-duration production test data. Models have been developed for both oil and gas wells representing a variety of reservoir and operating conditions. In this work, the models and procedures are extended to apply to more challenging reservoir/completion scenarios and are used in the analysis of several case studies from Canada. Each of the case studies demonstrate either the potential value add of the developed techniques, or a unique extension to the basic analysis methods. The case studies analyzed herein focus on light tight oil plays and consider layered reservoirs, multi-well flowback, and oil fracs in oil reservoirs. Further, the potential capital savings associated with conducting quantitative flowback analysis of early-time production test data is demonstrated. Each case study therefore presents a unique set of challenges that are often encountered in the real world. Numerical simulations are used to validate the sequence of flow-regimes depicted in the models. The methods presented in this paper will serve to partially satisfy the demands of industry to develop new methods for characterizing hydraulic fractures and forecasting production, particularly early in the well life. Through the use of several unique case studies, the wide-spread applicability and versatility of the techniques is demonstrated.
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".