Huff-n-Puff (HNP) Pilot Design in Shale Reservoirs Using Dual-Porosity, Dual-Permeability Compositional Simulations
Bibliographic record
Abstract
Summary Before implementing an HNP pilot in the field, reservoir studies are usually conducted, and compositional numerical simulations performed, to assess the impact of uncertainty on HNP design parameters. In the previous work conducted by the authors, the impact of parametric uncertainty on designing a single-well HNP was demonstrated using single-porosity models. However, recent studies show that a limited region of shattered rock is likely to be created during the hydraulic fracturing process. This region is closely represented by regional dual-porosity dual-permeability (DP-DK) models. In this study, we expand on the early work and address the impact of model uncertainty on designing an optimal HNP for a Duvernay shale example. In addition, a multi-well HNP design is exemplified to assess the impact of fracture communication during the cyclic gas injection scenarios. A unified framework is required to conduct Bayesian history matching and perform HNP optimizations using the Markov chain Monte Carlo process. This task is achieved by implementing new adaptive sampling designs and employing some surrogate modelling techniques (random forests and Gaussian processes) to obtain the distributions for probabilistic HNP forecasts. The results show that for an equivalent calibrated DP-DK model, the efficiency of HNP, for both lean and rich gas injection scenarios, can be substantially higher than that predicted with the caliberated single-porosity model. In particular, lean gas injection, predicted to have a low efficiency using single porosity models, is predicted to result in substantial incremental recovery in DP-DK models. The history matching and optimization results show that DK-DP models yield the highest recoveries during early cycles and a reduced efficiency for later cycles, whereas with single porosity models, the efficiency is fairly constant across cycles. The high efficiency of the DK-DP models is related to an enhanced swelling and mixing process due to pervasive communication (contact area) between the fracture network and the matrix. Moreover, the compositional simulations demonstrate that for multi-well HNP scenarios, communication through hydraulic fractures is far more important than the communication through the enhanced fracture region (EFR). This communication is shown to substantially reduce HNP performance, which is inferred by comparing the probabilistic forecast simulations. This study provides a novel workflow to accurately assess the impact of model uncertainty on the HNP designs for unconventional shale and tight light oil reservoirs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".