Matching of Pilot Huff-and-Puff Gas Injection Project in the Eagle Ford Shale Using a 3D 3-Phase Multiporosity Numerical Simulation Model
Bibliographic record
Abstract
Abstract Production of oil from pilot shale wells has generally increased by implementing huff-and-puff (H&P) gas injection. The objective of this paper is using a new 3D, 3-Phase, physics-based, multiporosity model for matching and understanding primary oil production as well as recovery by H&P gas injection from a pilot well in the Eagle Ford shale. History matching and performance forecast are carried out with a newly-developed fully-implicit 3D multi-phase modified black-oil finite difference numerical model, which uses a multiple porosity approach. "The model is capable of handling five storage mechanisms, including (1) organic porosity, (2) inorganic porosity, (3) natural fracture porosity, (4) adsorbed porosity, and (5) hydraulic fracture porosity" (Lopez Jimenez and Aguilera, 2019). Furthermore, the model has capabilities to handle dissolved gas in the solid part of the organic matter, adsorption/desorption from the organic walls, and stress-dependent properties of natural and hydraulic fractures. These storage and fluid flow mechanisms, as well as the stress-dependency of hydraulic fractures, are widely recognized in the case of some shale petroleum reservoirs. Their inclusion in our simulation model permits evaluating the effect of these mechanisms during H&P gas injection. Results of the simulation, presented as cross-plots of production rates and cumulative production vs. time, indicate that oil recovery from shale petroleum reservoirs can be increased significantly by H&P gas injection. The possibility of desorption and gas diffusion is investigated. The approach implemented in this H&P history match of an Eagle Ford pilot well should prove of value for simulating complex shale 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".