Factors Controlling Fluid Migration and Distribution in the Eagle Ford Shale
Bibliographic record
Abstract
Abstract Production of shale and tight oil is the cornerstone of the United States race for energy independence. According to the U.S. Energy Information Administration (EIA) nearly 90% of the oil production growth comes from six tight oil plays. The Eagle Ford is one of these plays and accounts for 33% of the oil production growth with a contribution of 1.3 million barrels per day. A geological challenge in the Eagle Ford shale is the unconventional fluids distribution: shallower in the structure there is black oil, deeper and to the south condensate appears, and at the bottom dry gas can be found. Differences in burial depth, temperature, and vitrinite reflectance are used to explain this unique distribution. A similar fluid distribution occurs in other reservoirs (e.g. Duvernay shale in Canada). The above observations led to the key objective of this paper: to identify the main factors that control fluid migration (due to buoyancy of gas in oil) from one zone to another. This was done by constructing a conceptual cross sectional simulation model with NW to SE orientation that allowed the study of fluid migration and distribution throughout one million years while maintaining computational time within reasonable limits. The input data used for the model were gathered from published work in the geoscience and petroleum engineering literature. Results show that although there is some gas migration through fractures to the top of the structure, fluids in the matrix remained with approximately the same original distribution. This fluid migration through fractures could be responsible for higher initial gas production in some oil wells in the top of the structure. Results show that ultralow permeability, low porosity, and low natural fracturing are the main restrictions for fluid migration in the Eagle Ford shale.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 teacher head, 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".