Eagle Ford and Pimienta Shales in Mexico: A Case Study
Bibliographic record
Abstract
Abstract The objective of this paper is to highlight the potential of the Eagle Ford (Cretaceous) and Pimienta (Upper Jurassic) shales in Burgos basin (Mexico) through a comparison with the Eagle Ford shale in Texas. The comparison is a case study focused on real data and their interpretation, north and south of the border, including geochemistry, geology, production, and reservoir engineering data. The overall approach includes the description of Eagle Ford data in Texas, as well as Eagle Ford and Pimienta data in Burgos basin. The geologic comparison is carried out with the use of cross sections of the various formations and geophysical data. Geochemical and petrophysical data are compared with the use of specialized crossplots. Production data are compared through rate transient analysis and by investigating the different flow periods observed in wells in both sides of the border. Reservoir engineering aspects are compared with the use of material balance methods developed specifically for the case of multiporosity shale petroleum reservoirs. Results indicate that there are many similarities but also some discrepancies between the Eagle Ford shale in Texas and shales in Mexico. The geologic and seismic cross sections show that there is continuity of the Eagle Ford in both sides of the border. However, structural geology in Mexico tends to be more complex than in Texas. The geologic and geochemistry descriptions also show important similarities in the rock mineralogy, and the quantity, quality and maturity of the organic matter. Well log data show the same pattern distribution on modified Pickett plots developed originally for evaluation of the Eagle ford shale in Texas. Shales production data in the Burgos basin are characterized by very long periods (several months or even years) of transient linear flow, something that compares well with the Eagle Ford in Texas. Specialized material balance calculations, which consider multiple porosities, have been used in the Eagle Ford shale in Texas and are shown to have similar application in the Burgos Eagle Ford and Pimienta shales. Based on the Eagle Ford shale performance in Texas, and the similarities with Burgos shales, the conclusion is reached that there is significant potential in the Mexican Eagle Ford and Pimienta shales. The novelty of the paper is that it presents a comparison of the interpretation of real geoscience and engineering shale data collected in both sides of the border. The comparison is meaningful and suggests that the potential of shale reservoirs south of the border will be quite significant. Playing its cards right, Mexico should benefit from the good, the bad and the ugly learned in the Texas Eagle Ford.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".