The use of palaeo-thermo-barometers and coupled thermal, fluid flow and pore-fluid pressure modelling for hydrocarbon and reservoir prediction in fold and thrust belts
Bibliographic record
Abstract
Abstract Basin modelling tools are now more efficient to reconstruct palinspastic structural cross sections and compute the history of temperature, pore-fluid pressure and fluid flow circulations in complex structural settings. In many cases and especially in areas where limited erosion occurred, the use of well logs, bottom hole temperatures (BHT) and palaeo-thermometers such as vitrinite reflectance (Ro) and Rock-Eval (Tmax) data is usually sufficient to calibrate the heat flow and geothermal gradients across a section. However, in the foothills domains erosion is a dominant process, challenging the reconstruction of reservoir rocks palaeo-burial and the corresponding calibration of their past thermal evolution. Often it is not possible to derive a single solution for palaeo-burial and palaeo-thermal gradient estimates in the foothills, if based solely on maturity ranks of the organic matter. Alternative methods are then required to narrow down the error bars in palaeo-burial estimates, and to secure more realistic predictions of hydrocarbon generation. Apatite fission tracks (AFT) can provide access to time–temperature paths and absolute ages for the crossing of the 120 °C isotherm and timing of the unroofing. Hydrocarbon-bearing fluid inclusions, when developing contemporaneously with aqueous inclusions, can provide a direct access to the pore-fluid temperature and pressure of cemented fractures or reservoir at the time of cementation and hydrocarbon trapping, on line with the tectonic evolution. Further attempts are also currently made to use calcite twins for constraining reservoir burial and palaeo-stress conditions during the main deformational episodes. Ultimately, the use of magnetic properties and petrographical measurements can also document the impact of tectonic stresses during the evolution of the layer parallel shortening (LPS). The methodology integrating these complementary constraints will be illustrated using reference case studies from Albania, sub-Andean basins in Colombia and Venezuela, segments of the North American Cordillera in Mexico and in the Canadian Rockies, as well as from the Middle East.
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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".