Ground Penetrating Radar Imaging of Ancient Clastic Deposits: A Tool for Three-Dimensional Outcrop Studies
Bibliographic record
Abstract
The growing need for better definition of flow units and depositional heterogeneities in petroleum reservoirs and aquifers has stimulated a renewed interest in outcrop studies as reservoir analogues in the last two decades. Despite this surge in interest, outcrop studies remain largely two-dimensional; a major limitation to direct application of outcrop knowledge to the three dimensional heterogeneous world of subsurface reservoirs. Behind-outcrop Ground Penetrating Radar (GPR) imaging provides high-resolution geophysical data, which when combined with two dimensional architectural outcrop observation, becomes a powerful interpretation tool. Due to the high resolution, non-destructive and non-invasive nature of the GPR signal, as well as its reflection-amplitude sensitivity to shaly lithologies, three-dimensional outcrop studies combining two dimensional architectural element data and behind-outcrop GPR imaging hold significant promise with the potential to revolutionize outcrop studies the way seismic imaging changed basin analysis. Earlier attempts at GPR imaging on ancient clastic deposits were fraught with difficulties resulting from inappropriate field techniques and subsequent poorly-informed data processing steps. This project documents advances in GPR field methodology, recommends appropriate data collection and processing procedures and validates the value of integrating outcrop-based architectural-element mapping with GPR imaging to obtain three dimensional architectural data from outcrops. Case studies from a variety of clastic deposits: Whirlpool Formation (Niagara Escarpment), Navajo Sandstone (Moab, Utah), Dunvegan Formation (Pink Mountain, British Columbia), Chinle Formation (Southern Utah) and St. Mary River Formation (Alberta) demonstrate the usefulness of this approach for better interpretation of outcrop scale ancient depositional processes and ultimately as a tool for refining existing facies models, as well as a predictive tool for subsurface reservoir modelling. While this approach is quite promising for detailed three-dimensional outcrop studies, it is not an all-purpose panacea; thick overburden, poor antenna-ground coupling in rough terrains typical of outcrops, low penetration and rapid signal attenuation in mudstone and diagenetic clay- rich deposits often limit the prospects of this novel technique.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".