Seismische Charakterisierung von geothermischen Reservoiren mittels “Common-Reflection-Surface” Stapelungs-Methode und Attribut-Analysen
Bibliographic record
Abstract
An important contribution of geosciences to the renewable energy production portfolio is the exploration and utilization of geothermal resources. For the development of a geothermal project at great depths a detailed geological and geophysical exploration program is required in the first phase. With the help of active seismic methods high-resolution images of the geothermal reservoir can be delivered. This allows potential transport routes for fluids to be identified as well as regions with high potential of heat extraction to be mapped, which indicates favorable conditions for geothermal exploitation. The presented work investigates the extent to which an improved characterization of geothermal reservoirs can be achieved with the new methods of seismic data processing. The summations of traces (stacking) is a crucial step in the processing of seismic reflection data. The common-reflection-surface (CRS) stacking method can be applied as an alternative for the conventional normal moveout (NMO) or the dip moveout (DMO) stack. The advantages of the CRS stack beside an automatic determination of stacking operator parameters include an adequate imaging of arbitrarily curved geological boundaries, and a significant increase in signal-to-noise (S/N) ratio by stacking far more traces than used in a conventional stack. A major innovation I have shown in this work is that the quality of signal attributes that characterize the seismic images can be significantly improved by this modified type of stacking in particular. Imporoved attribute analysis facilitates the interpretation of seismic images and plays a significant role in the characterization of reservoirs. Variations of lithological and petro-physical properties are reflected by fluctuations of specific signal attributes (eg. frequency or amplitude characteristics). Its further interpretation can provide quality assessment of the geothermal reservoir with respect to the capacity of fluids within a hydrological system that can be extracted and utilized. The proposed methodological approach is demonstrated on the basis on two case studies. In the first example, I analyzed a series of 2D seismic profile sections through the Alberta sedimentary basin on the eastern edge of the Canadian Rocky Mountains. In the second application, a 3D seismic volume is characterized in the surroundings of a geothermal borehole, located in the central part of the Polish basin. Both sites were investigated with the modified and improved stacking attribute analyses. The results provide recommendations for the planning of future geothermal plants in both study areas.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".