New technologies for unconventional reservoir characterization: Seismic inversion, focal-time estimation, and signal processing to improve reservoir imaging
Bibliographic record
Abstract
Seismic data, comprising both passively and actively recorded data, have long been used for resource evaluation and geohazard assessments. Unconventional resource extraction, such as Alberta’s Duvernay play, requires a multifaceted approach to optimize reservoir development and to mitigate geohazards such as induced seismicity. Frequently, hydraulic fracture stimulation programs do not go as planned; fractures occur out of zone, depart from the predicted models, and, in some cases, induce felt seismic events (induced by hydraulic fracturing operations). From the Fox Creek, Alberta study area are well log data, multicomponent seismic reflection data, and microseismic data recorded from a permanent near-surface passive recording array. For this study, an industry partner provided two multicomponent seismic reflection surveys, as well as two co-located passive microseismic surveys. The Microseismic Industry Consortium (MIC) supplied microseismic data from the Tony Creek dual microseismic experiment (ToC2ME); an anonymous industry contributor contributed a second passive survey. Technologies developed in this thesis enable more accurate positioning of microseismic hypocenters by incorporating seismic reflection data. Signal-processing techniques used in seismic reflection processing are employed in this thesis to enhance the detection quality and quality of induced seismic events. Structural interpretation provides a framework of vital information to map and understand the relationship between geological structure and induced seismic events. Constraints obtained from full-waveform inversion provide detailed information about the properties of the Duvernay Formation itself, such as brittle and ductile facies. Accurate microseismic hypocenter determination in the context of seismic analysis identifies which structural elements and reservoir facies control the direction and size of induced fractures and which faults may be responsible for induced seismicity. Hypocenters are accurately located and plotted in depth and are associated with faults mapped from the reflected seismic. This analysis highlights what geological conditions, faults, lithology, and structure are dominant factors with respect to hydraulic fracture propagation and induced seismicity. The results of this research will aid in the design of hydraulic fracture completion programs and geohazard (induced seismic event) mitigation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".