Downhole microseismic monitoring: processing, algorithms and error analysis
Bibliographic record
Abstract
Basic microseismic data processing for hypocentre location estimates is a pre-requisite to extracting information about the stimulated reservoir volume as well as understanding the geomechanics of the fracturing process. The primary objective of this thesis is to investigate the basic processing of microseismic data acquired with receivers in a single observation well, with emphasis on the evaluation and parameter selection of processing algorithms and the associated hypocentre location error analysis. This thesis is made up of five independent studies. The first study discusses the development of a MATLAB based microseismic data processing package (Calgary microseismic processing system; CaMPS). This software is used to process the microseismic data from a hydraulic fracture treatment in western Canada. For reference, the results are compared with those obtained independently by a microseismic services company. The second study examines several single-trace event-detection and arrival-time picking algorithms for microseismic data. A dynamic threshold criterion for event detection and a hybrid, arrival-time picking approach are proposed. The performance of these algorithms is evaluated using synthetic and real microseismic data. The third study describes an iterative cross-correlation based workflow to refine the initial arrival-time picks. This workflow is compared with other single-trace and multi-trace techniques. The proposed workflow provides an arrival-time accuracy of ±0.5 − 1ms for both synthetic and real microseismic data examples considered in this study. The fourth study examines hypocentre location uncertainty and errors due to inaccurate velocity model. The Monte Carlo uncertainty analysis suggests that the velocity errors have a greater impact on hypocentre locations than arrival-time pick errors. The hypocentre location errors resulting from the model calibration process are also discussed, in particular the use of single vs. multiple calibration shots, a priori information, and first vs. direct arrival times. The fifth study discusses the remaining hypocentre location errors after anisotropic model calibration. The behaviour of hypocentre location is discussed when a 1-D layered, isotropic and homogeneous model is used to locate hypocentres from anisotropic and heterogeneous subsurface. The results emphasize the use of a detailed model with anisotropy and lithological or structural variations for improving hypocentre location accuracy.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".