Enhancing Recovery in Shales Through Stimulation of Pre-Existing Fracture Networks
Bibliographic record
Abstract
Abstract Microseismic monitoring has become an attractive option for tracking hydraulic fracture stimulations because, unlike most other monitoring techniques, it can illuminate the behavior of fractures away from treatment wells. However, in most cases, the potential for microseismics in terms of developing an overall picture of fracture interactions within the reservoir is not fully exploited. Based on the analysis of microseismicity associated with stimulations in naturally fractured shale reservoirs, we illustrate how, using advanced seismic signal analysis techniques, namely seismic moment tensor inversion (SMTI) approaches, we can stimulate a pre-existing fracture network. As well, we can identify: 1) the failure type, such as shear or tensile failure associated with rough fracture surfaces, 2) the fracture connectivity related to the number of intersecting fractures in a volume, 3) the fracture intensity based on the developed fracture lengths per volume, 4) the fluid flow pathways and enhanced fluid flow volume as related to the relative degree of open fractures, and 5) the distribution of fracture lengths (power law distribution). Based on our analysis, we identify that most failures observed are mixed-mode failures, typically shear-tensile with either crack opening or crack closure components of failure. The fractures themselves are generally related to the failure of pre-existing natural fractures rather than in the creation of new fractures. Based on the finite sampling (bandwidth limitations), fracture sizes are limited to joint lengths and follow a power law distribution. By examining the spatial and temporal behavior of opening dominated failures, maps of over-lapping zones of potential enhanced fluid flow were identified. In many ways, stress induced fractures during single stages prepared the reservoir for subsequent stages that overall enhanced the interconnectivity and complexity of fractures thereby enhancing fluid flow opportunities. We further discuss, as outlined in these case studies, how, using SMTI, the microseismic data show that the stimulation program as designed achieved its objectives. Overall, we further suggest that these defined seismic parameters can then be used to refine, validate and constrain geomechanical models used as input to reservoir models and further optimize well and stage spacing to effectively drain a reservoir and provide better defined reserve estimates.
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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 | 0.000 |
| 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 | 0.001 |
| 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".