Geomechanical Characterization of an Unconventional Reservoir with Microseismic Fracture Monitoring Data and Unconventional Fracture Modeling
Bibliographic record
Abstract
Abstract Hydraulic fracturing is often the most effective option to stimulate production in unconventional reservoirs to economic levels. Results of stimulation can be mixed unless the hydraulic fracture design correctly interprets the geological and geomechanical setting of the field. In fields with naturally fractured reservoirs, the interpretation is particularly critical because natural fractures strongly influence the final stimulated rock volume. An accurate description of the natural fracture network and the geomechanical properties and stresses of the rock provide the information to optimize stimulation treatment in naturally fractured unconventional reservoirs. However, the uncertainty in some of this information can jeopardize the value of the modeling and the success of the stimulation. One of the key geomechanical parameters, which are often poorly constrained, is the maximum horizontal stress magnitude. Microseismic data are able to map the stimulated rock volume during hydraulic fracturing operations. These data can be used to verify the accuracy of the fracturing treatment modeling. Here, we present a case study characterizing geomechanical parameters of an unconventional reservoir using a novel technique that includes calibrated mechanical earth models. The technique reduces uncertainty in the geological and geomechanical parameters used to design hydraulic fracture operations, improving the prediction of the final stimulated rock volume.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".