Addressing Microseismic Uncertainty from Geological Aspects to Improve Accuracy of Estimating Stimulated Reservoir Volumes
Bibliographic record
Abstract
Abstract Unconventional resources development has had a great success in North America. But this success has not yet been duplicated in every tight oil or tight gas reservoir. A big challenge is how to correctly demonstrate and evaluate hydraulic fracturing outcomes, which are usually estimated by a stimulated reservoir volume (SRV). Microseismic data is a fundamental element to estimate a SRV. However, most microseismic signals are generated by shear fracturing, while hydraulic fracturing can induce tensile fracturing as well, which cannot be well reflected by microseismic data. A long distance between fracturing places and geophones can also make signals unclear and undetectable. Thus a sole microseismic interpretation has its own limitations. Integrating geological information and microseismic data can provide us with a valuable guide to increase the accuracy of an estimated SRV. In this study, detailed geological data and well logs are applied to evaluate rock mechanical properties. As known, under the same hydraulic fracturing treatment conditions, a more brittle rock interval can form more fractures, and a rock interval with more natural fractures can generate more complex fractures. The near-wellbore zones are first evaluated by a rock mechanical index based on how the rock is prone to form hydraulic fractures. A rock mechanical model of a whole shale layer is further determined by a Pixel-Based reservoir modeling method. A rock mechanical index is used as a criterion to address the microseismic data uncertainty. Comparing a rock mechanical model with a SRV, some mismatched areas are located, the microseismic uncertainty is addressed, and the microseismic signal threshold criteria are then adjusted in these areas. As a result, a SRV in the mismatched areas is further modified based on a rock mechanical index value and a different signal dataset. This approach has been applied in the Biyang shale oil reservoir, China. Compared with the successful Barnett marine shale formation, the target shale layer was deposited in a continental lacustrine environment with complex laminations and high heterogeneous rock mineral compositions. Comparing the SRV generated by original microseismic data with the newly SRV generated by applying this approach, the new one can precisely represent hydraulic outcomes and illustrate complex dual-permeability flow for unconventional reservoir development. The proposed approach is a good and valuable guide to increase the accuracy of estimating SRV.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".