Shale Gas Modeling Workflow: From Microseismic to Simulation -- A Horn River Case Study
Bibliographic record
Abstract
Abstract Recent success of commercial shale gas developments in a number of basins throughout North America can be attributed to the application of advanced technologies used to drill horizontal wellbores, stimulate the shale reservoir, and optimize productivity of shale-bearing formations. Many of the drilling and completion techniques learned from the thousands of wells drilled and stimulated in more mature shale basins, such as the Barnett, have been applied to newer shale discoveries such as the Marcellus and Haynesville in the United States, and both the Montney and the remarkable Horn River Basin in Canada. The unique properties of each shale, however, preclude a "cookie-cutter" development approach from being applied. Each play must be optimized as a unique reservoir. In order to utilize numerical simulation as a tool in optimizing well design, one needs to develop a model that appropriately represents the complex process of gas flow from the native reservoir to the hydraulic fractures and subsequently to the wellbore. This is challenging due to poor understanding of variables such as pressure dependent permeability variation, fluid cleanup, relative permeability effects, non-Darcy flow, methane desorption in a nano-Darcy shale matrix, and fracture conductivity variations from the dominant hydraulic fractures to the secondary induced and natural fractures. Another challenge is accurate representation of the hydraulic fracture. Is the fracture planar or complex? What is the fracture geometry? What is the fracture intensity within the stimulated volume? What is effectively propped? What is the proppant distribution within the fracture system? How does this tie to effective conductivity and does it vary with distance from the wellbore (three dimensionally)? Finding a unique match to historical production is very challenging. Shale gas operators collect a large amount of data including cores and logs (specialized for nano-Darcy rock), micro seismic, diagnostic fracture injection testing (DFIT), fluid and proppant tracers and more. This data is used to better characterize the reservoir and the natural and hydraulic fractures and can help to constrain model inputs. This paper discusses a workflow used in developing a numerical shale gas model for Nexen’s Horn River shale gas reservoir. Presented is a practical and systematic approach to using surveillance data; specifically microseismic data in construction of the stimulated reservoir volume (SRV) and the network of hydraulic fractures in the model. Discussions will also focus on accurately modeling complexities such as non-Darcy flow in the hydraulic fractures, pressure-permeability dependencies, variations in hydraulic fracture conductivity and fluid cleanup. The objective is to gain understanding and insight into the uncertainties that have the greatest impact on well performance.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".