Optimization of Horizontal Wellbore and Fracture Spacing Using an Interactive Combination of Reservoir and Fracturing Simulation
Bibliographic record
Abstract
Abstract The application of horizontal wellbore drilling and multistage fracturing technology has been playing a pivotal role in unlocking shale-gas reserves globally. More recently, the same technology has been applied to both new and mature oil fields. A key question for economic optimization of these assets is what fracture spacing to use along a horizontal wellbore. Of equal importance is what spacing to use for multilaterals and the wellbores themselves to achieve optimal drainage of the reservoir. In addition, the design of the fracturing treatments must be optimized. To address these questions, a combination of reservoir and fracturing simulation has been applied. The required input data are provided through a combination of advanced log and core analyses, diagnostic fracture injection testing (DFIT), rate transient analysis (RTA), and characterization of fracture geometry through microseismic monitoring. Fluid rheology is characterized using pressurized rheometers and flow loops. This paper presents results of this work using examples of current Canadian oil and shale-gas reservoirs and a methodology to improve the economic return of different completion and production scenarios.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".