Integrated Reservoir Modeling and Optimization Study of Multi-Fractured Horizontal Well in the Swan Hills Formation: A Case Study of Acid Fracturing
Bibliographic record
Abstract
Abstract With the advancement of drilling and fracturing technology in recent years, multi-stage fractured horizontal wells have become a norm in the development of unconventional oil and gas reservoirs in North America. This has created a renewed interest in major formations of the Western Canadian Sedimentary Basin. Due to higher completion cost, it is crucial to find an optimal fracture size and spacing as well as wellbore spacing for these horizontal wells. Acid Fracturing is a stimulation process in which acid is used to enhance the conductivity of a hydraulic fracture through differential etching of the fracture face. The effectiveness of acid fracturing depends on retaining fracture conductivity under closure stress after treatment. Fracture face roughness created by the acid etching and mechanical properties of rock after acid treatment are two important factors that play a vital role in retaining conductivity after fracture closure. Acid fracturing treatments of carbonate reservoirs have yielded an increase in production in many areas of the world. The Swan Hills Formation (a member of Beaverhill Lake group) in Alberta, Canada, a carbonate oil play, is the focus of this paper. A dual porosity model was employed to integrate core, rock mechanics, PVT, stimulation, and production data. Acid treatment data was used to estimate the fracture geometry and conductivity which was then incorporated into a three dimensional reservoir simulation model. A multi-layer, single-wellbore reservoir model of horizontal heterogeneities across the study area was built based on core calibrated formation and geomechanical log data. The model input parameters were further fine-tuned using production history matching. The calibrated reservoir parameters, based on a history matched model, were used for initial fluid in-place estimation, production forecasting and to investigate the fracture and wellbore interference. Interference analysis was performed based on reservoir pressure depletion and decline in cumulative production. A sensitivity study of fracture density, wellbore spacing, and lateral length of wellbore was carried out and their effects on oil and gas production are discussed. Recommendations on optimal fracture and wellbore spacing for the Swan Hills Formation are also provided.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".