Fracture-Optimized Production Efficiently Stimulates Sandstone Formations With Dunvegan Formation: Case Study
Bibliographic record
Abstract
In the Western Canadian Sedimentary Basin (WCSB), fracturing stimulations have been done for 70 years. In Canada, the government, service companies, and well operators accumulate massive volumes of information. This information exists in the form of paper files and databases of varying detail containing well and treatment information. The information age makes production data available making another leap in determining fracture designs based on real production results of past treatments.In the past few decades, a common way operators started the design process was to approach service companies for their experience in an area and formation. From around 1955, many new designs were created by referring to old designs. Then the industry used designs that were pumped in the field which progressed in later years to stimulation that were pumped to completion. This sometimes progressed to job designs pumped with production from the stimulated formation. More value is attained by looking at optimal post fracture production using actual results not calculated, uncalibrated predictions. This is most powerful when the production results are matched to calibrated fracture models, reservoir models and petrophysical analysis to continue area projects in stimulated a formation.This case study covers 3,600 square miles from 58-19W5 to 68-02W6 of the Dunvegan formation. It is a tight, water sensitive formation usually producing gas. Geologically, the Dunvegan has less than 1 mD permeability. The success of various fracturing techniques are evaluated on several levels including a review of the 203 out of the 406 wells in the region. The fracturing strategy comparisons will evaluate base fluid selection, proppant type, and the amount of proppant. The current commodity prices are used to calculate the optimized results versus the vast amount of information that was collected in the past using lower prices.Hydrocarbon based fracturing fluids were used 85% of the time as one would expect based on the geological evaluation of the area. However, the infrequent and larger water fractures have an IP rate of 1.5 MMscf/day using 100,000 lb of proppant where the more frequent and smaller hydrocarbon fractures are 1.2 MMscf/day using 40,0 lb. Total recovered gas is 4 times higher for the water fractures compared to the hydrocarbon based fractures. The optimization of the hydrocarbon fractures with respect to the pounds of proppant used will also be examined since the most common size was 33,000 lb but was found to have optimal IP gas production using 66,000 lb.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".