Bibliographic record
Abstract
Abstract A multi-well cold production model, developed at the Saskatchewan Research Council, was used to predict the effect of well spacing and in-fill drilling on oil recovery in cold production. Predictions of oil production as a function of well spacing were used to assess the comparative economics of five different well spacings: 10 acre, 20 acre, 40 acre, 60 acre and 80 acre. The 10 acre and 20 acre spacing appeared significantly less economical than the higher well spacings. For thin reservoirs (2m to 3m) where post-cold production does not appear as economical, 40 acre spacing is recommended. Predictions of additional oil recovery with in-fill wells were used to assess the economics of in-fill scheduling as a function of different in-fill scheduling times. In-filling a 40 acre spacing well with 10 acre spacing in-fill wells does not appear to be economical. Introduction The cold production (CHOPS) process, in which sand is deliberately produced, has been developed in Western Canada. With advances in the design of progressive cavity pumps, in sand disposal methods and in operating strategies, cold production has become an economical process1 well suited for thin (2 m to 10 m) heavy oil reservoirs. The roles of solution gas drive and sand production in the CHOPS process were first investigated by Smith2. He hypothesized that micro-bubbles would develop in heavy oil during primary production and maintain the reservoir pressure longer by increasing the compressibility of the oil/micro-bubble mixture. Flow visualization studies using glass micro-models, such as that of Bora et al.3, did not reveal the growth of micro-bubbles however. Ostos and Maini4 investigated the role of the capillary number in the solution gas drive process. The capillary number is a measure of the ratio of the viscous forces to interfacial tension forces. This number is defined as: Equation (Available in full paper) (1) where u is the pore velocity, μ is the viscosity and σ is the surface tension. Bora et al.3 observed that at low capillary numbers, gas bubbles would develop in the micro-model at a few locations on the surface of the pores and would grow into ganglia spanning several pores. These ganglia eventually connected leading to gas breakthrough and significantly reduced oil recovery. At higher capillary numbers, the bubbles would not grow into ganglia but would be broken up (dispersed) into the oil. The bubbles would then connect at higher gas saturations leading to greater oil recovery 3,4. Maini4 coined the term "foamy oil" for this gas in oil dispersion. Firoozabadi 5, based on a series of solution gas drive experiments, attributes the greater oil recovery factor for heavy oil compared to light oil, to the reduced gas flow in the former case. Because of the higher viscosity of the heavy oil, the molecular diffusivity of the dissolved methane is lower6. The gas bubbles will grow more slowly in the heavy oil because of the lower molecular diffusion coefficient and because of the higher viscosity of the heavy oil.
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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 | 0.000 |
| 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".