Bibliographic record
Abstract
Abstract This paper evaluates the potential of applying horizontal well CSS (HW-CSS) technology to enhance heavy oil production from non-CHOPS thin pay resources in the Iron River/Manatokan area. Thin non-CHOPS heavy oil and oil sand deposits are a considerable resource. The economics for thermal operations in these deposits are hampered by the fact that they contain less oil over producing horizontal wells than do thicker deposits. A numerical model was built and simulations were performed to answer the questions: Can HW-CSS be developed to exploit "non-CHOPS" thin heavy oil resources?What is the cut-off pay thickness for an economic application of HW-CSS?How can HW-CSS be used to maximize the net present value (NPV) of oil recovery from thin heavy oil resources? The initial simulation results indicated that despite a determined effort to develop optimized operating strategies, HW-CSS cannot economically recover heavy oil when the reservoir pay thickness is less than 8 m due to excessive heat loss to the overburden/underburden. Later simulations demonstrated that the cut-off pay thickness for an economic HW-CSS process is around 11 m. With an optimized operating strategy and well spacing, and short well life, a cumulative steam-oil ratio (CSOR) under 6 m3 cold water equivalent (CWE)/m3 oil was achieved. The estimated oil recovery ranged from 30 to 50% depending on the well spacing used in the estimates (either 100 m or 75 m). For a 25 year project involving the installation of a new 8 well pad every 8 years, the internal rate of return (IRR) was estimated to be in the range of 20 - 25%. For a 1 m reduction in pay thickness (e.g. from 11 to 10 m), the cumulative oil production/m of pay thickness decreased 6 - 8% when the reservoir pay thickness was less than 11 m, decreased 1 to 2 % when the reservoir pay thickness was between 11 and 17 m, and decreased less than 1% when the reservoir pay thickness was greater than 17 m. The reservoir pay thickness of 14 m displayed the most efficient oil recovery for the reservoir pay thicknesses investigated (5 m to 20 m) although 20 m pay thickness resulted in the highest oil production. Thicker pays had a greater tendency for steam to rise because of an increased gravitational influence resulting in reduced lateral expansion. These pays required smaller well spacing for maximum oil recovery. Better reservoir conditions such as higher permeability and lower oil viscosity generally achieved better performance. However, for reservoir pay thicknesses of 5 m and 8 m, an initial dead oil viscosity of 30,000 mPa.s resulted in lower oil recovery than having an initial dead oil viscosity of 50,000 mPa.s. The reservoir with 30,000 mPa.s viscosity oil displayed faster pressure and temperature dispersion resulting in ineffective use of the dilation and dead oil re-compaction mechanism.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".