Bibliographic record
Abstract
Abstract The economics of a SAGD project is related to several production performance parameters. The most significant of those are steam-oil ratio (SOR), calendar day oil rate (CDOR), ultimate recovery (recovery factor, RF), and project life. In consequence, these performance parameters are related to reservoir parameters such as reservoir thickness, permeability, oil saturation, porosity, oil viscosity, and other reservoir heterogeneities such as bottom water, top gas, and interbedded shale barriers. It would be useful to find and quantify the correlations between the SAGD performance and reservoir parameters. In this study, a reservoir screening parameter called SPIDER (SAGD Performance Indicator) is developed to evaluate a SAGD project. This new indicator is calculated from four key reservoir parameters such as reservoir thickness (H), permeability (Kv), oil saturation (So), and porosity (Ø) to predict the SAGD performance as well as to evaluate the economics of a SAGD project. A bitumen price and an SOR economic limit are considered to incorporate into a dynamic model. The results show there exists a relationship between SAGD performance parameters (CSOR, CDOR, and RF) and four reservoir parameters with a high correlation coefficient. Especially, there is a linear relationship between SPIDER and NPV with a correlation coefficient of higher than 0.97. This new indicator may be used as a reservoir screening parameter for a SAGD project. Introduction An economic indicator called STEP (Equation 1) was introduced to evaluate the economics of a SAGD project using the production performance parameters such as CSOR, CDOR, and RF in the series of studies (1, 2, 3). Especially, a dynamic model for STEP called STEP-D (Equation 2) was developed to be used as an economic indicator under the changing conditions of the economic limit of SOR and heavy oil price (3). Equation (1) (Available in full paper) Equation (2) (Available in full paper) STEP may be used as an economic criterion on a SAGD project when the SAGD performance parameters are provided from numerical simulation results or field performance data. As reservoir parameters such as reservoir thickness, permeability oil saturation, porosity, and other reservoir heterogeneities impact the SAGD performance, it would be valuable to quantify the impact of these reservoir parameters. There are a few studies regarding the geological parameters impact on thermal process performance. Thimm et al.(4) analyzed the well performance indices with geology for the steam soak phase of the Peace River Expansion Project. Ito and Suzuki (1998) investigated the effect of reservoir parameters on oil production rates in SAGD projects. They found that oil rates are proportional to pay thickness, permeability, movable oil saturation, and porosity. Llaguno et al.(5) proposed a new methodology to determine the potential SAGD application for Venezuela reservoirs. They used an analytical tool to evaluate the impact of the reservoir properties (API gravity, reservoir pressure, thickness, permeability, porosity, oil saturation, oil viscosity) on the SAGD performance. They found that oil rates were mainly affected by pay thickness, porosity, and oil saturation, and SOR was affected by all the properties.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".