Criteria for Ranking Realizations in the Investigation of SAGD Reservoir Performance
Bibliographic record
Abstract
Abstract Geostatistical techniques are being increasingly used to generate reservoir models and quantify uncertainty in reservoir properties. This is achieved through the construction of multiple realizations constrained by all available data. A large number of realizations are required to capture the extreme low and high cases. Not all realizations can be used in simulation. However, randomly selecting realizations will result inaccurate represent of the uncertainty. This paper presents a methodology for ranking the constructed realizations to reduce the number that must be processed in flow simulation. The ranking methodology is customized for a gravity drainage process where the well locations are specified. Although this ranking procedure is not general for a variety of recovery processes, it is particularly suited to steamassisted gravity drainage (SAGD) production. The ranking measure is highly correlated to performance parameters such as cumulative steam oil ratio (CSOR) and cumulative oil production rate (COPrate.). The ranking measure quantifies the local connected hydrocarbon volume within a local window for the SAGD reservoir. The local window represents the distribution of reservoir properties and location of the production well. A large number of flow simulations were undertaken to illustrate that ranking measure works. Results show that ranking with connected hydrocarbon volume (CHV) can be correlated to SAGD performance parameters. High correlation achieved when ranking of CHV performed through simple static and after calibrating with different windows size. Introduction Ranking is a useful geostatistical tool that is being widely used for reservoir analysis where significant variations are present in the reservoir properties. Efficient reservoir performance prediction requires an efficient ranking methodology before flow simulation to provide an accurate estimation of the reservoir properties and quantify the uncertainty associated with those properties. For predicting the recovery of hydrocarbon, understanding the geostatistical models is very important and has been given a great deal of attention. It is well known that uncertainty exists in oil and gas reservoirs. This uncertainty must be quantified for improved reservoir management. SAGD is an in-situ heavy oil recovery process. The process was invented by Roger Butler1 in 1970. Two parallel horizontal wells with vertical spacing of about 5m are drilled in the formation. The upper well is a steam injection well where steam heats the formation to increase the temperature and reduce the viscosity of the oil. The lower well is the production well where the heated oil can be drained and then pumped to the surface. A gravity driving force is introduced by injecting a steam in to the upper well. During the steam injection steam will rise and form a steam chamber, this process depends on the efficient connection of the steam chamber to the surrounding reservoir. Viscosity is an important issue in heavy oil production and lowering it is part of the SAGD process. The success of a SAGD project depends on controlling some parameters such as steam injection rate to minimize SOR and maximize the COPrate. Numerical reservoir models of porosity, permeability, fluid saturation and facies that are required for flow simulation are generated using geostatistical techniques.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".