Scaling A Water Coning Control Installation in Bottom Water Drive Reservoir by Inspectional Analysis
Bibliographic record
Abstract
Abstract This paper presents an integrated workflow dedicated to the interpretation of 4D seismic data to monitor the steam chamber growth during the Steam-Assisted Gravity Drainage recovery process (SAGD). Superimposed to the reservoir heterogeneities of geological origin, many factors interact during thermal production of heavy oil and bitumen reservoirs, which complicates the interpretation of 4D seismic data: changes in oil viscosity, fluid saturations, pore pressure, etc. The workflow is based on the generation of a geological model inspired from a real field case of the McMurray Fm in the Athabasca region (Canada). The approach consists of three steps: 1/ the construction of an initial static model, 2/ the simulation of the thermal production of heavy oil with two coupled fluid-flow and geomechanical models, 3/ the production of synthetic seismic maps at different steps of steam injection. The distribution of geological facies is simulated on a very fine grid using a geostatistical approach which honours all available well data. The reservoir, geomechanical and elastic properties are characterized from logs and literature at an initial stage before the start of production. Production scenarios are run to obtain pore pressure, temperature, steam and oil saturations on a detailed reservoir grid around a well pair at several steps of production. Direct coupling with a geomechanical model leads to volumetric strain and mean effective stress maps as additional properties. These physical parameters are used to compute new seismic velocities and density for each step of production according to Hertz and Gassmann formulas. Reflectivity is then computed, and a new synthetic seismic image of the reservoir is generated for each step of production. The impacts of heterogeneities, production conditions and reservoir properties are evaluated for several simulation scenarios from the beginning of steam injection to three years of production. Results show that short-term seismic monitoring can help in anticipating early changes in steam injection strategy. In return, long-term periods allow monitoring the behaviour of the steam chamber laterally and in the upper part of the reservoir. This study demonstrates the added value of 4D seismic data in the context of steam-assisted heavy oil production. Introduction The performance of heavy-oil production by Steam-Assisted Gravity Drainage process (SAGD) is affected by reservoir heterogeneities. However, as many factors interact during thermal production such as changes in oil viscosity, fluid saturations, pore pressure and stresses, the interpretation of 4D seismic data in terms of steam chamber geometry is not direct. Pressure and temperature variations during SAGD operations induce stress changes in the reservoir and in the surrounding media. These modifications of the stress state may imply deformations which can in turn have an impact on reservoir production. These changes also have an influence on the wave propagation into rocks and fluids and may consequently produce differences on seismic velocities and on the travel time. The objectives of this work are 1/ to evaluate the impact of reservoir heterogeneities on the steam chamber growth and 2/ to improve the interpretation of 4D seismic data in steam-assisted.
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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 | 0.001 | 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) | 0.001 | 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".