Assimilation of Time-Lapse Temperature Observations and 4D-Seismic Data With the EnKF in SAGD Petroleum Reservoirs
Bibliographic record
Abstract
Summary This paper presents the applications of the ensemble-Kalmanfilter (EnKF) inverse-modelling technique to petroleum-reservoir characterization of thermally operated oil fields in northern Alberta, Canada. The EnKF is applied to 2D- and 3D-case studies based on the steam-assisted-gravity-drainage heavy-oil-extraction method. The modelling technique integrates effectively both static and dynamic data (petrophysical core data from wellbores, continuous temperature data measured by thermocouples, and 4D-seismic attributes) into a petroleum-reservoir model. Assimilated secondary information provides better insight into geologic properties of a reservoir and improves production forecasting. The method performs well for linear or slightly nonlinear systems that follow a Gaussian distribution, but shows worse performance for nonlinear or non-Gaussian systems. Integration of a large amount of data with a small number of realizations leads to ensemble collapse because of insufficient degrees of freedom. Increasing the ensemble size is a solution, but by increasing forecasting time. To overcome these issues and reduce computational time, matrix localization techniques and a shortcut based on replacement of the model realizations with their mean in forecasting are suggested. Even though the EnKF has been proved to be simple in implementation and effective for modelling of continuous linear systems, special care should be taken for modelling nonlinear systems, including categorical variables (e.g., geologic facies).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 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".