Field Realization of Adaptive Reservoir Simulation for Steam Injection Forecasting
Bibliographic record
Abstract
Abstract Globally, steam injection for heavy and high-viscous oil recovery is increasing, including carbonate reservoirs. Lack of full understanding such reservoir heating and limited information about production and injection rates of individual wells require to forecast steam injection not only deterministic and simple liquid displacement characteristic modeling types, but also the data-driven one, which covers the adaptive modeling. The implementation and validation of the adaptive system is presented in this paper by one of the world's largest carbonate reservoirs with heavy and high-viscous oil of the Usinsk field. Steam injection forecasting in such reservoirs is complicated by the unstable well interactions and relatively low additional oil production. In the adaptive geological model, vertical dimensions of cells are similar to gross thicknesses of stratigraphic layers. Geological parameters of cells with drilled wells do not necessarily match actual parameters of those wells since the cells include information of neighboring wells. During the adaptive hydrodynamic modeling, a reservoir pressure is reproduced by cumulative production and injection allocation among the 3D grid cells. Steam injection forecasting is firstly based on the liquid displacement characteristics, which are later modified considering well interactions. To estimate actual oil production of steamflooding using the reservoir adaptive geological and hydrodynamic models, dimensionless interaction coefficients of injection and production wells were first calculated. Then, fuzzy logic functions were created to evaluate the base oil production of reacting wells. For most of those wells, actual oil production was 25 – 30 % higher than the base case. Oil production of steamflooding for the next three-year period was carried out by modeling two options of the reservoir further development - with and without steam injection. Generally, forecasted oil production of the option with steam injection was about 5 % higher. The forecasting effectiveness of cyclic steam stimulations of production wells was done using the cross-section method, when the test sample was divided into two groups - the best and the worst, for which the average forecasted oil rates after the stimulations were respectively higher or lower than the average actual oil rate after the stimulations for the entire sample. The difference between the average actual oil rates after the stimulations of the best and the worst groups was 32 %, i.e. this is in how much the actual oil production could have increased if only the best group of the sample had been treated.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".