Optimization of WAG Process for Coupled CO2 EOR-Storage in Tight Oil Formations: An Experimental Design Approach
Bibliographic record
Abstract
Abstract Restricted flow capacity of low permeability oil formations imposes unique challenges to the implementation of CO2-WAG processes in such reservoirs. Application of multi-stage fractured horizontal wells can substantially improve the injection and production rates. However, there are various design parameters and operating conditions which can affect the performance of a WAG flood. The parameters considered in this study are those related to development pattern (well spacing and well completion strategy), hydraulic fracture geometry (half-length and spacing), WAG parameters (WAG ratio and CO2 slug size) and the timing of the switch from primary or water-flood to WAG scheme. In this study, CO2 EOR performance is assessed based on the oil recovery factor and also the amount of stored CO2; in other words, the objective is to achieve both the goals of enhanced oil recovery and sequestration of CO2 in the tight oil formation. However, to reflect the effect of time, the net present value (NPV) of the projects was also considered. All three of these parameters were therefore included in objective functions to be optimized. The effect of all aforementioned parameters on objective functions was investigated using a compositional simulator. Design of experiment (DOE) was then utilized to perform a comprehensive statistical analysis to recognize the most prominent factors in fulfillment of each objective function in a tight reservoir with properties similar to Pembina Cardium field. Response surfaces were generated to quantify the effect of the factors on the objective functions. Optimization was carried out to find those sets of factors which provided the highest recovery, storage, and NPV. Searching for optimal values can be extended to any combination of objective functions which are obtained by applying weighting multipliers to each individual objective function.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".