Modeling Thermal and Solvent Injection Techniques as Post-CHOPS Applications Considering Geomechanical and Compositional Effects
Bibliographic record
Abstract
Abstract Cold Heavy Oil Production with Sand (CHOPS) is considered to be a promising method for heavy-oil recovery from unconsolidated sands, but it offers low recovery factors (~10%). Increasing recovery from such reservoirs should be achieved through post-CHOPS enhanced oil recovery (EOR) applications within an economic framework. Difficulties in modeling CHOPS are mainly due to the representation of wormhole network growth and foamy oil behavior. Also, the co-production of oil and sand simultaneously changes the fundamental reservoir properties including permeability, porosity, and formation compressibility, as well as in-situ stress conditions, making it even more difficult to model post-CHOPS EOR applications. One of the missing elements in current literature is a representative model capturing the growing nature of the wormhole network and geo-mechanical effects due to production or injection after CHOPS. To this end, in this study, we implemented fractal patterns based on DLA (Diffusion Limited Aggregation) algorithm along with a step-by-step simulation technique using a commercial simulator, coupled with a finite element geo-mechanical module to account for the in-situ stresses induced from overburden and gravity, or fluid injection during EOR processes after CHOPS. A mathematical model was also introduced to populate the wormhole domain based on sand production history, initial in-situ stress conditions, wormhole pattern, its radius and its length. The suggested approach was first validated using field data from a field in Alberta with fifteen CHOPS producers. Next, the model introduced and tested was used to assess post-CHOPS EOR processes such as thermal, solvent and their hybrid combinations using a black oil and compositional simulator. The latter raised certain difficulties which emerged from embedding the complex wormhole structure into the compositional model and the ways to overcome these difficulties were discussed. Finally, the model generated was used to test steam, solvent and their hybrid applications in the form of cyclic injection stimulation.
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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 | 0.000 | 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) | 0.000 | 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 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".