Application of Polymer and Nanomaterials for Improving Heavy Oil Recovery
Bibliographic record
Abstract
Booming population growth and economic activity have contributed significantly to an increased demand for energy in the last few decades, specifically in Canada. A major source of energy is oil extracted from underground petroleum reservoirs. Utilizing current technology and equipment, only a small portion of oil can be produced and recovered. Steam-assisted gravity drainage (SAGD), used as a common technique to produce heavy oil in Canada (specifically in oil sands reservoirs), requires a lot of energy and negatively impacts the environment. Using environmentally friendly and cost-effective techniques instead of or combined with SAGD improves the extraction of oil from Canadian oil reservoirs. Reservoir pressure, which is a driving force for pushing oil toward production wells, reduces drastically in the early stages of oil production from underground resources. This leads to a significant decrease in oil production rate. To solve this problem, enhanced oil recovery (EOR) methods inject water, gas, or chemical solutions to maintain reservoir pressure. When water is injected (water flooding) into heavy oil reservoirs, it cannot push the viscous oil smoothly because of water’s lower viscosity as compared to oil. As a result, injected water tends to bypass the pores containing trapped oil and the flooding becomes inefficient. To overcome this problem, one method adds polymers to the injected water. The addition of polymers leads to a more uniform flooding by increasing the viscosity of the injected fluid. Unfortunately, this approach suffers seriously from degradation of polymers at high temperatures and precipitation of polymers due to interaction with ions like sodium and calcium in brine. To solve these problems, the addition of nanomaterials to a polymer solution is highly recommended. The main focus of this PhD dissertation is to evaluate the effect of surface chemistry and geometry of nanomaterial on creation of a network with large polymer molecules. In addition, to mimic the large deformations in converging and diverging pores in porous media, linear and nonlinear rheology were employed to characterize the mechanical and flow behaviors of these hybrid dispersions. Sandpacks were used as the porous media to simulate oil reservoirs. Different hybrid dispersions were injected into sandpacks and the yield of recovered oil was reported. Results of this work can pave the way for use of polymer/nanomaterial solutions for heavy oil recovery. This study also demonstrated that large deformation oscillatory shear tests can be employed to distinguish flow behavior of hybrid systems. It was also shown that interaction between polymer and nanomaterial affects network structure and, consequently, oil recovery. Furthermore, size of nanomaterial compared to pore size distribution of porous media is a significant parameter that should be considered. The outcomes of this study could be helpful in improving heavy oil recovery in thin oil formations such as the Cardium, Montney, and Ostracod formations. These formations are too thin to utilize processes like steam-assisted gravity drainage and solvent vapor extraction, making this technique significant for increasing oil recovery in Canadian heavy oil reservoirs.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".