Application of Novel Hyper-branched Polymer Fracturing Fluid System in Low-permeability Heavy Oil Reservoir
Bibliographic record
Abstract
Abstract Hydraulic fracturing can be used in low-permeability heavy oil reservoirs for improving the production rate. It is challenging to achieve a high degree of fracturing fluid flow-back in low-permeability heavy oil reservoirs. This research investigates the application of a novel hyper-branched polymer fracturing fluid in low-permeability heavy oil reservoirs. A hyper-branched polymer, which has numerous end groups and possesses a special highly branched three-dimensional spherical molecular structure, is characterized by Fourier Transform Infrared (FTIR) spectrophotometer. Laboratory tests are conducted to evaluate the stability of viscosity at a given shear rate, temperature and shearing resistance, reversible cross-linking property, filtration property, and proppant-carrying performance of this hyper-branched polymer fracturing fluid. The degrees of core's formation damage are compared through conducting coreflood experiments, where the cores are treated with two fluids: the hyper-branched polymer fracturing fluid and guanidine gel fracturing fluid. The laboratory evaluation shows that the hyper-branched polymer fracturing fluid has good rheological characteristics, a high solubility in water, as well as good temperature and anti-shearing resistance. After being sheared for 90 minutes with the shear rate of 170 s−1 at 150°C, it can still be reversed to gel with high strength because of its reversible cross-linking effect. This fracturing fluid has a good antioxidant ability, which allows its basic fluid to be exposed to air for 48 h without viscosity reduction. Therefore, the basic fluid can be prepared as spare fluid in a large amount, significantly improving job efficiency on site. Meanwhile, field applications in some wells in China show that: the friction of this fracturing fluid during fracturing is about 30%-50% to that of the normal guanidine gum fracturing fluid. Thus it has an ultra-low friction feature. The flow-back rate of the fracturing fluid can reach as high as 80%. Besides, its cost is about 75% to that of the normal guanidine gum fracturing fluid, making it a clean and cost-effective system. The fracturing fluid prepared with hyper-branched polymer possesses outstanding technical advantages as well as good cost effectiveness, implying a promising application in the large-scale fracturing stimulation of low-permeability heavy oil reservoirs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".