Green Fracturing Technology of Shale Gas: LPG Waterless Fracturing Technology and its Feasibility in China
Bibliographic record
Abstract
Abstract As a kind of water saving and green fracturing methodology, the innovative LPG fracturing fluid system that is developed by Gasfrac is known for its low density, viscosity and surface tension, making satisfying achievements in McCully Gasfield in Canada. However, it has not been applied by scale in China and requires further analysis. The LPG fracturing technology was analyzed technically and its physical properties were studied, including viscosities and surface tensions of water, butane and propane within various temperature ranges. A LPG fracturing phase control diagram and a fluid capillary pressure diagram were drafted. Production data of many shale gas wells in McCully block were analyzed to accomplish the fracturing flowback formula. The economical analysis of the LPG fracturing technology was completed, including the overall cost of slickwater fracturing fluid and water treatment, as well as the overall cost of LPG fracturing fluid and its integrated devices. The slickwater fracturing fluid has been widely used in China to exploit shale gas, which wastes a lot of water and does harm to environment. The LPG fracturing technology utilizes liquefied petroleum gas as the fracturing fluid that mainly consists of propane with ethane, butane, propylene and some additives, so it is harmless to formation. Compared with conventional hydraulic fracturing, the LPG fracturing fluid is mixed with chemical additives when being pumped and it becomes a kind of viscous fluid like gel, so that proppant particles may be evenly distributed in it and they don't deposit along fractures. Besides, fractures are higher and the production life of gas wells is improved. Propane mixes with natural gas completely and it may reduce the oil's viscosity after contacting it. Therefore, there is no need of flowback and water treatment procedure. Injected in a closed loop, the LPG fracturing fluid is operated via a remote computer and monitored by sensors distributed in the operation area, so that LPG leakage risk is reduced and the fracturing operation may be carried out safely. Shale gas is being widely exploited in Sichuan Province, which is located at the source of Yangtze River. In Ordos Basin limited water sources make it very costly to prepare conventional fracturing fluids. The slickwater fracturing fluid is cheaper than the LPG fracturing fluid, while water treatment costs more. All in all, LPG fracturing technology shall be recommended in China that suffers from severe environmental risks and water scarcity.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".