The Study and Field Applications of Hydraulic Fracturing Technology in Clay-Carbonate Reservoirs with High Temperature, Deep Well Depth, and Densely Distributed Natural Fractures
Bibliographic record
Abstract
Abstract A hydraulic fracturing technology is put forward in clay- carbonate reservoirs whose characteristics (take well A as an example) are: 1)deep well depth(4237.5m), high temperature(146 °C), high pore pressure (42MPa); 2)high content of clay(15-22%) and high content of velocity-sensitive minerals (54-71%) such as illite and kaolinite; 3)horizontal stratifications are richly distributed as well as some natural fractures or cavities with high angles; 4) the matrix has a ultra-low permeability (0.0087-0.022 × 10-3μm2) and ultra-low oil saturation(12.3-18.3%). Consequently, the highlights of the hydraulic fracturing technology are: 1) a new fracturing fluid is developed using a super grade Guar as its densifier, with the viscosity of the base fluid being 93 to 102 mPa.s, while the residue content of 197 mg/l. 2) two types of high strength ceramic proppant are used, one is 20/40 mesh, the other is 40/60 mesh; 3) a new kind of mini-fracturing technique is utilized to determine the well head pressure under various pumping rate, near well bore friction pressure, loss coefficient and decrease reservoir temperature and control fracture height as well; 4) a new optimization technique of fracturing treatment parameters is developed systematically,. such as systematic laboratory tests for the fine evaluation of formation, strategy to avoid multiple fractures, monitoring technique of bottom hole pressure, etc.; 5) a new strategy is adopted to help flowing back of fracturing fluid, which takes the stress sensitivity near the well bore and natural fractures’ influences in to account. What's more, the technology has been put in to filed application in the well A, the fracturing treatment is success and the post-fracturing performance is satisfied. Summarily, a systematic fracturing technology is put forward adapted to clay-carbonate reservoirs, and it has a great significance in the near future especially in China.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".