Low-Damage Massive Hydraulic Fracturing Technique in Tight Gas Formations With High Temperature and High Pressure
Bibliographic record
Abstract
This article, written by Senior Technology Editor Dennis Denney, contains highlights of paper SPE 114303, "The Study and Application of Low-Damage and Massive Hydraulic-Fracturing Technique in Tight Gas Formations With High Temperature and High Pressure," by Lei Qun, Tingxue Jiang, SPE, Xu Yun, SPE, Yunhong Ding, SPE, Yongjun Lu, SPE, Cai Bo, Yuhua Shu, and Yaoyao Duan, PetroChina, prepared for the 2008 SPE Gas Technology Symposium, Calgary, 16-19 June. The paper has not been peer reviewed. Many tight gas formations in the southern Songliao basin in northeast China have high pressure/high temperature (HP/HT) conditions. However, it is very difficult to conduct successful fracturing treatments in the deep wells (deeper than 4200 m) with temperatures greater than 150°C and pressures greater than 60 MPa. A study was conducted to overcome these difficulties. Introduction There are many difficulties related to hydraulic-fracturing design and treatment in tight HP/HT gas formations, including formation fines, initiation and propagation of the hydraulic fracture, selection of a low-damage and highly viscous elastic fluid, selection of a high-strength proppant that is transported easily, and that many fracturing-treatment failures result from early tip screenout. The commonly used fracturing technique included ordinary guar fluid with relative high polymer concentration and high residue content; medium proppant size of 20/40 mesh; a common proppant-pumping schedule such as 180, 360, 540, 720, and then 900 kg/m3; and longer shut-in time (at least 2 hours). Consequently, the post-fracturing production rate was disappointing. Formation Evaluation For fracturing design, it is critical to understand the targeted formation in both macro- and microperspectives. Macroparameters include sediment, perforation data, permeability, porosity, thickness, saturations, rock mechanics, in-situ stress magnitude and azimuth, and the distribution of these parameters. Macroparameters may be available from drilling, logging, and well-testing data. Microparameters include mineral components and sensitivities of micropore structure such as pore and throat diameter, along with natural fissures and their distribution. Microparameters may be obtained from test data of core samples in the laboratory. In fracturing-stimulation design, macroparameters may be used to optimize fracture length, conductivity, and proppant selection, while microparameters are used mainly to choose low-damage fluid additives and determine their mixture, optimize use of 100-mesh proppant or any other solids-loss reducers in case of natural fissures, and optimize the time and velocity of flowing back fracturing fluid in case of stress and velocity sensitivity.
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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 | 0.001 | 0.001 |
| 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".