Application and Study of Fine-Silty Sand Control Technique Using Fiber-Complex High-Pressure Pack in Sebei Gas Reservoir
Bibliographic record
Abstract
Abstract Sebei Gas Field in Qinghai is the fourth largest gas field in China. The reservoirs here are of unconsolidated Quaternary siltstones and pelitic siltstones, alternating gas and water layers and with serious sand production. The grain size of the sands is very fine, so it is very difficult to realize sand control, a cosmopolitan puzzle. We have succeeded in applying a series of techniques of comprehensive sand control with fiber-complex high-pressure packing but without any screen pipe. The technology includes the reservoir pretreatment, the fiber- complex technique and the high pressure packing. As for the reservoir pretreatment, the soft fibers, of which the branched chains are with positive electrical charges, are used to adsorb the fine silty sand particles to form big aggregations for sand stabilization. As for the fiber-complex technique, the 3-dimensional reticular fiber and resin-coated sand complex are adopted for sand retention. As for the high pressure packing, the formation is not fractured while the fiber composites are packed to form screen pipe-like barrier around the cement sheath to perform sand control without any screen pipe used, eliminate any damage to the near wellbore zone and increase the gas bleeding area. This technique can achieve the dual purpose of sand control and production stimulation. This technique system had been applied in Well Se 7-1-4 in Sebei Gas Field with remarkable results achieved. After such sand control treatment, the output without sand production increased on average to 2.1 times of the output before. This paper presents mainly the relevant theory, experimental study, operation design and field application of the fine silty sand control technique using the fiber-complex high-pressure packing without any screen pipe.
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 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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".