Status of Heavy-Oil Development in China
Bibliographic record
Abstract
Abstract China has significant heavy oil deposit of more than 1.9 billion tons of oil reserve in place (OOIP) with four major heavy oil producing areas, which are Liaohe Oil Field, Xinjiang Oil Field, Shengli Oil Field and Henan Oil Field. China has many types of heavy oil reservoirs such as single-layer, multi-layer, thick-blocked reservoir with wide range of oil viscosity from 100 cp to 100,000 cp and depth from 200m to more than 2000m. Heavy oil has been produced for many years in China. However, the commercial heavy oil development was initial in 1982, when the first cyclic steam injection pilot test was successful in Liaohe Oil Field. In 1993, the heavy oil production had reached 10 × 106 tons per year. From then on, the annual heavy oil production has kept the level of 10~13 × 106 tons for more than 10 years. The development manners of heavy oil reservoir are cyclic steam stimulation (CSS), steamflooding, waterflooding. CSS is the major manner, widely used in traditional-heavy, extra-heavy and super-heavy oil reservoir in China with the annual production more than 85% of total heavy oil production. CSS has become a mature industry technology, which includes high-efficient steam injection, artificial lifting, sand controlling, re-entry drilling, steam surveillance and so on. Steamflooding is successful in developing shallow heavy oil reservoir such as Karamy oil reservoir, including high-temperature profile conformance, surveying and steam measurement technologies. This paper reviews the distribution of heavy oil resources, status of heavy oil development, trends and also the challenges faced in improving utilization of the resources 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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".