Foam Assisted Air Injection (FAAI) for IOR at Hailaer Oilfield: Prospects and Challenges
Bibliographic record
Abstract
Abstract The Xi11-72 Block, located in Hailaer Oilfield, Inner Mongolia Autonomous Region, China, contains many ultra-low permeability (<2.0×10-3µm2) oil reservoirs, with natural fractures. The reservoir temperature is 82°C; the original reservoir pressure is approximately 22.5 MPa; and the water-sensitive index of the reservoir is high (>0.7). Primary production and limited water flooding experience at adjacent Blocks have shown that the recovery factor in these reservoirs is very low due to lack of reservoir energy and poor water injectivity. Since 2009, Foam Assisted Air Injection (FAAI) has been proposed, in order to block natural fractures, maintain reservoir pressure and/or increase sweeping and displacement efficiency. A series of laboratory experiments have been conducted to study the oxidation kinetics of air/air foam with oil and the blocking and displacement efficiency of air foams in different oil sands. Reservoir simulation has also been carried out for predicting the reservoir response to foam assisted air injection and optimizing the injection process. Air injection pilot test started in the field since 23 April 2011 in a well group with 1 injector and 6 producers, using a Skid-mounted high pressure air compressor (40 MPa, 7 m3/min air rate). Oxygen breakthrough was observed (oxygen contents 3.7%) on 13 May 2011, and then assisted foam was injected to inhibit air breakthrough. Up to 30 June 2011, five foam slugs had been injected into the reservoir. The field results show that air injection can enhance injection capacity; assisted foam injection can inhibit air breakthrough effectively; and oil production can be significantly increased with water cut reduced by 4%. The detailed laboratory study, field experience and prospects and challenges analysis are presented in this paper.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".