Application of Deep Hydraulic Jet Perforating to Enhance Oil Production in Thin Reservoir with Bottom Water
Bibliographic record
Abstract
Abstract Exploitation of thin oil reservoirs with bottom water is a difficult task. Oil production rates are decreased rapidly because of water coning and inapplicability of stimulation methods such as fracturing, in many instances which makes wells have to be suspended or abandoned even at low levels of recovery. In this paper, deep hydraulic jet perforating technique (DHJP) is presented which provides an effective way to exploit such kind of reservoirs. Also the strategy, equipments, and operational procedures of the deep hydraulic jet perforating are discussed. Deep hydraulic jet perforating is different to the well-known hydrajet perforating (HJP). In the HJP, the typical penetration depths are 12.7–20.3 cm (Kritsanaphak, Tirichine & Abed, 2010) and the jet nozzle does not move during the process. For deep hydraulic jet perforating technique, the casing is windowed mechanically with high water-pressure and the formation is penetrated by the jetting, meanwhile, the nozzle driven by screw, keeps going further in the formation through the window. Thus, the extreme deep depth can be achieved to 2 m approximately depending on the formation characteristics, and stand-off of the tool inside the casing. Compared with the most widely used conventional explosive shape-charge perforating, the advantages of deep hydraulic jet perforating include (1) creating superior connectivity between the wellbore and formation; (2) enlarging the oil contact area; (3) leaving less near-wellbore damage. Hence, deep hydraulic jet perforating can be a good alternative for well completion, formation damage removal and hydraulic fracturing. Deep hydraulic jet perforating has been applied successfully in JS Oilfield for the thin formation with bottom water to enhance oil production. Totally 10 holes for two producers are penetrated in target zone and the effective hole depth ranges from 1.55 to 2.01 m. The production rates were doubled and the shut-in well is revitalized again.
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 | 0.000 |
| 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".