Integrated Optimization of Horizontal Well Performance
Bibliographic record
Abstract
ABSTRACT It is crucial to accurately predict the horizontal well flow performance so that the optimum production can be achieved. The horizontal well flow performance has generally been conducted under the assumption of uniform-influx profile along the horizontal wellbore, which neglects the pressure loss in horizontal wellbore and can cause an unreasonable well design. This paper developed a model to optimize the horizontal well performance by integrating the fluid flow in reservoir, the flow in sand control zone near wellbore, the flow in horizontal wellbore and tubing into an integral hydraulic system. The variable mass multiphase flow characteristics in the horizontal wellbore and the fluid lifting behaviors in tubing are also taken into account. In addition, the effects of reservoir properties, fluid properties and wellbore characterisrics and different well completions, involving open hole, slotted linear, pre-packed linear and gravel pack, on well performance are analyzed. The optimum well design is determined accordingly following the criterion presented. It has been shown that the integrated model enables not only the better understanding of the complexity of the interaction between reservoir and wellbore, but also the overall optimum and coordinated operation of the whole system. For example, in the case of accurate prediction of the liquid-gas flow characteristics in wellbore, it is necessary to build detailed flow pattern model because the liberation of a large amount of gas can significantly affect the overall pressure loss, tubing lifting and production. Another example is that most pressure in the whole hydraulic system is consumed in tubing lifting, therefore effective tubing lifting design also helps obtain the optimum production. Using our integrated model we have identified the different effects among different kinds of sand control well completion methods. We have also shown that the well length, wellbore diameter and producing gas-oil ratio are the major factors affecting the well production.
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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 | 0.000 | 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 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".