Productivity Prediction for Stacked Multilateral Horizontal Well Under Open Hole Series Completion Methods
Bibliographic record
Abstract
Productivities of stacked multilateral horizontal well under open hole series completion methods were predicted by an analytical model. The analytical model was established using conformal transformation, mirror image, potential superposition and equivalent flow resistance. The ideal well’s formula will be simplified to the famous Borisove’s formula when stacked well has only one branch and locate it in middle vertical depth of reservoir. Several productivity influencing factors were analyzed to provide references for stacked well completion design. Case studies show that, stacked well productivity decreases with higher screen filtration precision; Conventional horizontal well productivity is more sensitive to filtration precision than that of stacked well; Stacked well’s vertical location in reservoir with upper and lower sealed boundary has little impact on productivity; Analytical model overestimates productivity because of ignorance of seepage disturbance and well bore flow pressure drop, an infinitesimal sectional model works as a correction model and a correction coefficient is obtained to effectively reduce the error of analytical model. Key words : Stacked well; Open hole; Analytical model; Mirror image; Potential superposition; Seepage resistance
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".