Fluid Placement Model for Horizontal-Well Stimulation
Bibliographic record
Abstract
Summary Acidizing is a common method used to stimulate horizontal wells. The acidizing process is fundamentally different when applied in a horizontal well compared with application in a vertical well. The fluid distribution in a horizontal well is affected by a longer wellbore length, a broader variation in the reservoir properties along the wellbore, and possibly different mechanical means by which to place the fluids in the wellbore. A comprehensive fluid placement model linked with a reservoir acidizing simulator is essential to precisely design acidizing treatments for horizontal wells. In this paper we present a model of fluid placement in a horizontal well. The model predicts the placement of injected fluids by tracking the interfaces of different fluids in the wellbore. It is capable of tracking multiple interfaces for multiple injection stages in horizontal wells. For injection with coiled tubing, the model allows tubing tail movement during injection. It also handles simultaneous injection from the annulus and from a tubing string. The fluid distribution generated from the model can be used as input information in a reservoir acidizing model for sand-stone acidizing design. Both analytical and discretized solutions of the model are presented in the paper. Examples in the paper illustrate the effects of such factors as velocity of tubing movement, annular injection, and a nonuniform distribution of flow into the reservoir on fluid placement in an acidizing treatment. The fluid placement model can help to determine optimum tubing tail locations and optimal injection volumes of acids, to select the most appropriate diverting methods, and, hence, to maximize the benefits of an acidizing treatment.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".