Factors Affecting Horizontal Well Gravel Pack Efficiency
Bibliographic record
Abstract
Abstract Four major factors affecting horizontal well gravel pack were studied using a 3D simulator developed for horizontal well gravel packing. The factors included settling effect, gravel concentration, injection rate and carrier fluid viscosity. Three actual field horizontal well gravel pack jobs obtained from the literature were performed using the simulator to study these factors. The effect of carrier fluid viscosity on gravel pack efficiency was studied by varying the viscosity between 1 and 51 cP, injection rate between 0.159 and 0.636 m3/min and gravel concentration between 0.50 and 4.0 pound mass per gallon. Simulation results demonstrate the validity of the solution routine and the capability of the simulator, because the results were in agreement with the field results. The predicted pack efficiency for cases that considered settling effect are consistently higher than the cases without settling effect. The study also showed that the settling factor decreases with increasing gravel concentration and injection rate. Introduction Several authors have investigated the factors affecting gravel transportation and placement towards achieving an effective gravel pack. Gruesbeck et al.(1) performed experiments to measure pack efficiency as a function of screen parameter, fluid and gravel properties, completion configuration and angle of inclination of the wellbore. They concluded that packing efficiency increases with lower gravel concentration, lower gravel density, higher flow rate and increasing resistance to fluid flow in the tailpipe/screen annulus. Hodge(2) substantiated Gruesbeck et al.'s work by determining the accuracy of the predicted equilibrium bank height. Elson et al.(3) reported a study conducted to define optimum gravel pack procedures and completion design factors for high angle wells. Results of the study showed that high viscosity carrier fluids with high gravel concentration provide good gravel transport, but are unsuitable in wells with angles of 80 º from vertical. Skaggs(4) presented the results of a large-scale vertical wellbore model he used to study gravel transport through perforations during a high-density squeeze gravel packing operation. He concluded that the transport efficiency through perforations increases with increased fluid viscosity, gravel concentration and annular velocity. Winterfeld and Schroeder(5) developed a finite element numerical simulator and used it with a full-scale wellbore model to study gravel placement in perforations and annulus. Their model was based on mass and momentum conservation equations, as well as those for vertical wells. Peden et al.(6) developed some mathematical design models for predicting the optimum combination ofrequired design parameters, such as tailpipe diameter, slurry flow rate and gravel concentration, for an optimum packing efficiency. These models were based on extensive experimental study of factors affecting packing efficiency and dimensional analysis of obtained data. In 1988, Wahlmeier and Andrews(7) improved on the earlier works of Gruesbeck et al. and Peden et al. by developing a pseudo-three-dimensional mathematical model suitable for designing and evaluating gravel pack treatments. Shryock(8) worked on a full-scale deviated model and had similar conclusions with earlier works.
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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 | 0.001 |
| 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 | 0.001 | 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 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".