Sanding Prediction in a Gas Well Offshore Mexico Using a Numerical Simulator
Bibliographic record
Abstract
Abstract Sand production is a problem that plagues many reservoirs and has strongly affected benefit-cost relationships in the oil industry for years. Research dating as far back as the early 1930s has documented sand-production problems in unconsolidated formations. These problems are not related to one specific location or area, and although sand production is a worldwide problem, the major documented areas of sand production are in the USA, Canada, the North Sea, Europe, Venezuela, Bolivia, Brazil, and Colombia. Major causes of sand production include depletion, a change in flowing fluids, a change in stresses, and wellbore-stability failure. Failure to manage sand production can have a significant impact on the productivity of the well with the possibility of causing an eventual well collapse. In this paper, the application of a numerical simulator used for sand prediction in a gas well is presented. The simulator predicts the amount of produced sand and its effect on the productivity of the well. The model is based on the hydro-erosion model, first proposed by Vardoulakis in 1996 (Vardoulakis et al. 1996). The model is based on rigid, porous media (no skeleton deformation), in which mass balance is applied to a three-constituent system comprised of solid, fluid, and fluidized solid using the homogenization-mixture theory. Subsequently, Wan and Wang (2002) extended this pure-erosion model to include the effects of the deformation of porous media in a consistent manner. A single-phase flow is iteratively coupled with geomechanics within a continuum mechanics framework. Furthermore, Wang (2004) extended previous work to develop a fully coupled reservoir-geomechanics model to account for the effects of multiphase flow and geomechanics in a consistent manner. By using this numerical simulator application, the severity and quantification of the problem of sand production were resolved, resulting in an acceptable economical return. The results of this field case are documented below in further detail.
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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) | 0.000 | 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".