Bibliographic record
Abstract
Abstract This paper first presents a consistent mathematical framework to predict sand production volume, focusing on the coupling between hydro-mechanical factors, formation deformation and the resulting permeability change. Two types of sand production mechanisms are considered: production of coarse sands under mechanical failure and production of fine sands under hydro-dynamical erosion. The Drucker-Prager constitutive law with cap hardening is adopted to describe both dilative and collapsing deformation behaviour. The finite element method is used to solve the coupled governing equation system. After the model is validated with a field history case, it is used to compute two examples of wellbore pressure drawdown and the associated impact on the near-wellbore sanding process and permeability change. The calculation indicates that the permeability can be modified any time during the sanding. For example, under suitable reservoir depletion, the near-wellbore permeability can increase by 30%. However, more drastic pressure depletion under the otherwise identical in situ and operating conditions causes compaction near the wellbore and permeability decline by nearly 40%. Therefore, these simulated cases suggest that a balanced pressure depletion strategy should be used to manage the sand production. Introduction Sanding becomes more critical as operators follow more aggressive production strategies. Sand production occurs when the reservoir fluid, under high production rates, dislodges a portion of the formation solids leading to a continuous flux of formation solids into the wellbore. As a result, the sanding may compromise oil production, increase completion costs, and erode casing, pipes and pumps, or plug the well if sufficient quantities are produced. Moreover, the sanding process may cause complex temporal and spatial changes in permeability in the near-wellbore region. Generally, erosion during sanding increases permeability near the wellbore and thus benefits the petroleum production. Therefore, sand production has proven effective to increase well productivity, both in heavy oil and light oil reservoirs. However, stress concentration around the wellbore and/or perforation tips, if aggressive pressure drawdown is carried out, can induce localized formation collapse and compaction. Such a collapse/compaction region may spread outwards deep into the formation as the fluid flow and sand production continues. The formation compaction may lead to permeability impairment which is equivalent to formation damage, albeit mechanically induced. This is particularly serious for weakly consolidated sandstone reservoirs such as in the Shengli Oil Field in China. Therefore, however demanding operators are by wanting to increase production, it is critical to design a proper production strategy that minimizes the negative impact of sand production on field operation and reservoir permeability change and/or maximize its beneficial effect. This can only be achieved via an improved understanding of the sanding mechanism and associated permeability changes; in particular, the need to understand the sanding process under an integrated theoretical frame system(1, 2). The challenge is to develop relevant mathematical models to quantitatively interpret the evolution of the sanding process so that the amount of sand production can be predicted. A quantitative model will allow engineers to understand the complicated sanding phenomena, evaluate the impact of sand production on reservoir production and provide an efficient measure to reduce unnecessary costs during field operations.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".