Bibliographic record
Abstract
Abstract The main objective of this paper is to establish a consistent geometrical frame focusing on the coupling between hydromechanical aspects of the sanding process, formation deformation/collapse and the resulting permeability change. Two types of sand production mechanisms are presented: production of coarse sands under mechanical failure and production of fine sands under hydro-dynamical erosion. The Drucker-Prager constitutive law with cap hardening criterion is adopted to study the sandstone deformation behavior. Finite element method is used to solve the coupled governing equation system. Field data for sand production and relative permeability change, collected from 10 wells in Gudong (Shengli, China), are used to validate the model. Examples to be presented in this paper indicate that the permeability can be modified any time during the entire sanding process of a well. Our numerical results show the fact that permeability decline in compaction region can reach up to 60% of the initial level under depletion production. Our studies also suggest that a balanced pore pressure strategy is the key to control the permeability decline. Introduction Sanding becomes more critical as operators follow more aggressive production strategies. This demands an improved understanding about the sanding mechanism and associated permeability changes. Generally, erosion during the sanding increases permeability near the wellbore and thus benefits the petroleum production. For weakly consolidated sandstone reservoirs, variation of stress and deformation around the wellbore is complex and localized zones of formation collapse and compaction may develop. As a result, the near-wellbore region experiences significant temporal and spatial changes in permeability during the sanding process. Sand production occurs when the well fluid under high pumping rate dislodges a portion of the formation solids leading to a continuous flux of formation solids. Sand production compromises oil production; increases completion costs, and erodes casing, pipes and pumps or plugs the well if sufficient quantities are produced. On the other hand, sand production has been proven a most effective way to increase well productivity both in heavy oil and light oil reservoirs. Another important sanding process occurs in formation damage (failure) leading to the wellbore instabilities (this is particularly serious for cold production in Shengli oilfield China). A high stress distribution, especially near a well and perforation tips, often induces local formation collapse. Such collapse region may spread with fluid flow and sand production process. These sanding effects are becoming more critical these days as operators are following more aggressive production schedules. This has led to a demand for understanding sanding process under an integrated theory frame system [1]. The challenge is to develop some mathematical models to quantitatively interpret this sanding developing process so that can predict sand production amount. A quantitative model will allow engineers to understand this unique and complicated sanding phenomena and process, evaluate the impact of sand production on reservoir enhancement, and provide an efficient measure to reduce unnecessary costs during the field operations. From the mechanistic viewpoint, sand production process mainly refers to the following factors: Inherent factors: including formation consolidation degree and strength, failure properties, porosity, et al.
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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".