Sand Production Prediction: A New Set of Criteria for Modeling Based on Large-Scale Transient Experiments and Numerical Investigation
Bibliographic record
Abstract
Sand Production Prediction: A New Set of Criteria for Modeling Based on Large-Scale Transient Experiments and Numerical Investigation Alireza Nouri; Alireza Nouri Dalhousie University Search for other works by this author on: This Site Google Scholar Hans Vaziri; Hans Vaziri BP-America Inc. Search for other works by this author on: This Site Google Scholar Hadi Belhaj; Hadi Belhaj Dalhousie University Search for other works by this author on: This Site Google Scholar Rafiqul Islam Rafiqul Islam Dalhousie University Search for other works by this author on: This Site Google Scholar Paper presented at the SPE Annual Technical Conference and Exhibition, Houston, Texas, September 2004. Paper Number: SPE-90273-MS https://doi.org/10.2118/90273-MS Published: September 26 2004 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Nouri, Alireza, Vaziri, Hans, Belhaj, Hadi, and Rafiqul Islam. "Sand Production Prediction: A New Set of Criteria for Modeling Based on Large-Scale Transient Experiments and Numerical Investigation." Paper presented at the SPE Annual Technical Conference and Exhibition, Houston, Texas, September 2004. doi: https://doi.org/10.2118/90273-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search Dropdown Menu nav search search input Search input auto suggest search filter All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE Annual Technical Conference and Exhibition Search Advanced Search AbstractUsing novel physical model experiments and numerical analyses, a set of criteria are proposed that can be used to determine the onset of sanding and its severity in terms of rate and duration.Experiments on large-scale hollow cylinder specimens were conducted involving real time sand production measurement under various conditions. Synthetic sandstone was used in the experiments whose strength properties were comprehensively determined. A numerical approach was used for simulating the proposed experiments. The material behaviour was simulated using an elasto-plastic stress-strain relationship. The model simulates interaction between fluid flow and mechanical deformation of the medium in predicting sand production. The criteria considered for sanding are more comprehensive than conventionally used and included modeling strain softening of the material accompanied with shear bands formation as well as tensile failure. In the post disaggregation phase, additional features are considered including allowing for the removal of the disaggregated material and making the necessary adjustments to the size and properties of the domain under consideration. Hence, the model is considered to be suitable for time-dependent analysis of the rock as it undergoes disaggregation and production induced by depletion, drawdown, and water-cut. By relating the sand rate to production over the life of a field, the tool can be used in assisting with the completion and operation design of the wells.The model shows good agreement with experimental results in terms of rock deformation and sand rate. It predicted the onset of shear failure and the subsequent strain softening initiated from the cavity face and propagated inside the medium. Strain softening proved to be the main mechanism for material disaggregation. The good agreement between the numerical and experimental results under comprehensive and complex conditions bodes well for its field applications.IntroductionDespite the great effort to improve the prediction of sand production, field practices have been demonstrated that quantitatively accurate prediction of volumetric solid production yet needs to be developed, especially for poorly cemented sandstones. Existing models can predict the onset of sand production and analyze cavity stability and rock failure; however, they cannot predict the volumetric production of sand over time as a function of applied stresses, fluid flow rate and changes in water saturation.In the following, a brief description of the existing models is introduced.Modeling StrategiesSeveral analytical and numerical models are available for predicting sand production1,2,3,4,5,6. Some of these models assume sanding is due to seepage (tensile failure) while others base it on the strain level (compression). Most of the models predict sanding initiation4,7,8,9,10,11,12,13. Some other models view sand production as a mixed hydro-mechanical process14,15,16,17,18.Shear or Tensile FailureNumerous types of shear failure models have been published. An elastic brittle failure model is easy to implement19,20,21,22,23, but has disadvantage that it does not offer a very realistic description of friable and loose materials. An elastic-plastic material model involves more computational effort and, in return, enables a more realistic description of the material behaviour8,22.Stability criterion based on tensile failure has been expressed in terms of the drawdown pressure gradient at the cavity wall8,9. A critical value of the pressure gradient was first derived by Bratli et al.24. Their model has the weakness that the disintegration phase is overlooked in the stage of modeling. Keywords: strength, tensile strength, criteria, completion installation and operations, tensile stress, experiment, drillstem/well testing, reservoir characterization, kpa, effective stress Subjects: Reservoir Characterization, Formation Evaluation & Management, Perforating, Drillstem/well testing, Completion Installation and Operations, Completion Operations This content is only available via PDF. 2004. Society of Petroleum Engineers You can access this article if you purchase or spend a download.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".