Sand Production Prediction Analysis of Heterogeneous Reservoirs for Sand Control and Optimal Well Completion Design
Why this work is in the frame
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Bibliographic record
Abstract
Abstract This paper provides our approach to making sand production and sand rate prediction analysis for a gas and gas condensate field located offshore in the South Natuna Sea. Since the reservoirs are very heterogeneous and containing four major layers or producing intervals, the prediction of their sanding potential becomes more complex and thus requires a more elaborate and sound judgment to make a reasonable assessment. The key objective of this evaluation and sand rate prediction is to come up with an optimal plan for well completion design and providing effective sand control throughout the life of such multiple reservoirs. Sand production due to the failure of reservoir formation resulting from pressure depletion and drawdown pressure often causes significant loss in well production, facility damage, and can ultimately lead to shut-in of the well after continuous sanding-up. It is most worthwhile if we are able to predict the sanding potential of any given reservoir during continuous well production under certain completion design. Our ability to reliably predict such sanding potential and sand production rate can help generate an optimum design for well completion by running a series of computer simulations for various design scenarios. Our study showed that if the reservoir rock strength and its variation along the depth were measured for each well, the conditions that induce sand production problem for each interval could be predicted. The most important factors contributing to sanding problems were the rock strength, flowing bottom-hole pressure, reservoir pressure, in-situ stresses, and flow rate. Therefore, if permeability distribution and oil/gas and water saturations were measured for each well in addition to the rock strength, the best completion method to reduce sand problems without significantly decreasing oil or gas production can be identified without going through the costly trial-and-error selection method in the actual field. A 3D non-linear elastic-plastic finite element model incorporated with fluid-flow module for reservoir component has been effectively used for such numerical simulations. The results of this investigation conclude the following key points for optimal and effective well completion design:there are sporadic weak sands found in all four major intervals of the reservoirs and it's not possible to use a selective perforation scheme for this field;the average sand rate as predicted is too high so that at least half of the high sand producers will require an installation of some downhole sand control measures;The installation of a sand rate detection device at around the flow-line elbows is necessary and prudent;It is necessary to monitor the amount of sand production using equipments such as sand traps, sand rate measurement devices, and erosion coupons for better protection or timely replacement of the critical lines and flow pipes;produce the reservoir with smooth reduction of reservoir pressure by limiting the drawdown pressure to be 250 psi or smaller in order to reduce the sand rate by 50–75%.
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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 | 0.001 | 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 it