Use of Reservoir Formation Failure and Sanding Prediction Analysis forViable Well-Construction and Completion-Design Options
Bibliographic record
Abstract
Using two field case examples, this paper presents our current well construction and completion design analysis based on the following approach: (1) carry out detailed evaluation or determination of reservoir formation strength distribution using core testing, log data and drilling data analysis for rock strength estimate and its correlation with core testing results; (2) conduct a series of triaxial tests on selected reservoir core samples in the low to intermediate strength range for defining the stress-strain relationship (or material laws), rock failure and yield criteria, and other non-linear rock parameters required for numerical modeling analysis; (3) perform a series of formation failure and sanding potential analysis for a variety of possible well completion design scenarios using 3-D finite element technique for rock structure coupled with well production and fluid flow simulation. The types of completion design analyzed include cased hole completion using conventional perforations or stress-oriented perforations in inclined or high-angle well, openhole completion in high-angle or horizontal well, screen failure analysis in openhole completion, etc. In addition to investigating the mechanical response of the rock formations in each completion design, the model simulates both well drawdown and reservoir depletion effects on sand failure potential throughout the reservoir life. The results of such systematic study provide useful guidelines on well design and completion strategy for sand control or sand management in order to optimize well productivity. The two case examples presented in this paper highlight the use of this technique and approach. We have used this type of analysis and process for the well design in our business operations around the world with good success.Based on our case example analyses and the specific rock failure characteristics as defined in the laboratory testing results and subsequent numerical simulations of sanding behavior, we are able to identify the most viable well construction and completion design for achieving a superior well deliverability and productivity for the long term, minimizing problems due to unintended solid influx and/or loss of well integrity over the reservoir life. The two field case examples in the North Sea as presented demonstrate and highlight the fundamental concept, methodology and the procedures for conducting the well design analysis through a series of computer simulations of various options for well completion schemes. The field example results will also show the effective use of rock failure characteristics by the engineers for the control of critical flowing bottom-hole pressure in relation to the reservoir drawdown and depletion to avoid premature sand failures during well production.
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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 | 0.001 |
| 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".