Safely Improving Production Performance through Improved Sand Management
Bibliographic record
Abstract
Abstract Many operators implement a conservative approach to sand management implementing a "Zero Sand Production" or "Maximum Sand Free Rate" Criteria. This is due to the potentially severe consequences associated with sand production i.e. erosion, and the fact that existing standards and guidelines /1/ do not provide sufficient practical advice on how to manage erosion issues during operations. These criteria generally put restrictions on the production rate (and revenue) to reduce sand production and decrease the risk of erosion leading to a loss of containment. These restrictions are in many cases unnecessary. This paper describes the development of an alternative approach which improves the production and safety performance of fields that are capacity restrained due to sand problems. The development of the approach started with a project for Conoco 1996 - 2000 /2/ and was further refined on a pilot project for Statoil in 2001 /3/, where it received the Statoil prize for the most successful R&D project in 2001. From an erosion perspective the amount of sand produced is only one of many factors that must be managed; equally important factors are sand particle velocity, impact angle and material grading. By gaining a better understanding of the specific erosion characteristics of a field through: well sand risk ranking, identification of erosion critical components and detailed erosion assessment including 3D computer modeling; a more sophisticated erosion management strategy can be implemented identifying specific asset operational criteria, erosion monitoring and inspection requirements. Implementation of this erosion management approach has provided operators with enhanced production, reduced inspection and maintenance costs without compromising safety and environmental targets and increasing/enhancing business performance.
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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".