Instrumented Wellhead Load Relief System for Shallow Water Arctic Conditions: Paper 1 — System Design, Installation and Preliminary Results
Bibliographic record
Abstract
In recent years, lower oil prices have forced many oil companies to reduce capex costs by revitalizing brown fields, rather than developing new green fields. At the same time, the offshore drilling rig market has seen many old rigs, typically used for shallow water operations, being scrapped, leaving new generation, deep and ultra-deep water MODUs as the only viable option for new drilling campaigns. Based on the above, wellhead fatigue on older assets, especially in harsh, shallow water environments, has started to gain a central role during the planning phases of workover and intervention operations. In recent years, Suncor Energy began investigating an extension to its Terra Nova field, which began production in 2002. The field uses subsea wells tied back to an FPSO which is moored in 95m of water off Canada’s eastern Grand Banks, an area frequented by icebergs. Drilling operations for the field extension were planned to commence in summer 2017, and continue with a year-round drilling campaign using a Cat 6 MODU. Since the extension would involve sidetracks and interventions from existing wellheads, a series of wellhead fatigue studies were undertaken using a variety of industry recognized methodologies [1] to understand the levels of fatigue accumulation. Although there has been no evidence of wellhead fatigue damage, Suncor chose to take a very prudent and proactive approach, aimed at minimizing fatigue, and maintaining fatigue life for potential future drilling operations. An Instrumented Wellhead Load Relief (iWLR) system was installed, which is designed to restrain BOP motions, thereby reducing the wellhead loads considerably. The load reduction system virtually eliminates additional fatigue accumulation for the planned operations. Additionally, the instrumentation system enables the precise monitoring and tracking of loads applied at the wellhead for future analysis. This paper describes the engineering challenges needed to develop and install the iWLR system in a harsh, shallow water, arctic environment. This area is characterized by very stiff soils pitted with iceberg scours, where subsea equipment must be protected within 10m deep excavated drill centers to prevent iceberg collisions in the relatively shallow water. Additionally, the paper describes how the instrumentation system was integrated with the BOP MUX cable communication system, for the first time, to enable real time monitoring of BOP motions using high accuracy gyroscopes and load cells which monitor dynamic iWLR tether forces. A topside data gathering and processing system was developed to present wellhead loads based on the indirect method, with new algorithms established to account for the tether forces. Finally, the paper presents some preliminary high-level results, showing the efficiency of the system based on measured data.
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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".