Advances in Riser Management Technology Enabling Improved Efficiency for Deepwater and Harsh Environment Drilling
Bibliographic record
Abstract
Abstract Despite the abrupt fall in crude oil prices since 2014, operators continue to explore for, and develop, oil and gas resources in some of the most challenging offshore environments. Exploration and development drilling is currently ongoing or planned in locations such as West of Shetland, offshore Eastern Canada, along Ireland’s Atlantic margin, in the South Atlantic Ocean and offshore South Africa. All these locations are characterized by the challenges of deepwater, powerful ocean currents and high seas. With the lower oil price environment, carrying out drilling operations at these locations both safely and economically requires the adoption of new digital technologies and associated processes that maximize efficiency and reduce the cost of well programs. A significant aspect of this relates to planning and execution of operations involving the marine drilling riser, which can be a major contributor to non-productive time in deepwater and harsh environment locations. This paper describes a holistic approach to addressing this challenge, which covers every phase of riser operations for the drilling program, from pre-operations global riser analysis through to post-operations assessment. The paper focuses on the technology that enables this holistic solution, with emphasis on the state-of-the-art riser management technology that is deployed on the drilling vessel. This uses an advanced finite element model of the riser, BOP stack, wellhead, conductor, casing and soil interaction as well as a detailed model of the riser tensioning system. The same model is used in both the pre-operations global drilling riser analysis phase and the operational drilling phase to ensure consistency. Incorporation of the model provides the capability to perform forecast analysis on-board the rig, allowing offshore personnel to simulate a range of operations hours and days in advance using forecast metocean conditions, thereby assessing the feasibility of critical well construction operations before they commence. Capabilities for real-time monitoring of ongoing operations, fusing sensor data with the riser model, are also described. These provide calculation of live watch circles and operating envelopes for connected-mode operations, in addition to tracking of riser joint, wellhead, conductor and casing fatigue from both wave and VIV excitation. Additionally, calibration of soil models — often a critical input to wellhead fatigue analyses — can be performed. Application of the technology is illustrated by means of a case study describing deployment on a record-breaking well in a harsh environment location. This demonstrated significant cost savings while simultaneously increasing safety and improving integrity assurance.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".