An Integrated Approach to Zero Emissions Valve Actuation
Bibliographic record
Abstract
Abstract Valves are critical components in the oil and gas industry from up-stream to down-stream supply chains that control operation, while protecting lives and assets. Valve actuation at and/or near the wellsite is commonly powered through pneumatic or hydraulic systems, process line pressure and/or manual operation. This involves field transportation and site equipment utilization and/or process gas venting leading to increased greenhouse gas (GHG) emissions. Enabling zero emissions valve actuation is achieved through the development of a self-contained low power electro-hydraulic unit with an intelligent valve controller. This smart valve actuation and monitoring system reduces GHG emissions through four major elements: low power electro-hydraulics, intelligent operation, analytics, and remote operation. System implementation was conducted on actuated wellhead gate valves, where these four major elements were integrated. Both low power electro-hydraulics and intelligent operation contribute to an improved power utilization, while the analytics and remote operation lead to a normally unattended facility through condition-based maintenance and improved operational efficiency, paving the way towards a reduced emissions system. The self-contained low power electro-hydraulic system has been lab tested and field implemented by major energy producers to minimize personnel field presence and allow remote operation, thus leading to a reduction in emissions. Many of the mechanical and electrical components have been validated through continuous operation over more than 2 years on Canadian wellheads with an ambient temperature less than -40C in some instances. In addition, the system was successful in protecting lives during the 2023 wildfires season in Alberta. The analytics conducted by the intelligent valve control system were successful in monitoring the valve, and hydraulic circuit and actuation health through key performance indicators (KPIs). Processing of full and partial stroke data coupled with physics-based models led to an evaluation of a new hydraulic pressure intensity (HPI) factor that can predict potential valve failure to open or close. In addition, evaluation of sensors’ time-based data was found useful to identify the malfunctioning of specific components in the hydraulic circuit. Moreover, early detection of hydraulic leakage and identifying its location in the hydraulic circuit was possible through evaluation of hydraulic pressure signatures. These results indicate the advantage of a smart valve actuation and monitoring system on improving operational efficiency and safety, while reducing emissions. The developed smart system offers a new integrated approach that combines several control, monitoring, and communication elements to achieve zero emissions valve actuation compared to the manual operation that contributes to the baseline carbon footprint. This approach also improves overall system compatibility and efficiency, compared to current independent multi-contractor system integration that leads to inefficient installation, commissioning, and operation.
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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".