Continuous Green House Gas Emission Surveillance, Reporting and Control
Bibliographic record
Abstract
Abstract The purpose of this paper is to document Shell's experiences and learnings inthe effort to better track and reduce Green House Gases (GHG) and improveEnergy Efficiency in our downstream manufacturing and upstream productionoperations. The paper is based on Case Studies from various Operating Units inShell upstream and downstream operations, as well as outlining furtherdevelopment plans. In Shell operations we seek to minimize GHG emissions by continuouslymonitoring, displaying and reporting associated Key Performance Indicators(KPI's) and quickly alerting operators of changes to trigger remedialintervention. Reduction of GHG emission is also achieved by improving crossvalidated and mass balanced tracking of our process streams. This ensures thatmanufacturing and production processes are operated efficiently andtransparently. Continuous GHG monitoring also allows automatic compilation of emissions bysource which can then be automatically reported as part of the normal dailyreporting cycle. The resulting emissions figures and associated KPI's are thenprominently displayed in the Daily Production Report. The daily emissionstotals are also stored and trended to flag more subtle and/or gradual changes. In this way GHG emissions data and performance information are made availableto operations staff and management to facilitate awareness and correctiveactions when appropriate. Introduction By 2050 the world's energy demand is likely to double - yet more than halfthe energy we generate every day is wasted. At the same time the world isbecoming increasingly more carbon constrained and in order to counter thesechallenges, Shell has defined a number of strategic pathways as part of theiroverall GHG management strategy and policy. The first strategic pathwayfocusses on energy efficiency in our own operations, both for existing and newassets. Energy efficiency is regarded within Shell as good business, yieldingoften attractive and immediate business value, while also providing improvedrobustness in the sustainability and profitability of our operations, especially in a carbon constrained world. The management process starts with understanding and measuring energyconsumption and GHG emissions and embedding this in our daily decision makingprocess while also using the "CO2 lens" for driving and validating the designchoices in our new assets. In order to do this successfully, real-timemeasurement of the key process parameters is required. This will provide theoperator with instant feedback of his process / operational choices re theimpact on energy intensity and GHG emissions. It can be compared with the fuelefficiency indicator in modern cars. By providing this feedback, it stimulatesbehavioural change and positions energy efficiency as a core value to beoptimised using daily operations. The pupose of this paper is to describe a number of case studies as arepresentative subset of our activities, and describe how real-time datameasurement and surveillance can be used to support our drive for operationalexcellence in energy efficiency, thereby focussing on the following of ourglobal businesses:Upstream E&P European operations - derived from real time processmeasurements;Downstream assets - derived from real time process measurements;North American mid-stream operations - derived from a combination ofreal time process measurements and valve flow estimatesInternational upstream E&P assets - derived from a combination ofwell gas flow estimates and real time process measurements Note, Shell is also very active in the area of CO2 sequestration - this outwiththe scope of this paper and is described elsewhere (Ref. 1)
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".