Forecasting Energy Demand, Emissions and Discharges for the Petroleum Industry - Examples and Experiences
Bibliographic record
Abstract
Forecasting Energy Demand, Emissions and Discharges for the Petroleum Industry - Examples and Experiences Kristin Keiserås Bakkane; Kristin Keiserås Bakkane Novatech a.s Search for other works by this author on: This Site Google Scholar Geir Husdal; Geir Husdal Novatech a.s Search for other works by this author on: This Site Google Scholar Marta S. Linde Melhus; Marta S. Linde Melhus The Norwegian Petroleum Directorate Search for other works by this author on: This Site Google Scholar Toril Røe Utvik Toril Røe Utvik Norsk Hydro Search for other works by this author on: This Site Google Scholar Paper presented at the SPE International Conference on Health, Safety, and Environment in Oil and Gas Exploration and Production, Calgary, Alberta, Canada, March 2004. Paper Number: SPE-86606-MS https://doi.org/10.2118/86606-MS Published: March 29 2004 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Bakkane, Kristin Keiserås, Husdal, Geir, Linde Melhus, Marta S., and Toril Røe Utvik. "Forecasting Energy Demand, Emissions and Discharges for the Petroleum Industry - Examples and Experiences." Paper presented at the SPE International Conference on Health, Safety, and Environment in Oil and Gas Exploration and Production, Calgary, Alberta, Canada, March 2004. doi: https://doi.org/10.2118/86606-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search nav search search input Search input auto suggest search filter All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE International Conference and Exhibition on Health, Safety, Environment, and Sustainability Search Advanced Search AbstractThis paper presents state of the art methodology for establishing reliable fuel consumption and emissions to air forecasts for the offshore petroleum business in Norway. The methodology is applicable for any operation within the upstream oil and gas industry. It is unique due to a combination of simple input, simple algorithms and accurate output results.The general forecasting method is established as a process between the operators and Norwegian Authorities, under the management by FUN (Forum for Forecasting and Uncertainty Evaluations). The accuracy is proven by simple calibration techniques, comparing measured fuel consumption against calculated demands from using the forecasting method.Forecasts have been established by Novatech on behalf of the operator Norsk Hydro. Results for the Troll oil field are shown as a sample case. The case verifies the ability to forecast fuel consumption within an accuracy of 2–3% when the forecasting model is checked by use of actual activity level input data.Also by the Norwegian Petroleum Directorate (NPD)'s experience the precition of reported fuel and emission forecasts has been gradually improved as the methodology as described below has been implemented by the operators.Background and ApplicationsEach year the operators on the Norwegian Continental Shelf prepare fuel and emission forecasts to be reported to the Norwegian authorities, as input to the Revised National Budget (RNB). The data from the upstream oil and gas industry are received and evaluated by the Norwegian Petroleum Directorate (NPD).Similar forecasts are required for a number of reasons, as for instance as input to field development and operational planning; company internal, field licensee or shareholder reporting; emission permit applications and other environmental concessionary requirements set by the authorities, and for OPEX forecasting if any national taxes or carbon trading systems apply.All these purposes meet in a desire to make realistic quantification of future fuel and energy demands, greenhouse gas emissions etc. But for these forecasts to have any value, they must be fairly accurate. For the oil and gas industry this would as a minimum require a correlation to the activity levels; i.e. basically the drilling level and the throughput of oil, gas and water handled on an offshore installation or field. Preferably it should also reflect any major production philosophies and the technical design and constraints, or perhaps rearrangements/replacements that may be foreseen during the field production lifetime (or the forecasted period).Ref. [1] describes a general methodology for doing all this, and with means of relatively easily attainable information. The method relates future emissions, fuel consumption and energy needs to each other and to the oil, water and gas production, injection and deliveries as well as the drilling activities measured in number of wells drilled. Such forecasts are normally readily available. Furthermore, the accuracy of the fuel forecasts can be checked and the calculation models involved may be calibrated to improve the accuracy if required. Information from the forecasting model established may even be used actively for operational optimization purposes for a given case. This may actually identify potentials for a reduction in fuel demand, reduced expenditure by lower fuel consumption and possibly lower taxes, minimised maintenance down-time, improved efficiencies, reduce losses like for instance by flaring, etc.The methodology is presented in the next chapter, followed by a sample case and experiences gathered by the second largest operating company on the Norwegian Continental Shelf, Norsk Hydro, as well as by the Norwegian Authorities, represented by the NPD who receives and evaluates the annual RNB reporting from the operators. Keywords: emission factor, emission forecast, energy demand, air emission, operator, input data, throughput, fuel consumption, calculation, upstream oil & gas Subjects: Environment, Air emissions This content is only available via PDF. 2004. Society of Petroleum Engineers You can access this article if you purchase or spend a download.
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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".