S A

Author

Gianmarco Rossi

Company

Dept.

Company Tutors

Ing. Davide Sebastiano Lupica

University Tutor

Prof. Francesca Verga

Develop a baseline for Energy Efficiency Monitoring System.

Data Analytics and Statistical analysis optimization for datamanagement in order to develop a monitoring system whichprovide a cause-effect gap analysis and optimization.

Study a possible algorithm to estimate in advance the energyefficiency optimal profile from production data: the aim is toinvestigate the possibility to have a provisional tool in order tooptimize the energy performance of the plants.

Finalize an innovative system for the energy efficiencymonitoring and optimization for regular application to theUpstream plants.

Energy Intensity

EI =Consumed Energy [GJ]

Gross Hydrocarbon Production [toe]

Greenhouse gas Emission

ICO2=

Greenhouse gas Emission [t CO2]

Gross Hydrocarbon Production [kboe]

*IOGP – Data Series: Environmental Performance Indicators – 2014 data, Nov. 2015

Energy Intensity

EI =Consumed Energy [GJ]

Gross Hydrocarbon Production [toe]

Greenhouse gas Emission

ICO2=

Greenhouse gas Emission [tCO2]

Gross Hydrocarbon Production [kboe]

The values used to calculate the KPIs in this

work have to be understood in the

requirement of production only:

• The consumed energy is given by all activity

except for drilling ones;

• Greenhouses Emission are considered only

from from scope 1, stationary combustion

(excluding, then, venting and flaring).

• An Energy System Model was created to estimate the energyrequired in a production plant using as input only production data;

• The model follows the basis of TIMES Energy Model* by IEA.

• Main goal of the model is to provide a tool to help in choosing themost suitable production method.

*Tosato, G. C. Introduction to ETSAP and the MARKAL-TIMES models generators. IEA: Neet workshop on energy technology collaboration. 2008.

Tosato, G. C. Introduction to ETSAP and the MARKAL-TIMES models generators. IEA: Neet workshop on energy technology collaboration. 2008.

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Gas Compression

Electric Motor

Gas Treatment

Air TreatmentRotating Axle

Water Injection / Disposal

FG Power Plant

Incinerator

Steam Generator Hot Oil Treatment

TracingSeparator

Flare

Auxiliray to steam generation

General Service

Auxiliary / Emergency

*Diesel, kerosene, gasoline

• Energy System Modelling requires a set of drivers from externalsources and the construction of the reference demand scenario isachieved computing a set of energy service demands over thehorizon*.

• In IEA-TIMES documentation, this is done by choosing elasticity ofdemands to their respective drivers.

*Loulou, R. Remne. U., Kanudia, A., Lehtila, A. and Goldstein, G.(2005). Documentation for the TIMES Model-Part 1 (IEA).

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• The model was built mainlyconsidering:

Operating manual with design data

Energy assessment

Technical and physical description ofthe main utilities

𝐄𝐥𝐚𝐬𝐭𝐢𝐜𝐢𝐭𝐲 =∆𝐝𝐞𝐦𝐚𝐧𝐝%

∆𝐝𝐫𝐢𝐯𝐞𝐫%

Model calibration and validation for3 relevant asset;

The model follows very well theconsumption trend and in somecases is very accurate.

• A tool for ordering different perceptions aboutalternative future environments in which aset of decision might be played out.

• Scenarios are built around carefully constructedhypothesis that make the significant elementsof the energy scene stand out boldly.

• It should be seen more as disciplined way ofthinking than a formal methodology.

• Scenarios are built following the IEA booklet«Energy to 2050*»

*IEA, OECD. Energy to 2050: scenarios for a sustainable future. (2003).

*Eni Country A - Business Plan 2017-2026

Eni for 2015 – Sustainability Report

Eni for 2015 – Sustainability Report

*The saving percentage should only be seen as an indicative value.

The real one depends on the real plant condition, working parameters and specific energy improvement opportunity

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*Global, B. P., and B. P. Worldwide. "BP Energy Outlook 2035." (2016).

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The first part of the thesis was dedicated to gather production,

consumption and emission data to monitor Energy Efficiency

performance trends though KPIs analysis.

A cause-effect gap analysis between production and consumption was

provided through a model created from a reference asset and then

developed following mainly TIMES Energy Modelling technique and typical

asset configuration.

Energy Scenario was used to extend the model in order to provide a

provisional tool useful to compare the different energy performance of

the plant and then helping in choice the best strategy.

Business As Usual scenario evidences how improvement has to be

strongly considered in all plants. Dynamic But Careless scenario focuses

only on Production increase and we get a reduction on KPIs (≈20%) but

the values are still too high. Production doubles and Asset life is extended.

Moved only by the targets of sustainability (Clean But Not Sparkling

scenario) we can reduce the Energy Intensity and GHG Emission Index of

about 60% without big work-overs on the plant. Thus, this scenario is a

very good energy performance for Energy Efficiency.

Bright Skies, new carbon-free energy sources gives a strong reduction in

Energy Intensity and GHG emission of 50% and 30% respectively. The

trade-off is between fuel and emission saving plus production

increase and the high investment costs.

This model is a first approximation of energy consumption. It uses only

production data, statistical reservoir parameterization and generic plant

configuration. It gives a good idea on future trend and general

opportunity but cannot provide detailed information for a specific asset.

Next step should be to focus on a single asset and fit the model with the

plant and reservoir specification, with field survey energy assessment. In

such a way it will be possible to finalize an application for a regular

energy efficiency monitoring and optimization.

Last step could be the integration of the energy scenario with a Life Cycle

Cost Analysis (LCCA), to compare consumption and emission reduction

(OPEX) to the design improvement and modification cost (CAPEX).

I would thank Eni Management for permission to

present this work and related results and

PROD/RTI colleagues for the technical support

and needed assistance.

San Donato Milanese 17-18-19 October 2016

44

1) Define the problem and its horizon.

2) Gather information and build a coherent systemwith all relevant actors and agents, including thefactors and links between them.

3) Identify the key factors that would affect decisions.

4) Rank these factors by importance for the success ofthe focal issue, identifying the 2 or 3 factors ortrends that are most important and most uncertain.

5) Flesh out the scenarios in the form of consistentnarratives or "stories".

• Exploratory scenarios help to prepare for turns ofplausible events without representing a straightline continuation of past and present trends.

• Useful in proximity to bifurcations, especiallywhen a hint of such a situation takes shape inpresent day phenomena.

• Response to new developments (positive ornegative).

𝑞 = 𝑞𝑖𝑒−𝐷𝑡

𝑞 = 𝑞𝑖 1 + 𝑏𝐷 ∙ 𝑡 −1

𝑏

Fetkovich, Michael J. "Decline curve analysis using type curves." Journal of Petroleum Technology 32.06 (1980): 1-065.

*consumption and EI consider also the amount of energy given by renewable energy sources

Clean but not sparkling is interesting for sustainability, but RF is too low;

Production in Dynamic But Careless and Bright Skies doubles. The firstone would be the pathway followed in the 70-80s, while Bright Skies is thescenario we have to point towards to reach the sustainability targets.

• A single project slightly changes the Eni upstream KPIs (±1%), but severalnew projects will strongly influence the final results.

• Energy Efficiency policy are required in all asset: both producing plants andunder development projects. For new asset we should apply the criteria ofBright Skies to achieve the best future results.

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Compressors optimizationTo reduce stream re-circulation operating on pressure set-point

1000

Energy recover from wellhead pressure drop through turbines

High pressure fluids can be used to generate electrical energy in turbines

210

Thermal Energy recovery from wellhead with OCR

Production fluids have high Temperature, 135-100°C, and have to be cooled to 88-45 °C

130

Demethanizer column elimination

Gas demethanization could be done only by chiller 300

Water DisposalAdding a new injection pump to inject water at 90 bar in spite of 4 at 480 bar (no p sustain)

600

Electricity auto-generationUse FG to in-situ generation of electricity allows to reduce compressor and refrigerator consumption

5.300

TOT 2.240/7.540