3. Develop a baseline for Energy Efficiency Monitoring System.
Data Analytics and Statistical analysis optimization for data
management in order to develop a monitoring system which
provide a cause-effect gap analysis and optimization.
Study a possible algorithm to estimate in advance the energy
efficiency optimal profile from production data: the aim is to
investigate the possibility to have a provisional tool in order to
optimize the energy performance of the plants.
Finalize an innovative system for the energy efficiency
monitoring and optimization for regular application to the
Upstream plants.
4.
5. 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
6. 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).
7.
8.
9. • An Energy System Model was created to estimate the energy
required 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 the
most suitable production method.
*Tosato, G. C. Introduction to ETSAP and the MARKAL-TIMES models generators. IEA: Neet workshop on energy technology collaboration. 2008.
10.
Tosato, G. C. Introduction to ETSAP and the MARKAL-TIMES models generators. IEA: Neet workshop on energy technology collaboration. 2008.
12. • Energy System Modelling requires a set of drivers from external
sources and the construction of the reference demand scenario is
achieved computing a set of energy service demands over the
horizon*
.
• In IEA-TIMES documentation, this is done by choosing elasticity of
demands to their respective drivers.
*Loulou, R. Remne. U., Kanudia, A., Lehtila, A. and Goldstein, G.(2005). Documentation for the TIMES Model-Part 1 (IEA).
α
13. • The model was built mainly
considering:
Operating manual with design data
Energy assessment
Technical and physical description of
the main utilities
𝐄𝐥𝐚𝐬𝐭𝐢𝐜𝐢𝐭𝐲 =
∆𝐝𝐞𝐦𝐚𝐧𝐝%
∆𝐝𝐫𝐢𝐯𝐞𝐫%
14. Model calibration and validation for
3 relevant asset;
The model follows very well the
consumption trend and in some
cases is very accurate.
15.
16. • A tool for ordering different perceptions about
alternative future environments in which a
set of decision might be played out.
• Scenarios are built around carefully constructed
hypothesis that make the significant elements
of the energy scene stand out boldly.
• It should be seen more as disciplined way of
thinking 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).
21. *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
1
38. 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.
39. 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.
40. 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).
41. 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
45.
1) Define the problem and its horizon.
2) Gather information and build a coherent system
with all relevant actors and agents, including the
factors and links between them.
3) Identify the key factors that would affect decisions.
4) Rank these factors by importance for the success of
the focal issue, identifying the 2 or 3 factors or
trends that are most important and most uncertain.
5) Flesh out the scenarios in the form of consistent
narratives or "stories".
46. • Exploratory scenarios help to prepare for turns of
plausible events without representing a straight
line continuation of past and present trends.
• Useful in proximity to bifurcations, especially
when a hint of such a situation takes shape in
present day phenomena.
• Response to new developments (positive or
negative).
47.
𝑞 = 𝑞𝑖 𝑒−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.
48.
*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 first
one would be the pathway followed in the 70-80s, while Bright Skies is the
scenario we have to point towards to reach the sustainability targets.
53.
• A single project slightly changes the Eni upstream KPIs (±1%), but several
new projects will strongly influence the final results.
• Energy Efficiency policy are required in all asset: both producing plants and
under development projects. For new asset we should apply the criteria of
Bright Skies to achieve the best future results.
58. Compressors optimization
To 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 Disposal
Adding a new injection pump to inject water at 90
bar in spite of 4 at 480 bar (no p sustain)
600
Electricity auto-generation
Use FG to in-situ generation of electricity allows to
reduce compressor and refrigerator consumption
5.300
TOT 2.240/7.540
