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Digitalization for Reducing Carbon Footprint in Drilling Operations

Authors: Flavio Ferrari; Riccardo Naselli; Paolo Brunetti; Jean Michelez; Edoardo Zini;

Digitalization for Reducing Carbon Footprint in Drilling Operations

Abstract

Abstract Objectives/Scope Drilling activities are energy intensive, in order to support, for example, heavy loads, high volumes circulation, and high torque equipment. As of today, this energy is mainly provided by diesel generators consuming tons of fuel every day. Hence, drilling activities are a significant producer of greenhouse gases (GHG) in the upstream industry, therefore drawing attention on the potential for emissions reduction. There are two ways for reducing emissions: changing the source of energy, and reducing the consumption. This paper is focusing on the latter, addressing the potential for GHG reduction thanks digitalization of the rig operations. Methods, Procedures, Process The process is structured in two phases: Phase 1 - data monitoring Rig operations provide different data sources from rig sensors and daily reporting. The digitalization process in place in Saipem is gathering and consolidating these data on rig site and in headquarters in real time. On one hand, dedicated algorithms are applied to identify the rig state (type of ongoing operation) every 5 seconds. On the other hand, engines’ consumptions data are provided either through reporting or from engines monitoring systems (where available). All these data are then consolidated and displayed on interactive dashboards, providing insightful information on fuel efficiency and energy consumption by type of operations for each rig. Phase 2 - consumption optimization By analysing the power needs according to a given environment (eg. depth) and operational conditions (eg. tripping) the system provides the best statistical performance recorded from the rig fleet and set it as a target for low emission operations. Then the operators on the rig have clear instructions on how to utilize their diesel generators to ensure both operational safety and emissions reduction. In addition, the use of the engines at an optimal level supports also availability (less failures) and maintainability (longer lifetime). Results, Observations, Conclusions The system in place has produced valuable results in less than 6 months, by offering a clear visibility on the most consuming activities and the definition of best-in-class energy-efficient operations. These instructions are distributed among the rigs, and the operators can proactively optimize the use of their engines according to the upcoming activities and the operating environment. GHG emissions are constantly monitored and reductions have been recorded on a monthly basis. Novel/Additive Information Considering that the cleaner energy is the one that is not consumed, this digitalization process of rig sensor data and operation reporting offers an unprecedented vision of the activities and their related GHG emissions. A cautious analysis of these data provides practical indicators for the most efficient use of diesel generators. This proactive energy management supports operators and contractors in delivering a proactive sustainability strategy with measurable results.

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    influence
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Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
4
Top 10%
Average
Average
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