May 21, 2024


This put up was co-authored by Dan Russ, Affiliate Director, and Sacha Abinader, Managing Director from Accenture.

The 12 months 2022 was a notable one within the historical past of our local weather—it stood because the fifth warmest 12 months ever recorded1. A rise in excessive climate situations, from devastating droughts and wildfires to relentless floods and warmth waves, made their presence felt greater than ever earlier than—and 2023 appears poised to shatter nonetheless extra data. These unnerving circumstances reveal the ever-growing affect of local weather change that we’ve come to expertise because the planet continues to heat.

Microsoft’s sustainability journey

At Microsoft, our method to mitigating the local weather disaster is rooted in each addressing the sustainability of our personal operations and in empowering our prospects and companions of their journey to net-zero emissions. In 2020, Microsoft set out with a strong dedication: to be a carbon-negative, water constructive, and zero-waste firm, whereas defending ecosystems, all by the 12 months 2030. Three years later, Microsoft stays steadfast in its resolve. As a part of these efforts, Microsoft has launched Microsoft Cloud for Sustainability, a complete suite of enterprise-grade sustainability administration instruments geared toward supporting companies of their transition to net-zero.

Furthermore, our contribution to a number of international sustainability initiatives has the objective of benefiting each particular person and group on this planet. Microsoft has accelerated the provision of progressive local weather applied sciences by means of our Local weather Innovation Fund and is working onerous to strengthen our local weather coverage agenda. Microsoft’s give attention to sustainability-related efforts varieties the backdrop for the subject tackled on this weblog put up: our partnership with Accenture on the appliance of AI applied sciences towards fixing the difficult downside of methane emissions detection, quantification, and remediation within the power trade.

“We’re excited to accomplice with Accenture to ship methane emissions administration capabilities. This combines Accenture’s deep area information along with Microsoft’s cloud platform and experience in constructing AI options for trade issues. The result’s an answer that solves actual enterprise issues and that additionally makes a constructive local weather affect.”—Matt Kerner, CVP Microsoft Cloud for Trade, Microsoft.

Why is methane essential?

Methane is roughly 85 occasions stronger than carbon dioxide (CO2) at trapping warmth within the ambiance over a 20-year interval. It’s the second most ample anthropogenic greenhouse gasoline after CO2, accounting for about 20 p.c of worldwide emissions.

The worldwide oil and gasoline trade is without doubt one of the main sources of methane emissions. These emissions happen throughout your entire oil and gasoline worth chain, from manufacturing and processing to transmission, storage, and distribution. The Worldwide Power Company (IEA) estimates that it’s technically doable to keep away from round 75 p.c of as we speak’s methane emissions from international oil and gasoline operations. These statistics drive house the significance of addressing this crucial situation.

Microsoft’s funding in Undertaking Astra

Microsoft has signed on to the Undertaking Astra initiative—along with main power firms, public sector organizations, and educational establishments—in a coordinated effort to reveal a novel method to detecting and measuring methane emissions from oil and gasoline manufacturing websites.

Undertaking Astra entails an progressive sensor community that harnesses advances in methane-sensing applied sciences, knowledge sharing, and knowledge analytics to offer near-continuous emissions monitoring of methane throughout oil and gasoline amenities. As soon as operational, this type of good digital community would enable producers and regulators to pinpoint methane releases for well timed remediation.

Accenture and Microsoft—The way forward for methane administration

Attaining the objective of net-zero methane emissions is changing into more and more doable. The applied sciences wanted to mitigate emissions are maturing quickly, and digital platforms are being developed to combine complicated parts. As referenced in Accenture’s current methane thought management piece, “Greater than sizzling air with methane emissions”. What is required now could be a shift—from a reactive paradigm to a preventative one—the place the crucial situation of leak detection and remediation is remodeled into leak prevention by leveraging superior applied sciences.

Accenture’s particular capabilities and toolkit

Up to now, the power trade’s method to methane administration has been fragmented and comprised of a number of pricey monitoring instruments and gear which have been siloed throughout varied operational entities. These siloed options have made it tough for power firms to precisely analyze emissions knowledge, at scale, and remediate these issues shortly.

What has been missing is a single, reasonably priced platform that may combine these parts into an efficient methane emissions mitigation software. These parts embody enhanced detection and measurement capabilities, machine studying for higher decision-making, and modified working procedures and gear that make “net-zero methane” occur sooner. These platforms are being developed now and might accommodate all kinds of expertise options that may kind the digital core obligatory to realize a aggressive benefit.

Accenture has created a Methane Emissions Monitoring Platform (MEMP) that facilitates the combination of a number of knowledge streams and embeds key methane insights into enterprise operations to drive motion (see Determine 1 beneath).

Figure 1 shows Accenture’s Methane Emissions Monitoring Platform (MEMP).

Determine 1: Accenture’s Methane Emissions Monitoring Platform (MEMP).

The cloud-based platform, which runs on Microsoft Azure, permits power firms to each measure baseline methane emissions in close to real-time and detect leaks utilizing satellites, fastened wing plane, and floor stage sensing applied sciences. It’s designed to combine a number of knowledge sources to optimize venting, flaring, and fugitive emissions. Determine 2 beneath illustrates the aspirational end-to-end course of incorporating Microsoft applied sciences. MEMP additionally facilitates connectivity with back-end programs liable for work order creation and administration, together with the scheduling and dispatching of area crews to remediate particular emission occasions.

Figure 2: The Methane Emissions Monitoring Platform Workflow (aspirational)

Determine 2: The Methane Emissions Monitoring Platform Workflow (aspirational).

Microsoft’s AI instruments powering Accenture’s Methane Emissions Monitoring Platform

Microsoft has offered numerous Azure-based AI instruments for tackling methane emissions, together with instruments that help sensor placement optimization, digital twin for methane Web of Issues (IoT) sensors, anomaly (leak) detection, and emission supply attribution and quantification. These instruments, when built-in with Accenture’s MEMP, enable customers to observe alerts in close to real-time by means of a user-friendly interface, as proven in Determine three.

Figure 3:  MEMP Landing Page visualizing wells, IoT sensors, and Work Orders

Determine three: MEMP Touchdown Web page visualizing wells, IoT sensors, and Work Orders.

“Microsoft has developed differentiated AI capabilities for methane leak detection and remediation, and is worked up to accomplice with Accenture in integrating these options onto their Methane Emissions Monitoring Platform, to ship worth to power firms by empowering them of their path to net-zero emissions”—Merav Davidson, VP, Trade AI, Microsoft.

Methane IoT sensor placement optimization

Inserting sensors in strategic places to make sure most potential protection of the sphere and well timed detection of methane leaks is step one in direction of constructing a dependable end-to-end IoT-based detection and quantification answer. Microsoft’s answer for sensor placement makes use of geospatial, meteorological, and historic leak charge knowledge and an atmospheric dispersion mannequin to mannequin methane plumes from sources throughout the space of curiosity and procure a consolidated view of emissions. It then selects one of the best places for sensors utilizing both a mathematical programming optimization technique, a grasping approximation technique, or an empirical downwind technique that considers the dominant wind path, topic to value constraints.

As well as, Microsoft supplies a validation module to guage the efficiency of any candidate sensor placement technique. Operators can consider the marginal beneficial properties provided by using extra sensors within the community, by means of sensitivity evaluation as proven in Determine four beneath.

Figure 4: Left: Increase in leak coverage with number of sensors. By increasing the number of sensors that are available for deployment, the leak detection ratio (i.e., the fraction of detected leaks by deployed sensors) increases. Right: Source coverage for 15 sensors. The arrows map each sensor (red circles) to the sources (black triangles) that it detects.

Determine four: Left: Improve in leak protection with numerous sensors. By growing the variety of sensors which can be out there for deployment, the leak detection ratio (i.e., the fraction of detected leaks by deployed sensors) will increase. Proper: Supply protection for 15 sensors. The arrows map every sensor (purple circles) to the sources (black triangles) that it detects.

Finish-to-end knowledge pipeline for methane IoT sensors

To attain steady monitoring of methane emissions from oil and gasoline property, Microsoft has applied an end-to-end answer pipeline the place streaming knowledge from IoT Hub is ingested right into a Bronze Delta Lake desk leveraging Structured Streaming on Spark. Sensor knowledge cleansing, aggregation, and transformation to algorithm knowledge mannequin are completed and the resultant knowledge is saved in a Silver Delta Lake desk in a format that’s optimized for downstream AI duties.

Methane leak detection is carried out utilizing uni- and multi-variate anomaly detection fashions for improved reliability. As soon as a leak has been detected, its severity can also be computed, and the emission supply attribution and quantification algorithm then identifies the doubtless supply of the leak and quantifies the leak charge.

This occasion data is distributed to the Accenture Work Order Prioritization module to set off acceptable alerts based mostly on the severity of the leak to allow well timed remediation of fugitive or venting emissions. The quantified leaks may also be recorded and reported utilizing instruments such because the Microsoft Sustainability Supervisor app. The person parts of this end-to-end pipeline are described within the sections beneath and illustrated in Determine 5.

Figure 5: End-to-end IoT data pipeline that runs on Microsoft Azure demonstrating methane leak detection, quantification and remediation capabilities.

Determine 5: Finish-to-end IoT knowledge pipeline that runs on Microsoft Azure demonstrating methane leak detection, quantification, and remediation capabilities.

Digital twin for methane IoT sensors

Information streaming from IoT sensors deployed within the area must be orchestrated and reliably handed to the processing and AI execution pipeline. Microsoft’s answer creates a digital twin for each sensor. The digital twin contains a sensor simulation module that’s leveraged in numerous phases of the methane answer pipeline. The simulator is used to check the end-to-end pipeline earlier than area deployment, reconstruct and analyze anomalous occasions by means of what-if situations and allow the supply attribution and leak quantification module by means of a simulation-based, inverse modeling method.

Anomaly (leak) detection

A methane leak at a supply may manifest as an uncommon rise within the methane focus detected at close by sensor places that require well timed mitigation. Step one in direction of figuring out such an occasion is to set off an alert by means of the anomaly detection system. A severity rating is computed for every anomaly to assist prioritize alerts. Microsoft supplies the next two strategies for time sequence anomaly detection, leveraging Microsoft’s open-source SynapseML library, which is constructed on the Apache Spark distributed computing framework and simplifies the creation of massively scalable machine studying pipelines:

  1. Univariate anomaly detection: Primarily based on a single variable, for instance, methane focus.
  2. Multivariate anomaly detection: Utilized in situations the place a number of variables, together with methane focus, wind velocity, wind path, temperature, relative humidity, and atmospheric strain, are used to detect an anomaly.

Submit-processing steps are applied to reliably flag true anomalous occasions in order that remedial actions might be taken in a well timed method whereas lowering false positives to keep away from pointless and costly area journeys for personnel. Determine 6 beneath illustrates this characteristic in Accenture’s MEMP: the ‘hover field” over Sensor 6 paperwork a complete of seven alerts leading to simply two work orders being created.

Figure 6: MEMP dashboard visualizing alerts and resulting work orders for Sensor 6.

Determine 6: MEMP dashboard visualizing alerts and ensuing work orders for Sensor 6.

Emission supply attribution and quantification

As soon as deployed within the area, methane IoT sensors can solely measure compound alerts within the proximity of their location. For an space of curiosity that’s densely populated with potential emission sources, the problem is to determine the supply(s) of the emission occasion. Microsoft supplies two approaches for figuring out the supply of a leak:

  1. Space of affect attribution mannequin: Given the sensor measurements and site, an “space of affect” is computed for a sensor location at which a leak is detected, based mostly on the real-time wind path and asset geo-location. Then, the asset(s) that lie throughout the computed “space of affect” are recognized as potential emissions sources for that flagged leak.
  2. Bayesian attribution mannequin: With this method, supply attribution is achieved by means of inversion of the methane dispersion mannequin. The Bayesian method contains two important parts—a supply leak quantification mannequin and a probabilistic rating mannequin—and might account for uncertainties within the knowledge stemming from measurement noise, statistical and systematic errors, and supplies the more than likely sources for a detected leak, the related confidence stage and leak charge magnitude.

Contemplating the excessive variety of sources, low variety of sensors, and the variability of the climate, this poses a posh however extremely useful inverse modeling downside to resolve. Determine 7 supplies perception concerning leaks and work orders for a selected properly (Properly 24). Particularly, diagrams present well-centric and sensor-centric assessments that attribute a leak to this properly.

Figure 7: Leak Source Attribution for Well 24

Determine 7: Leak Supply Attribution for Properly 24.

Additional, Accenture’s Work Order Prioritization module utilizing Microsoft Dynamics 365 Area Service utility (Determine eight) permits Power operators to provoke remediation measures underneath the Leak Detection and Remediation (LDAR) paradigm.

Figure 8: Dynamics D365 Work Order with emission source attribution and CH4 concentration trend data embedded.

Determine eight: Dynamics 365 Work Order with emission supply attribution and CHfour focus development knowledge embedded.

Trying forward

In partnership with Microsoft, Accenture is seeking to proceed refining MEMP, which is constructed on the superior AI and statistical fashions introduced on this weblog. Future capabilities of MEMP look to maneuver from “detection and remediation” to “prediction and prevention” of emission occasions, together with enhanced occasion quantification and supply attribution.

Microsoft and Accenture will proceed to spend money on superior capabilities with a watch towards each:

  1. Integrating trade requirements platforms comparable to Azure Information Supervisor for Power (ADME) and Open Footprint Discussion board to allow each publishing and consumption of emissions knowledge.
  2. Leveraging Generative AI to simplify the consumer expertise.

Be taught extra

Case examine

Duke Power is working with Accenture and Microsoft on the event of a brand new expertise platform designed to measure precise baseline methane emissions from pure gasoline distribution programs.

Accenture Methane Emissions Monitoring Platform

Extra data concerning Accenture’s MEMP might be present in “Greater than sizzling air with methane emissions”. Further data concerning Accenture might be discovered on the Accenture homepage and on their power web page.

Microsoft Azure Information Supervisor for Power

Azure Information Supervisor for Power is an enterprise-grade, absolutely managed, OSDU Information Platform for the power trade that’s environment friendly, standardized, straightforward to deploy, and scalable for knowledge administration—ingesting, aggregating, storing, looking, and retrieving knowledge. The platform will present the dimensions, safety, privateness, and compliance anticipated by our enterprise prospects. The platform gives out-of-the-box compatibility with main service firm purposes, which permits geoscientists to make use of domain-specific purposes on knowledge contained in Azure Information Supervisor for Power with ease.

Associated publications and convention shows

Supply Attribution and Emissions Quantification for Methane Leak Detection: A Non-Linear Bayesian Regression Method. Mirco Milletari, Sara Malvar, Yagna Oruganti, Leonardo Nunes, Yazeed Alaudah, Anirudh Badam. The eightth Worldwide On-line & Onsite Convention on Machine Studying, Optimization, and Information Science.

Surrogate Modeling for Methane Dispersion Simulations Utilizing Fourier Neural Operator. Qie Zhang, Mirco Milletari, Yagna Oruganti, Philipp Witte. Offered on the NeurIPS 2022 Workshop on Tackling Local weather Change with Machine Studying.



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