Satellite based methane emission estimation for flaring activities in oil and gas industry: A data-driven approach(SMEEF-OGI)

Klimaendringer, delvis utløst av klimagassutslipp, utgjør en kritisk global utfordring. Metan, en svært potent drivhusgass med et globalt oppvarmings potensial på 80 ganger karbondioksid, er en betydelig bidragsyter til denne krisen. Kilder til metanutslipp inkluderer olje- og gassindustrien, landbruket og avfallshåndteringen, med fakling i olje- og gassindustrien som en betydelig utslippskilde. Fakling, en standard prosess i olje- og gassindustrien, antas ofte å være 98 % effektiv ved omdannelse av metan til mindre skadelig karbondioksid. Nyere forskning fra University of Michigan, Stanford, Environmental Defense Fund og Scientific Aviation indikerer imidlertid at den allment aksepterte effektiviteten på 98 % av fakling ved konvertering av metan til karbondioksid, en mindre skadelig klimagass, kan være unøyaktig. Denne undersøkelsen revurderer fakkelprosessens effektivitet og dens rolle i metankonvertering. Dette arbeidet fokuserer på å lage en metode for uavhengig å beregne metanutslipp fra olje- og gassvirksomhet for å løse dette problemet. Satellittdata, som er et nyttig verktøy for å beregne klimagassutslipp fra ulike kilder, er inkludert i den foreslåtte metodikken. I tillegg til standard overvåkingsteknikker, tilbyr satellittdata en uavhengig, ikke-påtrengende, rimelig og kontinuerlig overvåkingstilnærming. På bakgrunn av dette er problemstillingen for dette arbeidet følgende "Hvordan kan en datadrevet tilnærming utvikles for å forbedre nøyaktigheten og kvaliteten på estimering av metanutslipp fra faklingsaktiviteter i olje- og gassindustrien, ved å bruke satellittdata fra utvalgte plattformer for å oppdage og kvantifisere fremtidige utslipp basert på maskinlæring mer effektivt?" For å oppnå dette ble følgende mål og aktiviteter utført. * Teoretisk rammeverk og sentrale begreper * Teknisk gjennomgang av dagens toppmoderne satellittplattformer og eksisterende litteratur. * Utvikling av et Proof of Concept * Foreslå en evaluering av metoden * Anbefalinger og videre arbeid Dette arbeidet har tatt i bruk en systematisk tilnærming, som starter med et omfattende teoretisk rammeverk for å forstå bruken av fakling, de miljømessige implikasjonene av metan, den nåværende «state-of-the-art» av forskning, og «state-of-the-art» i felt for fjernmåling via satellitter. Basert på rammeverket utviklet i de innledende fasene av dette arbeidet, ble det formulert en datadrevet metodikk, som benytter VIIRS-datasettet for å få geografiske områder av interesse. Hyperspektrale data og metandata ble samlet fra Sentinel-2 og Sentinel-5P satellittdatasettet. Denne informasjonen ble behandlet via en foreslått rørledning, med innledende justering og forbedring. I dette arbeidet ble bildene forbedret ved å beregne den normaliserte brennindeksen. Resultatet var et datasett som inneholdt plasseringen av kjente fakkelsteder, med data fra både Sentinel-2 og Sentinel-5P-satellitten. Resultatene understreker forskjellene i dekningen mellom Sentinel-2- og Sentinel-5P-data, en faktor som potensielt kan påvirke nøyaktigheten av metanutslippsestimater. De anvendte forbehandlingsteknikkene forbedret dataklarheten og brukervennligheten markant, men deres effektivitet kan avhenge av fakkelstedenes spesifikke egenskaper og rådatakvaliteten. Dessuten, til tross for visse begrensninger, ga kombinasjonen av Sentinel-2 og Sentinel-5P-data effektivt et omfattende datasett egnet for videre analyse. Avslutningsvis introduserer dette prosjektet en oppmuntrende metodikk for å estimere metanutslipp fra fakling i olje- og gassindustrien. Den legger et grunnleggende springbrett for fremtidig forskning, og forbedrer kontinuerlig presisjonen og kvaliteten på data for å bekjempe klimaendringer. Denne metodikken kan sees i flytskjemaet nedenfor. Basert på arbeidet som er gjort i dette prosjektet, kan fremtidig arbeid fokusere på å innlemme alternative kilder til metan data, utvide interesseområdene gjennom industrisamarbeid og forsøke å trekke ut ytterligere detaljer gjennom bildesegmenteringsmetoder. Dette prosjektet legger et grunnlag, og baner vei for påfølgende utforskninger å bygge videre på.Climate change, precipitated in part by greenhouse gas emissions, presents a critical global challenge. Methane, a highly potent greenhouse gas with a global warming potential of 80 times that of carbon dioxide, is a significant contributor to this crisis. Sources of methane emissions include the oil and gas industry, agriculture, and waste management, with flaring in the oil and gas industry constituting a significant emission source. Flaring, a standard process in the Oil and gas industry is often assumed to be 98% efficient when converting methane to less harmful carbon dioxide. However, recent research from the University of Michigan, Stanford, the Environmental Defense Fund, and Scientific Aviation indicates that the widely accepted 98% efficiency of flaring in converting methane to carbon dioxide, a less harmful greenhouse gas, may be inaccurate. This investigation reevaluates the flaring process's efficiency and its role in methane conversion. This work focuses on creating a method to independently calculate methane emissions from oil and gas activities to solve this issue. Satellite data, which is a helpful tool for calculating greenhouse gas emissions from various sources, is included in the suggested methodology. In addition to standard monitoring techniques, satellite data offers an independent, non-intrusive, affordable, and continuous monitoring approach. Based on this, the problem statement for this work is the following “How can a data-driven approach be developed to enhance the accuracy and quality of methane emission estimation from flaring activities in the Oil and Gas industry, using satellite data from selected platforms to detect and quantify future emissions based on Machine learning more effectively?" To achieve this, the following objectives and activities were performed. * Theoretical Framework and key concepts * Technical review of the current state-of-the-art satellite platforms and existing literature. * Development of a Proof of Concept * Proposing an evaluation of the method * Recommendations and further work This work has adopted a systematic approach, starting with a comprehensive theoretical framework to understand the utilization of flaring, the environmental implications of methane, the current state-of-the-art of research, and the state-of-the-art in the field of remote sensing via satellites. Based upon the framework developed during the initial phases of this work, a data-driven methodology was formulated, utilizing the VIIRS dataset to get geographical areas of interest. Hyperspectral and methane data were aggregated from the Sentinel-2 and Sentinel-5P satellite dataset. This information was processed via a proposed pipeline, with initial alignment and enhancement. In this work, the images were enhanced by calculating the Normalized Burn Index. The result was a dataset containing the location of known flare sites, with data from both the Sentinel-2, and the Sentinel-5P satellite. The results underscore the disparities in coverage between Sentinel-2 and Sentinel-5P data, a factor that could potentially influence the precision of methane emission estimates. The applied preprocessing techniques markedly enhanced data clarity and usability, but their efficacy may hinge on the flaring sites' specific characteristics and the raw data quality. Moreover, despite certain limitations, the combination of Sentinel-2 and Sentinel-5P data effectively yielded a comprehensive dataset suitable for further analysis. In conclusion, this project introduces an encouraging methodology for estimating methane emissions from flaring activities within the oil and gas industry. It lays a foundational steppingstone for future research, continually enhancing the precision and quality of data in combating climate change. This methodology can be seen in the flow chart below. Based on the work done in this project, future work could focus on incorporating alternative sources of methane data, broadening the areas of interest through industry collaboration, and attempting to extract further features through image segmentation methods. This project signifies a start, paving the way for subsequent explorations to build upon. Climate change, precipitated in part by greenhouse gas emissions, presents a critical global challenge. Methane, a highly potent greenhouse gas with a global warming potential of 80 times that of carbon dioxide, is a significant contributor to this crisis. Sources of methane emissions include the oil and gas industry, agriculture, and waste management, with flaring in the oil and gas industry constituting a significant emission source. Flaring, a standard process in the Oil and gas industry is often assumed to be 98% efficient when converting methane to less harmful carbon dioxide. However, recent research from the University of Michigan, Stanford, the Environmental Defense Fund, and Scientific Aviation indicates that the widely accepted 98% efficiency of flaring in converting methane to carbon dioxide, a less harmful greenhouse gas, may be inaccurate. This investigation reevaluates the flaring process's efficiency and its role in methane conversion. This work focuses on creating a method to independently calculate methane emissions from oil and gas activities to solve this issue. Satellite data, which is a helpful tool for calculating greenhouse gas emissions from various sources, is included in the suggested methodology. In addition to standard monitoring techniques, satellite data offers an independent, non-intrusive, affordable, and continuous monitoring approach. Based on this, the problem statement for this work is the following “How can a data-driven approach be developed to enhance the accuracy and quality of methane emission estimation from flaring activities in the Oil and Gas industry, using satellite data from selected platforms to detect and quantify future emissions based on Machine learning more effectively?" To achieve this, the following objectives and activities were performed. * Theoretical Framework and key concepts * Technical review of the current state-of-the-art satellite platforms and existing literature. * Development of a Proof of Concept * Proposing an evaluation of the method * Recommendations and further work This work has adopted a systematic approach, starting with a comprehensive theoretical framework to understand the utilization of flaring, the environmental implications of methane, the current state-of-the-art of research, and the state-of-the-art in the field of remote sensing via satellites. Based upon the framework developed during the initial phases of this work, a data-driven methodology was formulated, utilizing the VIIRS dataset to get geographical areas of interest. Hyperspectral and methane data were aggregated from the Sentinel-2 and Sentinel-5P satellite dataset. This information was processed via a proposed pipeline, with initial alignment and enhancement. In this work, the images were enhanced by calculating the Normalized Burn Index. The result was a dataset containing the location of known flare sites, with data from both the Sentinel-2, and the Sentinel-5P satellite. The results underscore the disparities in coverage between Sentinel-2 and Sentinel-5P data, a factor that could potentially influence the precision of methane emission estimates. The applied preprocessing techniques markedly enhanced data clarity and usability, but their efficacy may hinge on the flaring sites' specific characteristics and the raw data quality. Moreover, despite certain limitations, the combination of Sentinel-2 and Sentinel-5P data effectively yielded a comprehensive dataset suitable for further analysis. In conclusion, this project introduces an encouraging methodology for estimating methane emissions from flaring activities within the oil and gas industry. It lays a foundational steppingstone for future research, continually enhancing the precision and quality of data in combating climate change. This methodology can be seen in the flow chart below. Based on the work done in this project, future work could focus on incorporating alternative sources of methane data, broadening the areas of interest through industry collaboration, and attempting to extract further features through image segmentation methods. This project signifies a start, paving the way for subsequent explorations to build upon

Symposium franco-chinois de télédétection quantitative en agronomie et environnement. Bilan et perspectives de collaboration. Rapport de mission (26 au 30 mars 2000)

Ce rapport présente les principaux résultats d'un Symposium en Télédétection entre des équipes de chercheurs de l'INRA, du CIRAD, de l'Université de Lille et leurs homologues chinois de l'Institute of Remote Sensing Applications (IRSA) of Chinese Academy of Sciences (CAS), et du National Satellite Meteorological Center (NSMC). Les perspectives d'un programme de collaboration sont présentées avec deux axes majeurs correspondant à deux niveaux d'approche, régional et local en agriculture de précision. (Résumé d'auteur

A Feedforward Neural Network Approach for the Detection of Optically Thin Cirrus From IASI-NG

The identification of optically thin cirrus is crucial for their accurate parameterization in climate and Earth's system models. This study exploits the characteristics of the infrared atmospheric sounding interferometer-new generation (IASI-NG) to develop an algorithm for the detection of optically thin cirrus. IASI-NG has been designed for the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) polar system second-generation program to continue the service of its predecessor IASI from 2024 onward. A thin-cirrus detection algorithm (TCDA) is presented here, as developed for IASI-NG, but also in parallel for IASI to evaluate its performance on currently available real observations. TCDA uses a feedforward neural network (NN) approach to detect thin cirrus eventually misidentified as clear sky by a previously applied cloud detection algorithm. TCDA also estimates the uncertainty of "clear-sky" or "thin-cirrus" detection. NN is trained and tested on a dataset of IASI-NG (or IASI) simulations obtained by processing ECMWF 5-generation reanalysis (ERA5) data with the s-IASI radiative transfer model. TCDA validation against an independent simulated dataset provides a quantitative statistical assessment of the improvements brought by IASI-NG with respect to IASI. In fact, IASI-NG TCDA outperforms IASI TCDA by 3% in probability of detection (POD), 1% in bias, and 2% in accuracy, and the false alarm ratio (FAR) passes from 0.02 to 0.01. Moreover, IASI TCDA validation against state-of-the-art cloud products from Cloudsat/CPR and CALIPSO/Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) real observations reveals a tendency for IASI TCDA to underestimate the presence of thin cirrus (POD = 0.47) but with a low FAR (0.07), which drops to 0.0 for very thin cirrus

Innovative Techniques for the Retrieval of Earth’s Surface and Atmosphere Geophysical Parameters: Spaceborne Infrared/Microwave Combined Analyses

With the advent of the first satellites for Earth Observation: Landsat-1 in July 1972 and ERS-1 in May 1991, the discipline of environmental remote sensing has become, over time, increasingly fundamental for the study of phenomena characterizing the planet Earth. The goal of environmental remote sensing is to perform detailed analyses and to monitor the temporal evolution of different physical phenomena, exploiting the mechanisms of interaction between the objects that are present in an observed scene and the electromagnetic radiation detected by sensors, placed at a distance from the scene, operating at different frequencies. The analyzed physical phenomena are those related to climate change, weather forecasts, global ocean circulation, greenhouse gas profiling, earthquakes, volcanic eruptions, soil subsidence, and the effects of rapid urbanization processes. Generally, remote sensing sensors are of two primary types: active and passive. Active sensors use their own source of electromagnetic radiation to illuminate and analyze an area of interest. An active sensor emits radiation in the direction of the area to be investigated and then detects and measures the radiation that is backscattered from the objects contained in that area. Passive sensors, on the other hand, detect natural electromagnetic radiation (e.g., from the Sun in the visible band and the Earth in the infrared and microwave bands) emitted or reflected by the object contained in the observed scene. The scientific community has dedicated many resources to developing techniques to estimate, study and analyze Earth’s geophysical parameters. These techniques differ for active and passive sensors because they depend strictly on the type of the measured physical quantity. In my P.h.D. work, inversion techniques for estimating Earth’s surface and atmosphere geophysical parameters will be addressed, emphasizing methods based on machine learning (ML). In particular, the study of cloud microphysics and the characterization of Earth’s surface changes phenomenon are the critical points of this work

Water Quality Observations from Space: A Review of Critical Issues and Challenges

Water is the basis of all life on this planet. Yet, approximately one in seven people in the world do not have access to safe water. Water can become unsafe due to contamination by various organic and inorganic compounds due to various natural and anthropogenic processes. Identifying and monitoring water quality changes in space and time remains a challenge, especially when contamination events occur over large geographic areas. This study investigates recent advances in remote sensing that allow us to detect and monitor the unique spectral characteristics of water quality events over large areas. Based on an extensive literature review, we focus on three critical water quality problems as part of this study: algal blooms, acid mine drainage, and suspended solids. We review the advances made in applications of remote sensing in each of these issues, identify the knowledge gaps and limitations of current studies, analyze the existing approaches in the context of global environmental changes, and discuss potential ways to combine multi-sensor methods and different wavelengths to develop improved approaches. Synthesizing the findings of these studies in the context of the three specific tracks will help stakeholders to utilize, share, and embed satellite-derived earth observations for monitoring and tracking the ever-evolving water quality in the earth’s limited freshwater reserves

The future of Earth observation in hydrology

In just the past 5 years, the field of Earth observation has progressed beyond the offerings of conventional space-agency-based platforms to include a plethora of sensing opportunities afforded by CubeSats, unmanned aerial vehicles (UAVs), and smartphone technologies that are being embraced by both for-profit companies and individual researchers. Over the previous decades, space agency efforts have brought forth well-known and immensely useful satellites such as the Landsat series and the Gravity Research and Climate Experiment (GRACE) system, with costs typically of the order of 1 billion dollars per satellite and with concept-to-launch timelines of the order of 2 decades (for new missions). More recently, the proliferation of smart-phones has helped to miniaturize sensors and energy requirements, facilitating advances in the use of CubeSats that can be launched by the dozens, while providing ultra-high (3-5 m) resolution sensing of the Earth on a daily basis. Start-up companies that did not exist a decade ago now operate more satellites in orbit than any space agency, and at costs that are a mere fraction of traditional satellite missions. With these advances come new space-borne measurements, such as real-time high-definition video for tracking air pollution, storm-cell development, flood propagation, precipitation monitoring, or even for constructing digital surfaces using structure-from-motion techniques. Closer to the surface, measurements from small unmanned drones and tethered balloons have mapped snow depths, floods, and estimated evaporation at sub-metre resolutions, pushing back on spatio-temporal constraints and delivering new process insights. At ground level, precipitation has been measured using signal attenuation between antennae mounted on cell phone towers, while the proliferation of mobile devices has enabled citizen scientists to catalogue photos of environmental conditions, estimate daily average temperatures from battery state, and sense other hydrologically important variables such as channel depths using commercially available wireless devices. Global internet access is being pursued via high-altitude balloons, solar planes, and hundreds of planned satellite launches, providing a means to exploit the "internet of things" as an entirely new measurement domain. Such global access will enable real-time collection of data from billions of smartphones or from remote research platforms. This future will produce petabytes of data that can only be accessed via cloud storage and will require new analytical approaches to interpret. The extent to which today's hydrologic models can usefully ingest such massive data volumes is unclear. Nor is it clear whether this deluge of data will be usefully exploited, either because the measurements are superfluous, inconsistent, not accurate enough, or simply because we lack the capacity to process and analyse them. What is apparent is that the tools and techniques afforded by this array of novel and game-changing sensing platforms present our community with a unique opportunity to develop new insights that advance fundamental aspects of the hydrological sciences. To accomplish this will require more than just an application of the technology: in some cases, it will demand a radical rethink on how we utilize and exploit these new observing systems

Deep learning in remote sensing: a review

Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a 'black-box' solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin

Potential of using remote sensing techniques for global assessment of water footprint of crops

Remote sensing has long been a useful tool in global applications, since it provides physically-based, worldwide, and consistent spatial information. This paper discusses the potential of using these techniques in the research field of water management, particularly for ‘Water Footprint’ (WF) studies. The WF of a crop is defined as the volume of water consumed for its production, where green and blue WF stand for rain and irrigation water usage, respectively. In this paper evapotranspiration, precipitation, water storage, runoff and land use are identified as key variables to potentially be estimated by remote sensing and used for WF assessment. A mass water balance is proposed to calculate the volume of irrigation applied, and green and blue WF are obtained from the green and blue evapotranspiration components. The source of remote sensing data is described and a simplified example is included, which uses evapotranspiration estimates from the geostationary satellite Meteosat 9 and precipitation estimates obtained with the Climatic Prediction Center Morphing Technique (CMORPH). The combination of data in this approach brings several limitations with respect to discrepancies in spatial and temporal resolution and data availability, which are discussed in detail. This work provides new tools for global WF assessment and represents an innovative approach to global irrigation mapping, enabling the estimation of green and blue water use