Knowledge and Data Driven Approaches for Hydrocarbon Microseepage Characterizations: An Application of Satellite Remote Sensing
(2020) In Master Thesis in Geographical Information Science GISM01 20202Dept of Physical Geography and Ecosystem Science
- Abstract
- Methane is a potent greenhouse gas that plays a major role in climate change. Significant uncertainty exists in the estimates of emissions from natural methane sources. This uncertainty in data is one of the primary scientific challenges in climate model. Geological seepage considered as the second largest natural source of methane after wetlands and today is recognized as a major contributor to atmospheric methane. This work focuses on positive methane fluxes to the atmosphere from sedimentary basins hosting natural gas and oil reservoirs termed as microseepage.
Microseepage contributes to the global atmospheric methane budget and creates large uncertainties in the global methane atmospheric budget estimates (sources and sinks). The... (More) - Methane is a potent greenhouse gas that plays a major role in climate change. Significant uncertainty exists in the estimates of emissions from natural methane sources. This uncertainty in data is one of the primary scientific challenges in climate model. Geological seepage considered as the second largest natural source of methane after wetlands and today is recognized as a major contributor to atmospheric methane. This work focuses on positive methane fluxes to the atmosphere from sedimentary basins hosting natural gas and oil reservoirs termed as microseepage.
Microseepage contributes to the global atmospheric methane budget and creates large uncertainties in the global methane atmospheric budget estimates (sources and sinks). The global coverage of methane microseepage is unknown, and data available today is based on estimates. With respect to global and regional estimates, the level of microseepage emission was established by assuming, a priori, that the full area of petroleum basins in dry climate produces positive fluxes of methane into the atmosphere. This assumption is subject to considerable uncertainties because microseepage does not occur throughout the entire petroleum field area. In this context, satellite remote sensing imagery was used to investigation areas affected by natural hydrocarbon microseepage using knowledge and data-driven based approaches.
Knowledge-based approaches constructed based on the theoretical model established from literature targeting specific minerals and surface manifestations. The specific mineral groups or features were determined based on their reflectance and absorption characteristics. Methods in knowledge-driven approach include Band Ratio (BR), Principle Component Analyses (PCA) with Crosta technique and Mixture Tuned Matching Filtering (MTMF) classification. The data-driven approach used Support Vector Machine (SVM) algorithm. The SVM model was purely estimated from the multispectral image data based on occurrence and abundance of oil and dry hole wells and without any prior assumption of area mineralogical assemblage. The mapped microseepage extent exhibit some level of consistency between all models.
Results were satisfactory enough judged by the level of consistency realized between all models for the mapped microseepage extent, which indicates statistical significance. The data-driven model yields best results, the gain in performance from using data-driven approach as compared to knowledge-based was relatively small. However, the long processing steps and time in the knowledge-based approaches gives merits to the data driven approach. The work demonstrated the potential of satellite remote sensing and its analysis in mapping hydrocarbons microseepage extent on regional or even global scale. (Less) - Popular Abstract
- Methane is a potent greenhouse gas that plays a major role in shaping the global climate. The total accumulation of methane in the atmosphere is the balance between sources that emit methane to the atmosphere and sinks that remove methane from atmosphere. Methane can be produced as a direct result of human activities and natural processes. Of the natural sources is the geological seepage which considered as the second largest natural source of methane after wetlands. It is estimated to contribute to about 54 million ton per year. Soil in drylands is known to withdraw methane from the atmosphere, however drylands which are part of petroleum basins may not consume methane but instead produce it. Hydrocarbon in subsurface and due to high... (More)
- Methane is a potent greenhouse gas that plays a major role in shaping the global climate. The total accumulation of methane in the atmosphere is the balance between sources that emit methane to the atmosphere and sinks that remove methane from atmosphere. Methane can be produced as a direct result of human activities and natural processes. Of the natural sources is the geological seepage which considered as the second largest natural source of methane after wetlands. It is estimated to contribute to about 54 million ton per year. Soil in drylands is known to withdraw methane from the atmosphere, however drylands which are part of petroleum basins may not consume methane but instead produce it. Hydrocarbon in subsurface and due to high pressure at depth and weakness areas in geological structure can escape to surface in the form of visible oil or invisible gases. The invisible gases are composed of methane as the most predominant gas seep and it is termed as microseepage. The relative contribution of methane from microseepage to the atmosphere is highly uncertain. This uncertainty in data is one of the primary scientific challenges in today climate model.
Uncertainties arise due to incomplete knowledge of the actual area of microseepage. Accurate and more reliable estimates can be obtained by performing as many as possible ground measurements from many different petroleum basins, an exercise that pose lots of challenges at regional or global scales. Motivated by the incomplete knowledge of the actual area of microseepage and therefore uncertainty over the magnitude of global methane source and sink. And as it is hard to measure every drylands, satellite remote sensing imagery was used to investigation areas affected by natural hydrocarbon microseepage. Mapping the boundary of a microseepage can then facilitates for more accurate predictions.
The gases that leaked out of petroleum traps and rose towards the surface and interacted with soil and rocks create alterations in surface. Possible alterations include development of different minerals, formation of carbonates, development of vegetation anomalies and surface temperature variations. Satellite sensors have the ability to see more than just red, green and blue and therefore can recognize a particular minerals, or mineral families. Through the recognition of mineralogical and biological changes in soils, sediments, rocks and vegetation using satellite remote sensing the boundary of microseepage was mapped. The methodologies used are suitable for the detection and visualization of overall spatial distribution of minerals surface anomalies. The work demonstrated the potential of satellite remote sensing and its analysis in mapping hydrocarbons microseepage extent on regional or even global scale. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9032408
- author
- Al Farid, Ali
- supervisor
-
- Jonas Ardö LU
- organization
- alternative title
- Indirect Mapping of Methane Seeps Using Satellite Remote Sensing
- course
- GISM01 20202
- year
- 2020
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- Geography, Geographical Information Systems, Hydrocarbon Microseepage, PCA, Crosta, Band Ratio, MTMF, Weight of Evidence, SVM, Methane Fluxes, Remote Sensing, Satellite Image Classification, Earth Observation, Mineral Classifications, Machine Learning
- publication/series
- Master Thesis in Geographical Information Science
- report number
- 122
- language
- English
- id
- 9032408
- date added to LUP
- 2020-11-29 18:09:49
- date last changed
- 2020-11-29 18:10:51
@misc{9032408,
abstract = {{Methane is a potent greenhouse gas that plays a major role in climate change. Significant uncertainty exists in the estimates of emissions from natural methane sources. This uncertainty in data is one of the primary scientific challenges in climate model. Geological seepage considered as the second largest natural source of methane after wetlands and today is recognized as a major contributor to atmospheric methane. This work focuses on positive methane fluxes to the atmosphere from sedimentary basins hosting natural gas and oil reservoirs termed as microseepage.
Microseepage contributes to the global atmospheric methane budget and creates large uncertainties in the global methane atmospheric budget estimates (sources and sinks). The global coverage of methane microseepage is unknown, and data available today is based on estimates. With respect to global and regional estimates, the level of microseepage emission was established by assuming, a priori, that the full area of petroleum basins in dry climate produces positive fluxes of methane into the atmosphere. This assumption is subject to considerable uncertainties because microseepage does not occur throughout the entire petroleum field area. In this context, satellite remote sensing imagery was used to investigation areas affected by natural hydrocarbon microseepage using knowledge and data-driven based approaches.
Knowledge-based approaches constructed based on the theoretical model established from literature targeting specific minerals and surface manifestations. The specific mineral groups or features were determined based on their reflectance and absorption characteristics. Methods in knowledge-driven approach include Band Ratio (BR), Principle Component Analyses (PCA) with Crosta technique and Mixture Tuned Matching Filtering (MTMF) classification. The data-driven approach used Support Vector Machine (SVM) algorithm. The SVM model was purely estimated from the multispectral image data based on occurrence and abundance of oil and dry hole wells and without any prior assumption of area mineralogical assemblage. The mapped microseepage extent exhibit some level of consistency between all models.
Results were satisfactory enough judged by the level of consistency realized between all models for the mapped microseepage extent, which indicates statistical significance. The data-driven model yields best results, the gain in performance from using data-driven approach as compared to knowledge-based was relatively small. However, the long processing steps and time in the knowledge-based approaches gives merits to the data driven approach. The work demonstrated the potential of satellite remote sensing and its analysis in mapping hydrocarbons microseepage extent on regional or even global scale.}},
author = {{Al Farid, Ali}},
language = {{eng}},
note = {{Student Paper}},
series = {{Master Thesis in Geographical Information Science}},
title = {{Knowledge and Data Driven Approaches for Hydrocarbon Microseepage Characterizations: An Application of Satellite Remote Sensing}},
year = {{2020}},
}