121 research outputs found
Quantifying Oil and Gas Industry Related Geohazard Using Radar Interferometry and Hydro-geomechanical Modeling
The Permian Basin, containing a large amount of oil and gas, has been intensively developed for hydrocarbon production. However, the hazards related to the oil and gas industry including surface deformation and the underlying mechanisms in this region have not been well known. My PhD study aims to monitor the geohazards in the Permian Basin and better comprehend the subsurface mechanisms with the aid of high-resolution and high-accuracy Interferometric Synthetic Aperture Radar (InSAR) images. Generally, as the pore pressure is influenced by wastewater injection/hydrocarbon production, the pressure changes can propagate to other surrounding underground and overlying rock/soil layers, resulting in surface deformation. The distribution and temporal development of the surface deformation can be obtained from InSAR processing and analysis. To reveal the underground geo-mechanical process responsible for the development of the surface deformation, numerical modeling based on poroelasticity is then applied to estimate the effective parameters (i.e., parameters inferred from the simulation) including depth and volume. This method is applied to three cases in West Texas. At a site in Reeves county, InSAR detects surface uplift up to 17 cm near a wastewater disposal well from 2007 to 2011. Results from both elastic and poroelastic models indicate that the effective injection depth is much shallower than reported. The most reasonable explanation is that the well was experiencing leakage due to casing failures and/or sealing problem(s). At a site in Winkler county, surface uplift and the follow-on recovery detected by InSAR from 2015 to 2020 can be attributed to nearby wastewater disposal. Bayesian inversion with the poroelastic models provides estimates of the local hydro-geomechanical parameters. The posterior distribution of subsurface effective volumes reveals under-reported volumes in the well near the deformation center. We also investigate a case of aseismic slip related to oil and gas activities. The combination of InSAR observation and poroelastic finite element models in three cases shows the capability to investigate the ongoing geohazards related to fluid injection and hydrocarbon production in the Permian Basin. This kind of study will be helpful to the decision-making of federal/local authorities to avoid future geohazards related to oil and gas activities
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
Characterizing slope instability kinematics by integrating multi-sensor satellite remote sensing observations
Over the past few decades, the occurrence and intensity of geological hazards, such as landslides, have substantially risen due to various factors, including global climate change, seismic events, rapid urbanization and other anthropogenic activities. Landslide disasters pose a significant risk in both urban and rural areas, resulting in fatalities, infrastructure damages, and economic losses. Nevertheless, conventional ground-based monitoring techniques are often costly, time-consuming, and require considerable resources. Moreover, some landslide incidents occur in remote or hazardous locations, making ground-based observation and field investigation challenging or even impossible.
Fortunately, the advancements in spaceborne remote sensing technology have led to the availability of large-scale and high-quality imagery, which can be utilized for various landslide-related applications, including identification, monitoring, analysis, and prediction. This efficient and cost-effective technology allows for remote monitoring and assessment of landslide risks and can significantly contribute to disaster management and mitigation efforts. Consequently, spaceborne remote sensing techniques have become vital for geohazard management in many countries, benefiting society by providing reliable downstream services. However, substantial effort is required to ensure that such benefits are provided.
For establishing long-term data archives and reliable analyses, it is essential to maintain consistent and continued use of multi-sensor spaceborne remote sensing techniques. This will enable a more thorough understanding of the physical mechanisms responsible for slope instabilities, leading to better decision-making and development of effective mitigation strategies. Ultimately, this can reduce the impact of landslide hazards on the general public. The present dissertation contributes to this effort from the following perspectives:
1. To obtain a comprehensive understanding of spaceborne remote sensing techniques for landslide monitoring, we integrated multi-sensor methods to monitor the entire life cycle of landslide dynamics. We aimed to comprehend the landslide evolution under complex cascading events by utilizing various spaceborne remote sensing techniques, e.g., the precursory deformation before catastrophic failure, co-failure procedures, and post-failure evolution of slope instability.
2. To address the discrepancies between spaceborne optical and radar imagery, we present a methodology that models four-dimensional (4D) post-failure landslide kinematics using a decaying mathematical model. This approach enables us to represent the stress relaxation for the landslide body dynamics after failure. By employing this methodology, we can overcome the weaknesses of the individual sensor in spaceborne optical and radar imaging.
3. We assessed the effectiveness of a newly designed small dihedral corner reflector for landslide monitoring. The reflector is compatible with both ascending and descending satellite orbits, while it is also suitable for applications with both high-resolution and medium-resolution satellite imagery. Furthermore, although its echoes are not as strong as those of conventional reflectors, the cost of the newly designed reflectors is reduced, with more manageable installation and maintenance. To overcome this limitation, we propose a specific selection strategy based on a probability model to identify the reflectors in satellite images
Integrated Exploration, Geothermal Modelling and Techno-Economic Resource Assessment of the Crystalline Basement in the Northern Upper Rhine Graben
The climate crisis is already causing significant humanitarian and economic impacts that will intensify in the future if global greenhouse gas emissions are not immediately reduced. Under the Climate Protection Act, Germany is therefore obliged to achieve net carbon neutrality by 2045. To meet this ambitious target, a far-reaching transformation of the energy sector is necessary, with imports of fossil fuels being replaced by domestic renewable energy production. In addition to established energy sources, deep geothermal energy, as a low-emission, base-load capable, local and scalable solution, will likely become a cornerstone of energy supply in the upcoming decades. The crystalline basement offers the greatest geothermal potential, which could be exploited through so-called enhanced geothermal systems (EGS). Particularly favourable conditions for geothermal utilization exist in the Upper Rhine Graben (URG), where compared to other regions in Germany higher reservoir temperatures and permeabilities are observed. To date, however, deep geothermal energy occupies only a small niche due to the comparatively high costs and risks associated with drilling, development, and operation of geothermal power plants. In addition, geological uncertainties in the basement are particularly large, as it has been insufficiently explored by the hydrocarbon industry and previous geothermal research projects. This thesis aims to quantify and reduce these uncertainties to promote geothermal development in the northern URG. A comprehensive lithological, petrophysical and structural reservoir characterization is carried out by combining geological and geophysical techniques on multiple scales. All relevant data are integrated into a 3D geothermal model that enables a regional resource assessment for the basement.
In the northern ORG, geologic modelling of the basement faces significant challenges because well data from the basement are very sparse and 3D seismic data are often not openly available. Therefore, gravity and magnetic data were additionally considered in a stochastic joint inversion that provided new insights into the structure and composition of the basement while also quantifying model uncertainties. The inversion demonstrates that the geologic units of the graben shoulders can be traced below the sedimentary filling. Comparison of the inverted petrophysical properties with existing databases and newly collected susceptibility measurements yielded a map of the predicted basement lithology in the northern URG. Accordingly, most areas are dominated by granitoids, which tend to have higher permeability than shales and gneisses and thus are preferred targets of geothermal drilling. In contrast, a predominantly metamorphic basement can be assumed in the Saxothuringian Zone and at the northwestern rift margin.
The developed 3D basement model and inversion results were key input for a techno-economic resource assessment, which furthermore incorporated data from thermal and geomechanical models, operating geothermal power plants, and financial aspects of geothermal utilization. Calculation of resources at the regional scale was based on the widely used volumetric 'heat in place' method, whereby model uncertainties were quantified by means of Monte Carlo simulation. The recoverable heat along large-scale fault zones, considered as preferential fluid pathways, was estimated as a function of the slip and dilation tendency in the recent stress field. The economically exploitable part of the resources (reserves) was subsequently investigated by a sensitivity analysis of relevant parameters. The assessment reveals that the basement in the URG is characterized by a vast resource base, of which between 8 and 16 PWh are potentially recoverable with current EGS technologies. This could sustainably provide a significant fraction of the heat and power supply in the northern URG. About 65% of the resources were economically recoverable at market conditions in January 2022. In view of the enormous increases in energy prices resulting from the war in Ukraine, the share is now likely higher. A comparison of the calculated resources with the socio-economic-environmental conditions for geothermal utilization at the surface shows a high level of correlation, especially in the densely populated areas around Mannheim and Darmstadt.
As groundwater flow in the crystalline basement is mainly controlled by open fractures, accurate knowledge of the natural fracture network is essential for the planning, development and operation of geothermal power plants. Image logs from deep boreholes provide the most meaningful information on fracture properties, but these are very rare and often inaccessible in the URG. A comprehensive structural outcrop analog study was conducted to compensate for the lack of borehole data. The Tromm Granite in the southern Odenwald was selected as the study area as it is both a suitable analog for the granitoid reservoirs in the northern URG and a potential site for the upcoming GeoLaB project. Here, lineament analyses and lidar surveys in abandoned quarries were combined, resulting in a multiscale description of the basement's fracture network. Discrete fracture network (DFN) models were then developed based on the obtained properties to estimate the permeability under assumed reservoir conditions. While the Tromm Granite is overall intensely fractured and the network is well connected, the density and orientation of fractures is strongly influenced by nearby fault zones. Fractures cluster roughly in the N-S direction, parallel to σHmax, resulting in an order of magnitude higher permeability than in the E-W direction.
The structural investigations were complemented by geophysical surveys, designed to map and characterize the buried faults in the Tromm Granite. As in the regional modelling, potential field methods (terrestrial gravimetry and aeromagnetics) were applied and additionally the radon activity concentration was measured along one profile. The gravity data show rather broad anomalies, which cannot be assigned to single faults, but rather to zones of increased fault and fracture density. Inversion of the gravity data indicated a fracture related porosity of up to 9% along the pluton margins. The drone-based aeromagnetic survey, conversely, allows a more detailed mapping of the fault network. After filtering, the dataset revealed a complex network of linear anomalies that are interpreted as altered fault zones with increased reactivation potential, thus representing preferred fluid pathways.
In conclusion, the crystalline basement is an attractive target for deep geothermal exploitation in the northern URG due to the vast resource base. As part of the dissertation, a new detailed geothermal 3D model and a regional map of the resources have been developed, providing politicians, investors, and project engineers with a more reliable basis for decision-making. Furthermore, the understanding of the fracture network properties and thus of the hydraulic properties in the northern URG was improved. Nevertheless, significant uncertainties remain at the local scale that can only be eliminated through targeted exploration measures and coupled numerical modelling. Besides, the risk of noticeable induced seismicity persists, which is a major obstacle to the exploitation of deep geothermal energy. Great hope therefore lies in the development of new safe stimulation techniques for EGS reservoirs, which will be advanced in particular within the framework of the upcoming GeoLaB project
Multi-scale analysis of active landslides using two-pass differential interferometry
Landslides are common features of the landscape of the north-central Apennine mountain range and cause frequent damage to human facilities and infrastructure. Most of these landslides move periodically with moderate velocities and, only after particular rainfall events, some accelerate abruptly. Synthetic aperture radar interferometry (InSAR) provides a particularly convenient method for studying deforming slopes. We use standard two-pass interferometry, taking advantage of the short revisit time of the Sentinel-1 satellites.
In this paper we present the results of the InSAR analysis developed on several study areas in central and Northern Italian Apennines.
The aims of the work described within the articles contained in this paper, concern: i) the potential of the standard two-pass interferometric technique for the recognition of active landslides; ii) the exploration of the potential related to the displacement time series resulting from a two-pass multiple time-scale InSAR analysis; iii) the evaluation of the possibility of making comparisons with climate forcing for cognitive and risk assessment purposes.
Our analysis successfully identified more than 400 InSAR deformation signals (IDS) in the different study areas corresponding to active slope movements. The comparison between IDSs and thematic maps allowed us to identify the main characteristics of the slopes most prone to landslides.
The analysis of displacement time series derived from monthly interferometric stacks or single 6-day interferograms allowed the establishment of landslide activity thresholds.
This information, combined with the displacement time series, allowed the relationship between ground deformation and climate forcing to be successfully investigated.
The InSAR data also gave access to the possibility of validating geographical warning systems and comparing the activity state of landslides with triggering probability thresholds
Patch-Like Reduction (PLR): A SAR offset tracking amplitude filter for deformation monitoring
As complementary to Synthetic Aperture Radar (SAR) Differential Interferometry (DInSAR), SAR Offset Tracking (OT) is an efficient tool for large ground deformation monitoring in situations when DInSAR cannot work. However, SAR images are affected by speckle noise and some strong point-like scatters which can cause what is known as Patch Like (PL), a kind of errors that can be seen as homogeneous patches of almost constant deformation in the results. These errors are clearly visible in the results as non-consistent deformations along time, but they are difficult to detect with the traditional metrics that evaluate the cross-correlation results, like the Signal to Noise Ratio (SNR). This paper addresses this problem and proposes a simple amplitude filter to reduce PL named as Patch Like Reduction (PLR). The main idea is to find a sensor and scene independent threshold to remove the high amplitude pixels prone to cause PL. Five different SAR data sets and in-field GPS measurements are used to determine the optimal threshold and evaluate the performance of the proposed method. The results show that PL effects can be reduced with the proposed amplitude filter. The processing parameters of the improved OT processing chain are optimized as well to preserve the results resolution as much as possible.This work has been financially supported by China Scholarship Council (Grant No. 201806420035), the Spanish Ministry of Science and Innovation (MCINN) and the State Research Agency (AEI) project PID2020-117303GB-C21 MCIN/AEI/10.13039/501100011033. This work has also been financially supported by the Natural Science Foundation of China (Grant No. 42004011), China Postdoctoral Science Foundation (Grant No. 2020M671646), Centro para el Desarrollo Tecnológico Industrial and Ministry of Science and Technology of the People’s Republic of China (Spanish-Chinese CHINEKA project No. 2022YFE0102600), and the Ministry of Education of the People’s Republic of China (Construction Program of Space-Air-Ground-Well Cooperative Awareness Spatial Information Project B20046)Peer ReviewedPostprint (published version
Data Processing and Modeling on Volcanic and Seismic Areas
This special volume aims to collecg new ideas and contributions at the frontier between the fields of data handling, processing and modeling for volcanic and seismic systems. Technological evolution, as well as the increasing availability of new sensors and platforms, and freely available data, pose a new challenge to the scientific community in the development new tools and methods that can integrate and process different information. The recent growth in multi-sensor monitoring networks and satellites, along with the exponential increase in the spatiotemporal data, has revealed an increasingly compelling need to develop data processing, analysis and modeling tools. Data processing, analysis and modeling techniques may allow significant information to be identified and integrated into volcanic/seismological monitoring systems. The newly developed technology is expected to improve operational hazard detection, alerting, and management abilities
Remote Sensing of Natural Hazards
Each year, natural hazards such as earthquakes, cyclones, flooding, landslides, wildfires, avalanches, volcanic eruption, extreme temperatures, storm surges, drought, etc., result in widespread loss of life, livelihood, and critical infrastructure globally. With the unprecedented growth of the human population, largescale development activities, and changes to the natural environment, the frequency and intensity of extreme natural events and consequent impacts are expected to increase in the future.Technological interventions provide essential provisions for the prevention and mitigation of natural hazards. The data obtained through remote sensing systems with varied spatial, spectral, and temporal resolutions particularly provide prospects for furthering knowledge on spatiotemporal patterns and forecasting of natural hazards. The collection of data using earth observation systems has been valuable for alleviating the adverse effects of natural hazards, especially with their near real-time capabilities for tracking extreme natural events. Remote sensing systems from different platforms also serve as an important decision-support tool for devising response strategies, coordinating rescue operations, and making damage and loss estimations.With these in mind, this book seeks original contributions to the advanced applications of remote sensing and geographic information systems (GIS) techniques in understanding various dimensions of natural hazards through new theory, data products, and robust approaches
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