Introduction
1. Present application of spatial information technologies in CO2 storage monitoring
Fig. 1. Spatial distribution and relationship of each sector in the application of spatial information technologies in carbon storage monitoring ( |
Table 1. Technical indicators, maturity, and application cases of spatial information technologies in carbon storage monitoring |
Category | Technology | Monitoring parameters/functions | Technical maturity | Cases of application or validation |
---|---|---|---|---|
Eddy covariance method | Eddy covariance monitoring | CO2 flux [11] | Applied in carbon storage projects | (1) Applied to the CO2CRC Otway carbon storage project in Australia [20]; (2) Applied to the CO2 Enhanced Recovery Project of Farnsworth Oilfield in the United States [21] |
Remote sensing technology | Differential absorption LiDAR monitoring | CO2 number density [11] | Applied in carbon storage projects | Applied to Kevin Dome carbon storage project in the United States [11] |
Fourier transform infrared spectroscopy monitoring | CO2 concentration [22] | Pilot study | Technical verification at the site of natural CO2 seepage from the sub-surface in the Cheb Basin of Bohemia, Czech Republic [22] | |
Tunable diode laser absorption spectrometer monitoring | CO2 concentration [23] | Applied in carbon storage projects | Applied to the Decatur Carbon Storage Project in the Illinois Basin of the United States [23] | |
Raman LiDAR monitoring | CO2 concentration [17] | Pilot study | Technical validation conducted at Eumsong Environmental Impact Assessment testing facility in South Korea [17] | |
Greenhouse gas laser imaging tomography monitoring | CO2 concentration [11] | Applied in carbon storage projects | Applied to the Decatur Carbon Storage Project in the Illinois Basin of the United States[11] | |
Unmanned aerial vehicle remote sensing (UAVRS) | CO2 concentration [11] | Pilot study | (1) Testing UAVRS data transmission in Barranquilla, Colombia [24]; (2) The UAV integration system assembled by Oklahoma State University is expected to be installed, tested, and optimized in the Farnsworth carbon storage project in the United States [11] | |
Satellite spectral monitoring | CO2 concentration [17] | Under development | ||
Hyperspectral imaging | Vegetation health [11] | Pilot study | Tested at ZERT Test Site in Montana, United States [11] | |
Interferometric synthetic aperture radar technology | Ground deformation [11] | Applied in carbon storage projects | (1) Applied to In Salah CO2 storage project in Algeria [11]; (2) Applied to the CO2 enhanced oil recovery project of Kelly Snyder oilfield in the United States [25]; (3) Applied to the CO2 enhanced oil recovery project in Jingbian, China [26]; (4) Applied to the CO2 storage project of Aquistore in Canada [27] | |
GNSS | GNSS monitoring | Ground deformation [11] | Applied in carbon storage projects | (1) Applied to the CO2 storage project of Aquistore in Canada [27]; (2) Applied to the CO2 enhanced oil recovery project in southern Texas of the United States [28] |
GNSS positioning | Positioning | Pilot study | Tested the integration of CO2 sensors and GNSS receivers in Jakarta, Indonesia [29] | |
IoT technology | Wireless sensor network | Data transmission | Under development | |
Geographic Information System | Geographic Information System | Data management and decision support | Applied in carbon storage projects | Applied to the Decatur Carbon Storage Project in the Illinois Basin of the United States [30] |
1.1. Acquisition of monitoring data
Table 2. Software/hardware requirements and space/time ranges of spatial information technologies for data acquisition in carbon storage monitoring |
Technology | Software and hardware requirements | Monitoring range of time and space |
---|---|---|
Eddy covariance monitoring | Hardware: 3D acoustic anemometer, gas analyzer [31]; Software: Signal processing software (converting electrical signals into physical parameters, removing peak values from raw data, correcting raw data, etc.), inversion algorithms for evaluating leak position [31,34] | Space: Several square meters to several square kilometers [11,31]; Time: 10-20 records are made per second on average, and the carbon flux for a specific time period is calculated by integrating instantaneous data [11,31] |
Differential absorption LiDAR monitoring | Hardware: Pulse laser transmitter, Differential absorption LiDAR monitoring receiver [11]; Software: Algorithm for inverting CO2 concentration from differential absorption data [35] | Space: Several square meters to several square kilometers [11]; Time: The data sampling interval is a few minutes [36] |
Fourier transform infrared spectroscopy monitoring | Hardware: Interferometer, infrared detector [22]; Software: Spectral algorithm for samples [22] | Space: Several square meters to several square kilometers [22]; Time: A complete collection sequence takes 10 min [22] |
Tunable diode laser absorption spectrometer monitoring | Hardware: Tunable infrared laser, detector [23]; Software: Algorithm for inverting CO2 concentration [23] | Space: The detection distance of the system installed on site for the Decatur carbon storage project in the Illinois Basin of the United States is 100 m [23]; Time: With high temporal resolution, suitable for continuous monitoring [37] |
Raman LiDAR monitoring | Hardware: Laser, detector and others [36]; Software: Algorithm for calculating CO2 concentrations[38] | Space: The detection distance in pilot test is 2 km [38]; Time: The data sampling interval is a few minutes [39] |
Greenhouse gas laser imaging tomography monitoring | Hardware: Laser transceiver, reflector, weather station[11]; Software: Cloud-based software tools (used for data processing, storage, propagation, and nearly real-time generation of two-dimensional maps of CO2 concentration)[11] | Space: Verified on-site, the detectable range is 0.2 km2 [11]; Time: Capable of providing real-time CO2 concentration feedback [11] |
UAVRS | Hardware: CO2 sensors, UAV platforms [11]; Software: Visualization of monitoring data on maps | Space: Pilot tests show the UAV can be deployed for data transmission within an altitude range of 0-120 m [24]; Time: Inspection intervals can be set according to needs |
Satellite spectral monitoring | Hardware: Satellite, spectrometer [17]; Software: Algorithm for calculating CO2 concentrations [17] | Space: Several hundred square meters to several square kilometers [40]; Time: Passive remote sensing technology is subject to impacts of the return cycle [40] |
Hyperspectral imaging | Hardware: Spectral imaging sensor [11]; Software: Image denoising, inversion, and visualization | Space: Several square meters to several square kilometers [11]; Time: Usually, signals are recorded on a daily basis [11] |
Interferometric synthetic aperture radar technology | Hardware: Interferometric synthetic aperture radar [17]; Software: Algorithm for inverting ground deformation | Space: With millimeter level resolution, it can cover an area of 1×104 km2 [11]; Time: Determined by the satellite access cycle of carrying radar, usually with a time interval of day [11] |
GNSS monitoring | Hardware: Satellite, signal receiving station Software: Algorithm for inverting ground deformation | Space: With millimeter level resolution, it can cover the entire world [11]; Time: Available for continuous observation |
1.1.1. Near-surface eddy covariance monitoring
1.1.2. Differential absorption LiDAR monitoring
1.1.3. Unmanned aerial vehicle remote sensing
1.1.4. Interferometric synthetic aperture radar technology
1.1.5. Global Navigation Satellite System monitoring
1.1.6. Hyperspectral imaging
1.2. Positioning and data transmission
1.2.1. Global Navigation Satellite System
1.2.2. Internet of Things (IoT)
1.3. Data management and decision support
Fig. 2. Acquisition, transmission, and management of monitoring data. |
1.3.1. Geographic Information System
1.3.2. Cloud computing services
2. Challenges faced by spatial information technologies in CO2 geological storage monitoring
Table 3. Technical advantages and challenges of different spatial information technologies |
Technology | Advantages | Challenges |
---|---|---|
Eddy covariance monitoring | Large measurable spatial range and the ability to provide continuous measurement data [11] | Huge storage spaces are required for massive measurement data (with data collection frequency of 10 Hz or 20 Hz); Effective data processing methods are needed to determine the location and amount of leakage, and the natural space/time variation of CO2 flux has a certain impact on the detection of CO2 leakage signals [11,31] |
Differential absorption LiDAR monitoring | Relatively low cost and high mobility [11] | The natural space/time variation of CO2 flux has a certain impact on the detection of CO2 leakage signals [11] |
Fourier transform infrared spectroscopy `monitoring | Capable of monitor multiple gas components simultaneously [17] | Temperature, humidity, and other meteorological conditions can affect the accuracy of measurements [17] |
Tunable diode laser absorption spectrometer monitoring | Handheld instruments can be used for convenient and flexible monitoring [17] | Short distances for accurately detecting concentrations [23] |
Raman LiDAR monitoring | Less instruments and low cost required [17] | The Raman echoes of CO2 are very weak and difficult to be extracted from the background light waves [17] |
Greenhouse gas laser imaging tomography monitoring | This is an automated monitoring technology that can monitor and visualize real-time changes in atmospheric CO2 concentration, helping to reduce environmental monitoring costs [11] | Due to the need for denoising, correction, and other processing of laser signals, high requirements are placed on information processing equipment and algorithms |
UAV remote sensing monitoring | Fast monitoring, high efficiency and lower labor costs [47⇓⇓⇓⇓⇓⇓-54] | Smaller monitoring range |
Satellite spectral monitoring | Wide monitoring range with no need for on-site equipment [17] | Susceptible to disturbances of aerosols under low resolution (1-10 km) conditions [17] |
Hyperspectral imaging | Wide monitoring range | The accuracy of passive optical remote sensing inversion is relatively low and is greatly affected by the inversion model; The development time of active remote sensing is relatively short, and the technological maturity is low |
Interferometric synthetic aperture radar technology | Available to monitor large-scale millimeter level surface deformation [11] | Significant errors may be observed in areas with complex terrain conditions, high vegetation coverage, and high land use efficiency [11] |
Global Navigation Satellite System Monitoring | Available to monitor large-scale millimeter level surface deformation [11] | High requirements for inversion algorithms |
Global Navigation Satellite System Positioning | Combining CO2 sensors with GNSS receivers can accurately determine the CO2 concentration at specific locations in real time [29] | High equipment requirements |
Wireless sensor network | Can achieve remote sensing and control, improve monitoring efficiency, accuracy and economy, and minimize human involvement [63] | Vulnerable to interference with low confidentiality |
Geographic Information Systems | Capable of integrating spatial data from different sources to facilitate spatial analysis of data [15] | Regular maintenance and updates of the system are required |