In our recent report "The Next Generation of Satellite Imaging," we discussed the present and future applications of satellite imaging across various industries. In this blog, we guide the oil and gas industry to build three-term strategies based on various opportunities offered by satellite imaging:
1. Short-term – Measurement of carbon sequestration
Large oil and gas companies, including Shell, BP, and Chevron, have pledged to become carbon neutral. While fully switching to synthetic fuel is the ultimate goal, companies are also using several temporary measures, such as offsetting CO2 emissions from the fuel they sell by planting an adequate number of trees. However, it is often unclear if the compensation is sufficient to offset these emissions. Satellite data can help these companies measure the extent to which their afforestation efforts are offsetting their CO2 emissions.
This can be done by either using satellites with regular (RGB) cameras or using more advanced hyperspectral imaging-equipped satellites. The former allows for capturing the surface area of forests before and after the afforestation initiative, which can then be used in combination with carbon sequestration data. The use of hyperspectral imaging offers a much higher level of granularity in attributes like the density of the forest, classification of tree species and their CO2 absorption rate, measurement of wildlife and vegetation underneath, chlorophyll mapping, and the measurement of air particles, such as moisture, or different types of gases, such as nitrogen, emitted from or absorbed by the forest – all of which are related to sustainability. However, hyperspectral imaging is still an expensive, slow, and power-consuming technology, which makes it easier to mount on large traditional satellites launched by NASA and ESA. Companies should closely monitor research centers and startups like TNO, VTT Technical Research Centre of Finland, and Satellogic, which are beginning to offer hyperspectral imaging with smallsats, which will be a game changer in measuring carbon sequestration from forests.
2. Medium-term – Methane leak measurement
With rising concerns about global warming and the defrosting of permafrosts, the focus on greenhouse gas emissions is increasing on a global scale. One of the main greenhouse gases is methane (CH4), which is estimated to have more than 80 times the warming power of CO2 during its first 20 years after being released into the atmosphere. Many are pointing to the oil and gas industry as the main culprit of methane leakage; however, others argue that the emissions from peatland are more significant. Although peatlands only cover 3% of the world's land area, they are estimated to contain 21% of the world's soil carbon. Therefore, the proper identification of sources and contributions of methane emission is crucial for oil and gas companies to avoid the pressure of decarbonization.
Current field-based methods of estimating the annual carbon exchange between peatlands and the atmosphere include flux chambers and eddy covariance towers. The use of remote sensing methods like satellite imaging offers several advantages in terms of cost, spatial coverage, and accessibility to remote locations. As most peatlands are covered by dense vegetation, the use of regular satellite imaging and lidar systems is not always possible. The use of multispectral and hyperspectral imaging that can penetrate through vegetation is necessary.
In addition to the quantification of methane emissions from peatland, satellite imaging can also be used to detect pipeline leakages, which are another source of methane emissions. Traditionally, measurement of methane emissions from pipeline leakage is done by mounting multispectral cameras on helicopters or airplanes, but using satellites allows for more frequent data updates at a similar price. Startups like GHGSat, Bluefield, and MethaneSAT use multispectral imaging cameras, which are tuned to detect one or a few types of gases based on their spectroscopic signatures to detect leakage. Similarly, startup Satelytics monitors changes in vegetation around pipelines with hyperspectral cameras to detect small leaks. The rapid improvements in satellite imaging will make it easier for oil and gas companies to detect and quantify methane emissions with increasing detail and frequency.
Oil and gas companies are increasingly adding renewable energy to their portfolio. If they want to differentiate their renewable offering against existing utility companies, they should be able to predict and anticipate the production of renewable energy. The generation of wind energy and solar energy depends on favorable weather conditions. Hence, strong weather forecasting is required to predict how much energy can be reaped from turbines and solar panels when the wind is blowing or when the sun is shining, which is then used to predict the amount of renewable power that will flow into the grid in the next 15 minutes, hour, day, or even week. Accurate predictions are critical to operators of renewable power plants, who must report their expected production to power grid operators and can be fined if they deviate from that forecast. These predictions can be made using various methods, including satellite imaging.
Although satellite imaging has been used to predict the weather for a long time, smallsats have made it even more accessible. In addition, the use of hyperspectral imaging enables the generation of an extensively detailed 3D profile of atmospheric temperatures and moisture (sounding) that ultimately fit into weather models. For comparison, it would take hundreds of thousands of weather balloons to generate the level of detail that one satellite equipped with hyperspectral imaging produces. Furthermore, the development of AI tools like machine learning algorithms has made it possible to extract more information from images than before. In its recent blog post, Google claimed that its AI model can produce nearly instantaneous weather forecasts. In the non-peer-reviewed paper, Google's researchers describe how they were able to generate accurate rainfall predictions up to six hours ahead of time at a 1 km resolution from just minutes of calculation. As a result, NOAA has formed a partnership with Google to use AI tools for enhancing its satellite data. As an increasing number of satellites are capturing atmospheric data and AI tools are becoming better at analyzing those data, clients should see satellite imaging as a strong technology to predict weather and subsequently the generation of renewable energy.