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In July 2021, Shell Argentina announced the construction of a 105 km crude pipeline in the Vaca Muerta in order to eliminate a transportation bottleneck. The 16 in. line will transport up to 120 000 bpd between Neuquen and Rio Negro provinces. The US$80 million project is expected to enter service at the end of 2022.

Guyana In Guyana, the prospects for the oil and gas sector continue to brighten. In May 2021, ExxonMobil announced a new discovery in its offshore Starbroek block, Uara-2, which now places its reserves above 9 billion bbl of oil. Currently, Guyana’s production stands at approximately 130 000 bpd at the Liza-1 FPSO, with Liza-2 expected to begin 220 000 bpd production in 2022. Exxon has two more projects; the 220 000 bpd Payara project and the 220 000 bpd Yellowtail project, which, when completed, will bring offshore production to almost 800 000 bpd by 2025. Exxon and partner Hess have a total of six projects and up to 10 FPSOs planned for Guyana by 2027. The light, sweet crude being pumped makes it relatively easy to create high-quality fuels; break-even prices for the play are estimated to be as low as US$25/bbl.

Suriname The former Dutch colony of Suriname shares much of the offshore Guyana-Suriname Basin in which ExxonMobil and partners have made major discoveries. Total and Apache, which hold the Block 58 lease in Suriname waters, have made four oil discoveries contiguous with Exxon’s Stabroek Block. Rystad Energy estimates that recoverable reserves now stand at almost 2 billion bbls. The consultancy further expects that production from the impoverished country could begin as soon as middecade, with output rising to 650 000 bpd by 2030.

The future The prospects for oil and gas remain bright, especially for natural gas pipelines. In addition to Brazil’s liberalisation, new producing jurisdictions such as Guyana and Suriname hold tremendous potential for the monetisation of associated gas. Even Mexico’s phenomenal network expansion still has a long way to go; government agencies note that many aspects are still ‘20-30 years behind’ US infrastructure. A sign of renewed opportunity has already emerged; in August 2021, CFE announced a memorandum of understanding (MOU) with TC Energy, stating that it would work to resolve social conflicts over the 268 km Tuxpan-Tula pipeline; construction was halted on the 886 million ft3/d line amid worries that the ROW would trespass sacred lands. In addition, CFE and TC Energy are also working to extend the country’s natural gas network to the south in order to deliver commercial quantities of gas to the Yucatan peninsula. A new offshore pipeline will be built from the Tuxpan terminal in the state of Veracruz to connect to the Mayakan pipeline system in the states of Campeche and Tabasco. The developments are seen as positive steps toward reconciliation between the pipeline sector and AMLO after the latter renegotiated pipeline contracts in 2019 that he asserted were unfair to Mexican taxpayers.

In conclusion, as the scourge of COVID recedes and demand once again resumes, countries in Latin America will continue to explore and develop their rich endowment of energy resources in an effort to build their economies and improve the wellbeing of their citizens.

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Nomcebo Jele, SuperVision Earth, Germany, discusses how satellite technology can help to enhance the monitoring of oil and gas pipelines.

Pipelines are commonly used to transport hydrocarbon fluids across thousands of kilometres around the world. With over a million kilometres of high-pressure pipelines worldwide, oil and gas infrastructure are naturally vulnerable to threats. Although pipeline structures are “designed to withstand several environmental loading conditions, to ensure safe and reliable distribution from the point of production to the shore or distribution depot”, leaks in pipeline networks are a significant source of loss for pipeline operators and the environment. The causes of pipeline damage vary. Third-party activity, vegetation, and ground movement are all frequent threats to pipeline networks today. Similarly, activities such as construction and drilling near or on pipelines are the major cause of pipeline infrastructure incidents. To avoid such threats and maintain a secure and reliable pipeline system, SuperVision Earth is developing an innovative network monitoring solution that offers effectiveness and consistency through the integration of satellite images into pipeline leak detection and risk management workflows.

Review of current pipeline monitoring systems A leak detection system (LDS) is designed to assist pipeline controllers in detecting and locating leaks. Alarms, displays, and other associated data are provided to pipeline controllers to aid in decision-making. This results in reduced downtime and inspection times, increasing productivity and reliability of the pipeline network.

In recent years, natural gas has become one of the world’s most vital energy resources and demand for it is predicted to be growing. Nevertheless, as the demand for natural gas has grown, the problem of pipeline leak detection has become more relevant.

Most traditional leak detection technologies rely on periodic inspections by helicopter, on foot monitoring, optic sensors, drones and so on. Some more examples include: acoustic emission (Meng, Yuxing, Wuchang, & Juntao, 2014), fibre optic sensor (Lim, Wong, Chiu, & Kodikara, 2016), ground penetration radar (Hoarau, Ginolhac, Atto, & Nicolas, 2017), negative pressure wave (Delgado & Mendoza, 2017), vapour sampling, infrared thermography, digital signal processing, and mass-volume balance (Manekiya & Arulmozhivarman, 2016). Many authors categorise them differently.

For example, fibre optic sensing can provide quicker detection if the leak secretes smaller amounts of the substance, as this method takes direct measurements of different response dynamics. Although fibre optic sensing detection offers many advantages within structural monitoring contexts, it has limitations such as costs of installation, complex and unfamiliar detection systems and the precision required for installation, making it a complicated system to incorporate into existing pipeline leak detection systems (Frings & Walk, 2010).

Right-of-way surveillance – also known as ground patrols and aircraft surveillance – is employed to notice any unexpected behaviour, but they provide neither continuous nor real-time detection. As a result, significant portions of pipeline may go unmonitored for long periods of time, leaving them exposed to unintentional damage or even criminal threats.

According to Barbosa (2021), there is a standard requirement in North America to identify a leak equivalent to 1% of the pipeline’s flowrate. That 1% translates to 1000 bbls in a pipeline moving 100 000 bpd of oil. Some 250 bbls will have escaped in six hours if a leak is discovered. This emphasises the need for real-time, reliable and accurate pipeline monitoring systems.

A continuous monitoring solution is urgently needed to enable operators to detect breaches and theft attempts more precisely and rapidly, assisting pipeline operators in their efforts to reduce product loss. However, all pipeline leak detection systems currently available fail to enable realtime pipeline monitoring and risk prevention, resulting in delayed leak detection reaction time, system dependability, leak detection sensitivity, positioning accuracy, and system cost (Xu, Zhao, & Bai, 2020). Furthermore, high costs, planning complexities and insufficient access to pipeline routes remain major hindrances to reaching full pipeline security and monitoring.

Satellite monitoring in pipeline leak detection The pipeline industry’s best strategy for dealing with public and environmental safety is risk management. Satellite technology allows for most threats on and along pipeline routes to be observed from space. The leaks and movements on or near the pipelines can be detected daily by this real-time pipeline monitoring system.

With the use of satellite technology and AI-driven data processing techniques, it is now possible to remotely monitor thousands of kilometres of pipelines for third party activity to prevent hydrocarbon and methane leakage. Satellites can also assist in identifying new buildings, farms, machinery, and other objects that are encroaching on the right-of-way or pipeline route. Furthermore, satellites are versatile and scalable to use to monitor vegetation along pipelines, including determining the health and growth of various plant species.

Currently, helicopters and airplanes and vehicles with heavy cross-country traffic capabilities are used as a form of remote monitoring for pipeline conditions. These traditional methods are not only time-consuming, but also costly. Research efforts and investments are being injected into more cost-effective remote monitoring methods such as unmanned systems and satellite imaging. Although unmanned aerial vehicles (UAVs) have some advantages in terms of ultra-high spatial resolution, their deployment still necessitates the direct flight of professionals to the location, which incurs additional costs (INNOTER, 2021).

Importance of monitoring One of the most important reasons for regularly inspecting pipelines is to protect the environment and the human life in the area where the pipeline runs through. The pipeline system, which is buried below, receives less attention than other infrastructure systems such as roadways and bridges.

Figure 1. Statistics on the major causes of pipeline failure (Adegboye, Fung, & Karnik, 2019).

Figure 2. Annual CO2 emissions from the burning of fossil fuels for energy and cement production. Land use charge is not included. Source: Global Carbon Project; Carbon Dioxide Information Analysis Centre (CDIAC, 2019).

Furthermore, depending on what is being transported, regulatory control of pipeline safety, operations, and worthiness is varied.

By offering deep learning in the classification of satellite imagery, in combination with the most recent breakthroughs in AI-based technology, pipelines can now be monitored at great speed and with even higher accuracy. A GIS specialist, for example, can evaluate and record extraneous objects, violations of diversion zones, and air pollution faster than with traditional visual monitoring techniques by executing the algorithm for analysing satellite photos (INNOTER, 2021).

According to INNOTER (2021) the only problem with automatic decryption and detection is the accumulation of geographical patterns and/or training and test data sets. Machine learning algorithms learn from data. The algorithms use training data to form relationships, gain understanding, make judgements and to assess their confidence. In actuality, the quality and quantity of machine learning training data is just as important as the algorithms themselves and the model or algorithm works better when the training data is good (Appen, 2020).

Emissions Since 1990, annual global greenhouse gas emissions have increased by 41% and are continually rising. Energy consumption is by far the most significant source of human-caused greenhouse gas emissions, accounting for 73% of global emissions (Mengpin & Friedrich, 2020). Transportation, electricity and heat, buildings, manufacturing and construction, fugitive emissions, and other fuel combustion are all part of the energy industry.

Carbon dioxide emissions are not falling fast enough in developed countries, where they have already peaked, to counteract emissions growth elsewhere. Emissions in the EU and the US were projected to fall by 1.7% in 2019, while emissions in India were expected to rise 1.8% (significantly lower than the past five-year growth rate of 5.1%). China’s were expected to rise 2.6% – larger than the global total increase – and finally, emissions in the rest of the world are expected to rise 0.5% (Hausfather, 2019).

Figure 2 is a map that shows the annual CO2 emissions by country, with the highest emitting country being China accounting for a share of 10.17 billion t from the world +36.41 billion t. The Global Carbon Project illustrates how global carbon dioxide emissions from fossil fuels have already reached new heights in 2019, putting the planet at risk of catastrophic climate change due to the heattrapping gases (CDIAC, 2019).

According to the new Global Energy Review 2021 report from the International Energy Agency, energyrelated carbon dioxide emissions are expected to rise by 1.5 billion t this year, the second-largest increase in history (IEA, 2021). According to the report, CO2 emissions are likely to grow about 5% to 33 billion t in 2021, reversing the estimated drop in emissions from 2020 which was caused by the COVID-19 pandemic. The increase in greenhouse gas (gases that trap heat in the atmosphere) emissions this year will be the highest yearly increase since the global financial crisis ended in 2010.

The IEA claims that the rise in coal use is the primary cause of the increase in emissions, predicting that coal use in 2021 will be close to its all-time high of 2014. According to the International Energy Agency, global CO2 emissions will rise considerably this year, despite a predicted increase in wind-power output and solar-power generation. In 2021, renewable energy, including hydropower, is predicted to account for 30% of global electricity generation. Nonetheless, CO2 emissions are rising due to global developing economies, particularly Asia and specifically China, where coal-fired power plant building is on the rise (Global CO2 Emissions set to Surge in 2021 Post-COVID Economic Rebound, 2021).

Patricia Espinosa, the UN’s top Climate official, said: “it’s time to wrap up outstanding negotiations and implement the Paris agreement. Time is running out for the world to achieve the goals of the Paris agreement. Unleashing its full potential will not only address climate change but [it will] help the world build forward from COVID-19 and drive transformation towards a cleaner, greener and more sustainable future” (Harvey, 2021).

Combining AI and satellite technology Our objective at SuperVision Earth is to make gas infrastructure safer. To do so, we are working on an innovative network monitoring solution that promises efficacy and consistency through the integration of satellite imagery. We can discover dangers such as third-party interference (e.g. construction operations), vegetation change, and ground deformation by analysing satellite data. Our algorithms, which are easily deployed on the cloud, analyse the data and provide alerts that can be directly integrated into the operator’s pipeline monitoring

Figure 3. SuperVision Space (SVS) app location detection.

software, long before they would have been reported by traditional monitoring.

Pipeline disasters are usually severe enough to put many lives in danger, devastate the environment, and cost millions in damages. To ensure the protection of the environment and people alike, the safety of pipelines should be paramount in the maintenance of a pipeline.

Whilst pipelines generally go through a variety of geographical areas and are often in remote, isolated areas, the potential impact of a disaster could result in large scale catastrophic damage to its surroundings. Therefore, more stringent and regular monitoring is required. Techniques which are not optimised by the already existing monitoring methods. Pipeline operators require more than routine inspections; they require a continuous pipeline monitoring system that analyses satellite imagery and informs them of any unexpected activities or changes in the vicinity or routes of their pipelines. This is achievable with GIS-enabled services and high-resolution imagery (Roberts, 2019).

An advantage of SuperVision Earth’s innovation is high temporal resolution. Here, satellites acquire images of the same section of the surface of the earth to achieve high temporal resolution. Optical images from space employ reflected light from the Earth’s surface as a source of radiation. Radar satellites, on the other hand, work in a different way: they actively generate radiation that can penetrate clouds. This allows for reliable monitoring regardless of the weather conditions (Hilsenbek, 2020).

Small satellites are being used by commercial suppliers to acquire optical images daily. Aside from the increased temporal resolution, another advantage is that the satellite sensors gather data that is not visible to the naked eye. This additional data allows for a more detailed perspective than the human eye can provide. As a result, dangers that might normally go unnoticed during aerial surveys can be identified.

Further changes on the surface can be observed using radar data. They can not only provide information on the surface structure/roughness and thus the kind of subsoil, but they can also detect changes in soil moisture, for example. Interferometric techniques can also be used to identify and, in certain situations, anticipate large-scale ground deformations and landslides.

Figure 4 illustrates the added value of SuperVision Earth’s service. SuperVision Earth’s AI-innovation detected the threats in the images below. The before and after images were captured one month apart, respectively. They reveal construction activity near the protected area of a high-pressure gas pipeline path. The images provided have the spatial resolution of 0.5 m. Prices of satellite images vary depending on the chosen resolution and range between 0.3 - 0.5 m. In this case, detection from a 0.5 m distance is adequate as data can still be extracted from the image. The yellow line lies on what would be the actual pipeline in real life.

In the case of a traditional aerial survey, valuable time (up to several weeks) would have passed before this was revealed. This solution provides the operator with more time to investigate whether the stored equipment is registered and, if in question, to advise the personnel on site about the nearby pipeline and the related risks, thanks to the earlier warning and detection.

SuperVision Earth’s proprietary technology employs algorithms that detect and classify changes between two photos to achieve these results. The optical data as well as the radar data are both processed. For optical data, all accessible spectral bands are used to produce several indices that distinguish between relevant (e.g., a construction site) and irrelevant changes (e.g., the harvesting of an agricultural field). In order to improve the classification, historical photographs are added in addition to current images. In addition to the optical data, radar data is considered.

This, when combined with the optical data, results in optimum detection. In this method, the number of mistakenly recognised dangers (‘false positives’) can be reduced, resulting in fewer unwanted warnings and expenditures owing to the operator’s superfluous reactions. Our algorithms are continually evolving with the help of machine learning technologies in order to be able to interpret vast amounts of data, and hence recognise various hazards, even better. Once threats are detected, the information is automatically and immediately sent to the pipeline integrity manager or engineer’s profile on the SuperVision Space app, which also incorporates the customer’s chosen GIS or PIMS software. The SuperVision Space (SVS) app is an AI-based innovation that uses earth observation and remote sensing technology to

Figure 4. Left: Before – detected construction of a residential house. Right: After – showing ongoing construction activity along pipeline route.