Small unmanned airborne systems to support oil and gas pipeline monitoring and mapping

Conventional monitoring systems of oil and gas pipelines

Monitoring oil and gas pipeline networks requires periodic assessment of the physical state and functioning of the pipes to minimize the risk of leakage, spill and theft, as well as documenting actual incidents and the effects produced on the environment. Detailed mapping for monitoring involves characterization of a base condition and identification of any changes produced during the pipeline life (e.g. addition of new valves, modification of fluxes) as well as any incidents. Furthermore, monitoring oil and gas pipelines differs from monitoring other infrastructure owing to the need for the early detection of spills or leakages.

Traditional monitoring of pipeline networks has been often restricted to visual inspections or volume and mass balance measurements. Currently, most of the monitoring is still performed using conventional methods, mainly through periodic inspections by foot patrols and aerial surveillance using light aircraft or helicopters (Murvai and Silea 2012). Although ensuring a high level of security, the cost of periodic monitoring methods involving intensive human action is very high. Furthermore, the main disadvantage of those methods used for monitoring and inspection is the potential for late detection of failures, when the output (oil or gas) has been reduced, or the environment has already been affected and damaged. Some alternative approaches to monitoring pipelines that do not rely on human intervention utilize real-time monitoring systems based on a network of small sensors (e.g. pressure, acoustic and temperature). These high sensitivity sensors are designed to measure real-time flow, enabling detection of leakages, or to identify changes in the wall thickness through temperature or noise measurements (El-Darymli et al. 2009). A network of well-distributed sensors can be connected to the pipeline network, and coordinated to send data to a control centre via wire or wireless. However, sensors are vulnerable to damage at any point along the network and can provide incomplete or inaccurate information (Wan et al. 2012). Concerns still exist in relation to the monitoring of pipelines with wireless sensor networks, such as reliability, standardization, energy consumption and general operational, data and physical security issues, especially in areas where vandalism and sabotage is a threat (Obodoeze et al. 2012), or for long distance pipelines (Almazyad et al. 2014).

Satellite data from radar (e.g. ERS, RADARSAT-1, ENVISAT) and optical images are used operationally to detect oil spills in marine environments (Brekke and Solberg 2005), where the hydrocarbons’ spectral signature is very distinctive. Differential interferometric synthetic aperture radar (DInSAR) technology has the capacity to identify millimetre ground deformation and land subsidence (Tomás et al. 2010) alerting hazardous situations for pipes. DInSAR technology on large UAV is currently in the design phase (e.g. NASA UAVSAR). To date, no single system or sensor is able to provide all the information required for oil spill contingency planning (Goodman 1994; Jha et al. 2008). On the contrary, airborne and satellite missions combined carrying diverse instrumentation—optical, hyperspectral and radar on-board satellites, lidar on-board aircraft—have demonstrated capacity to characterize oil spill location, extent, concentration and changes over time in catastrophic spills like the Deepwater Horizon BP oil spill in the Gulf of Mexico (NASA 2010). Although investment has been made in numerous projects using satellite-based remote sensing (e.g. PRESENSE, PIPEMOD and GMOSS) to reach the monitoring demands required by pipeline operators, to date, no satisfactory conclusions have been achieved. Progress in high-resolution remote sensing and image processing technology has provided the basis for designing pipeline monitoring systems using remote sensors and context-oriented image processing software (Hausamann et al. 2005; van der Werff et al. 2008), but the configuration of spatial and temporal data resolutions of operational satellite missions is still not enough for monitoring energy pipelines. Traditional airborne solutions provide some mission flexibility and enable frequent coverage of relatively large areas with high spatial resolution imagery. Airborne monitoring programs have also their own safety difficulties: manned aircraft using pilot and/or operator for detection and identification tasks are forced to fly very close to the terrain, they frequently can only detect visible effects (i.e. no gas leak detection) and they require expensive manned aircraft, limiting the frequency and duration of flight.

Identification of hydrocarbon leaks

Monitoring systems should be able to detect leaks from oil or gas pipelines as soon as possible, aiming to minimize the volume of hydrocarbon loss and to lessen the impact on the pipeline network as well as the environmental damage. With a network of well-distributed sensors—volumetric or gas detectors—in communication with a control station, the location of the faulty section is facilitated, reducing the area in need of detailed inspection. Otherwise, regular and detailed inspections of the entire pipeline network are necessary. A number of techniques for the identification of hydrocarbon leaks exist and can be applied, whether for an initial alert or for a detailed inspection once the presence of a leak is acknowledged.

The simplest and most direct method to identify a hydrocarbon leak applies visual observation, on real-time inspection or analysing a recorded image. Trained observers can recognize leaking pipelines by the colour, texture and pattern displayed in a still or video image portraying data captured in some electromagnetic wavelengths (Ellis et al. 2001; Gade and Alpers 1999). Beyond the visible (0.4–0.8 μm), other electromagnetic wavelengths are practical to detect hydrocarbon leaking. The thermal infrared (TIR) wavelengths (8–14 μm) are particularly useful due to the temperature differences between the fluids (i.e. hydrocarbons) and the soil (Grierson 1998; Kodikara et al. 2011). The thermal conductivity of soils is affected by draining liquids, and whilst an oil leak creates a warmer area, gas escapes show colder than the ground substrate. Consequently, the rationale for detecting hydrocarbon leakages from pipelines using TIR data surveys is based upon thermal differences, either on a single image, where leakage points look remarkably different to the surroundings, or by comparison of images of the same area captured on different days. Images with adequate spatial resolution (0.1–0.2 m) can clearly show pipeline thermal traces, watered sites with high danger of corrosion and hydrant stoppers. With specialized techniques for image analysis and interpretation, detection of small temperature differences is possible. However, other factors that affect the soil temperature, like water content, should be carefully considered and auxiliary methods (e.g. impedance measurements) are convenient to overcome limitations of the approach based on repetitive TIR images. Regular thermal imaging of the land in the vicinity of a pipeline, just after sunset, can reveal the different thermal properties attributable to the leakage. For automation of data interpretation, specialized computer software is needed, and the availability of frequent and regular images will increase the sensitivity of the data through the use of averaging techniques, enabling small differences in heat capacity of the soil to be detected, on a day-by-day basis.

The typical effect produced on vegetation by hydrocarbon contamination is a reduction of vigour, eventually leading to death (Mishra et al. 2012). Decay effects can be rapid or pervasive and depend on the type of vegetation community (i.e. grass, forest) affected by the polluter. Repetitive imagery is needed to identify this affection, as well as some kind of automated threshold of change providing the alert. For an early identification of affection, vegetation indices evaluating the contrast in reflectance in various EM wavelengths, e.g. the visible and near infrared are well suited. The presence of oil or gas in an area reduces the vegetation vigour, typically inducing a lowering of the vegetation index too. This effect can take some time if the leak is subtle, but could be rapid in the closest areas if there is a big leak. Repetitive multitemporal measurements of the visible and NIR wavelengths of a vegetated area can provide an indication of the health of plants, highlighting areas affected by the toxicity of oil and gas spills and leaks and helping to monitor their evolution.

Fine spectroscopy of 0.05–0.1 nm spectral resolution has proven to reliably identify the specific spectral signature of hydrocarbons (Brown and Fingas 2003; Lemke et al. 2005) and also the early effects of oil and gas pollution and contamination on vegetation (Brown and Fingas 2003). Emitted fluorescence (F) is directly linked to vegetation primary production (Frankenberg et al. 2011), and thus, it could be used as an early indicator of the health and status of vegetation. The spatial patterns of F provided by imaging spectrometers provide insights into photosynthesis and plant stress (Dobrowski et al. 2005). Estimates of F can be derived from ultraviolet active laser fluorosensors (e.g. Filippova et al. 1993), as well as from passive multispectral and hyperspectral radiance sensors (e.g. Zarco-Tejada et al. 2013). As other reflectance measures, the quantitative estimation of F from the air is complicated by the absorption of the atmosphere en route to the sensor, and approaches to deal with atmospheric effects have yet to be developed (Meroni et al. 2009). Importantly, laser fluorosensors are currently the most useful and reliable instruments to detect oil on various backgrounds, including water, soil, weeds, ice and snow (Brown and Fingas 2003). In fact, they are the only reliable sensors to detect oil in the presence of snow and ice (Brown and Fingas 1997; Jha et al. 2008) and they do not detect false positives.

Gas detection represents an unequivocal means (i.e. no false positives) for detection of specific gases (e.g. methane) from a certain distance, which works in day and night conditions. Despite gas diffusion and dispersion of gas contamination into the atmosphere, particularly in windy conditions, the highest concentration of gas is a reliable indicator of the leakage location (Allen et al. 2015).

Platforms

Small UAV platforms are typically grouped into two main categories: rotary wing UAVs and fixed-wing UAVs. The capacity for vertical take-off and landing (VTOL) as opposed to horizontal take-off and landing (HTOL) was a valid criterion for categorizing UAVs, until some fixed-wing airframes also acquired VTOL capacity. Fixed-wing UAVs have a relatively simple structure making them stable platforms easy to control during autonomous flights. Efficient aerodynamics enables longer flight duration and higher speeds, which make them ideal for applications such as aerial survey requiring the capture of geo-referenced imagery over large areas. On the down side, fixed-wing UAVs need to fly forward continuously and need space to both turn and land. These platforms are also dependent on a launcher (human or mechanical) or a runway to facilitate take-off and landing, which can have implications on the type of payloads they carry. Typical lightweight fixed-wing UAVs currently in the commercial arena have a flying wing design with wings spanning between 0.8 and 1.2 m and a very small fin at both ends of the wing. In-house vehicles tend to have slightly longer wings to enable carrying the required heavier sensors (Petrie 2013). A second type of design is the conventional fuselage, with dimensions around 1.2 to 1.4 m length for the fuselage and 1.6 to 2.8 m wing length.

Rotary wing aircraft (multicopter or multirotor) have more complex mechanics, which translates into lower speeds and shorter flight ranges. Amongst their strengths, rotary wing UAVs can fly vertically, take-off and land in a very small space, and can hover over a fixed position and at a given height. This makes rotary wing UAVs well suited for applications that require manoeuvring in tight spaces and the ability to focus on a single target for extended periods (e.g. facility inspections). On the down side, rotary wing UAVs are less stable than fixed-wing counterparts and also more difficult to control during flight. These platforms maintain directional control by varying blade pitch via a servo-actuated mechanical linkage. Single-rotor and coaxial rotor UAVs are typically radio-controlled, powered by electric motors, although some of the heaviest examples use petrol engines, and they require cyclic or collective pitch control. Multicopters, with an even number of rotors, utilize differential thrust management of the motor units to provide lift and directional control. As a general rule, the more rotors, the higher the payload they can take, and are functional in stronger wind conditions, as the redundant lift capacity provides for increased safety, and more control in the event of a rotor malfunction or failure. A few examples of UAV platforms searching a combined solution have already emerged, combining rotary and fixed-wing technologies (Cetinsoy et al. 2012), providing flying stability and manoeuvrability (e.g. Flying Wing, Songbird 1400).

Sensors on-board UAV for monitoring oil and gas pipelines

The essential difference between active and passive sensors originates from the source of energy illuminating the target objects (passive sensors rely on the sun, active sensors emit some kind of radiation themselves). This essential difference translates into missions with an active sensor requiring higher lifting and carrying capacity UAV platforms. The capacity to perform a particular task and to work under certain environmental conditions (e.g. topography, weather) is sensor dependent (Table 1). For instance, optical sensors measure radiation in the visible and infrared part of the EM spectrum relying on the sun for illumination; these sensors are suitable in daylight conditions, but even mounted on UAV flying at low altitude, they can be limited by clouds, haze or smoke. Multispectral (MS) sensors measure multiple spectral wavelengths simultaneously providing information that can be visually or automatically interpreted. For a given location, algebraic combinations of values in various spectral wave bands (e.g. vegetation indices) can be useful to detect environmental features, identifying plant stress, disease and nutrient or water status. As a chemical plant stressor, oil spills leave a spectral mark on the plant, that may be identified from the air with MS cameras on-board UAVs. Lightweight thermal still cameras and video have been adapted or specifically developed for use in UAVs (e.g. FLIR Vue); thermal sensors can provide data portraying thermal differences in space and time, revealing the presence of hydrocarbons (i.e. spill, leak) by a strong and increasing contrast in temperature. Video capture and processing enables operational inspections to take place in real-time, without the need to pause production or put ground crew in harm’s way.

The overall range of opportunities provided by optical sensors is constrained by issues concerning the digital frame cameras that can be deployed on lightweight UAVs. The weight and size of the camera, the type of lenses and the spatial resolution, and image footprint size relative to the available UAV payload are limited. Low flying altitudes in small-scale UAVs determine the need for high framing rates and large longitudinal and lateral overlapping of images. Furthermore, basic UAV configurations generate non-metric images, and exposure times are very short to help combat the effects of platform instability due to speed, roll, pitch and yaw.

Active sensors emit some kind of radiation measuring the fraction reflected by the target objects and the difference in time between emission and reception. Active sensors require power supplied by a source, adding weight to the aerial system which makes active equipment less versatile for use on UAVs than passive equipment. Lidar and RADAR sensors have been adapted for use in certain UAV configurations, reducing weight and using specific mechanics (e.g. gimbals). Still, the miniaturization of these active sensors is a remaining challenge. Lidar facilitates generation of very high-resolution (cm scale) surface models and accurate infrastructure 3D models, enabling identification of ground small-scale changes over time before they become a hazard and identification of irregularities in the infrastructure. Likewise, interferometric processing of synthetic aperture radar (SAR) data enables land subsidence mapping to the millimetre scale facilitating the identification of subsidence patterns long before a landslide or other disaster occurs (Bianchini et al. 2013); periodic SAR surveys constitute an alerting system to secure infrastructure like oil or gas pipelines.

For detection of gas (e.g. methane) presence, flux detectors originally designed for hand use (e.g. Laser Methane Copter (LMC), Pergam) are suitable to mount on UAVs. Moreover, other high precision gas detectors for UAVs are under development (Allen et al. 2015). Laser gas detectors beam radiation of a gas-specific wavelength (e.g. 1.65 nm for methane) and measure the backscattered radiation when part of it has been absorbed by the gas cloud. Differential absorption Lidar (DIAL) technology (Zirnig et al. 2005), currently operating on manned helicopters, beams and records light pulses of two different wavelengths—the measurement wavelength is absorbed by the gas (if present) whilst the reference wavelength is not. On-board UAVs, laser gas detectors can detect (Horn 2016) and provide accurate measures of gas leaks (Hodgkinson et al. 2006), although they have an inherent disadvantage of small sampling capacity (Bretschneider and Shetti 2015). Gas (methane) cameras enabling visualization of the gas presence might be useful for identification of pipeline leakages; the adaptation of these sensors to small-scale flying platforms has still to be realized. Not aiming to be exhaustive, Gómez and Green (2015) provide examples of commercial sensors from most of the techniques listed in Table 1 which are adapted to small UAVs.

Auxiliary equipment

To make a UAV mission successful, the aircraft and main on-board equipment (e.g. sensor) are supported by a series of systems and elements. Amongst the most relevant, supporting equipment include systems dedicated to position and navigation, to autonomous flight and for communications. Additionally, the need of elements for the launch, recovery and retrieval, and the mechanics and payloads are UAV and mission dependent. The authors briefly note them here, but for more details, a comprehensive review of commercial auxiliary equipment is compiled in Colomina and Molina (2014).

The positioning and navigation systems play a crucial role in UAV missions, since the location of the UAV has to be known and controlled at all times, be it by the remote operator, or by the autonomous pre-programmed flight planner. The quality of lightweight and compact GNSS equipment now available and capable of receiving signal from multiple satellite systems (e.g. GPS, Galileo, BeiDou) provides for the acquisition of high accuracy location information (Colomina and Molina 2014), especially when operated as differential GPS (DGPS), and facilitates all UAV navigation. For remote control (from the ground, air or sea), where non-autonomous operations are necessary, radar (Jang et al. 2015) or radio (Nitti et al. 2015) tracking solutions are required (Austin 2010). Basically, the radar tracking system fits a transponder to the aircraft that responds to a radar scanner emitting from the control system (CS) and enables the CS to control the aircraft position (bearing and range). With a radio tracking system, a signal carries data informing the CS of the aircraft bearing; the range is determined from the aircraft to CS signal travel time.

Autonomous capacity to both take off and land, as well as to fly along a pre-defined path with n-waypoints pre-programmed by the UAV operator, is desired where the survey is likely to be repeated at certain time intervals ensuring the repeat imagery covers exactly the same area. Likewise, the ability of the platform to navigate amongst obstacles in the flight path and the sense and avoid technologies are advantageous for safety and for systems expected to fly in enclosed spaces. Ideally, the control software (e.g. Mission Planner and APM Planner from Dronecode©) enables pre-programmed autonomous flight as well as manual control when the operator decides to modify the original plan. Existing and economic systems can be easily programmed and monitored with the aid of a smartphone or tablet.

The communication between small UAVs and the CS is usually through radio frequency, commonly in the 900 MHz and 1.2-, 2.4- and 5.8-GHz bands. Uplink transmission (i.e. CS to aircraft) consists of a flight plan, real-time flight-control commands, control commands to the different payloads and updated positional information. The downlink information (i.e. aircraft to CS) consists of the payload data (e.g. imagery), positional data and aircraft housekeeping data (e.g. battery or fuel state). Two different frequencies are necessary to keep the transmission of command information and sensor acquired data independently, avoiding interference. The complexity, weight and cost of the communication equipment are determined by the range of operation possible, the sophistication of the payload transmission and the need for security.

Fixed-wing vehicles require additional equipment to assist with launch and recovery. Launch equipment is typically an acceleration ramp with a trolley to provide the aircraft enough speed to sustain flight; the acceleration is provided by compressed air or by rocket. Recovery equipment can be a parachute installed within the aircraft, combined with a means to absorb the impact energy (e.g. airbag or an easily replaceable piece of material). For most UAV acquiring data, an important requirement is the ability to control the sensor pointing direction and to maintain the sensor orientation during the flight. Furthermore, securing the sensor on the front or underside of the UAV platform is crucial, because rotor vibration and gust instabilities very easily translate into blurred images and shaky videos (Bereska et al. 2013) particularly when flying too fast. Simple solutions for stabilization include damping the sensor (e.g. camera) with a mounting bracket and rubber mounts in between the UAV and the sensor; this will help in reducing or eliminating what is known as the ‘jello’ effect on video imagery. An elaborated solution with specific mechanics adapted to both the platform and sensors is the addition of a gimbal, a precision-engineered component that provides control of pan, tilt and yaw—in case of 3D gimbals.

Operational cases

In June 2014, the British Petroleum (BP) and AeroVironment Inc. (CA, USA) agreement to inspect the Prudhoe Bay (Alaska, USA) oil field area represented the first large-scale, government-approved commercial use of unmanned aircraft in the USA (case 1 in Table 2). AeroVironment’s UAS operators performed photogrammetric (visible and IR) and LiDAR data capture and analysis to monitor the Prudhoe Bay infrastructure, including the gravel roads (∼300 km), pipelines (∼1900 km) and a gravel pit, in search of deteriorated infrastructure and to identify areas vulnerable to flood. The hand-launched aircraft used was the Puma™ AE (Fig. 1a), a lithium-ion polymer battery platform with 2.8 m wingspan and 6.1 kg weight. Puma™ AE is capable of up to 3.5 h flight time per battery, is waterproof and has the ability to fly low (∼120–150 m above ground level) and slowly (<40 knots) with a maximum range of 20 km. This UAV configuration provided BP with highly accurate location maps to help manage the Prudhoe Bay oil complex. Soon after, on September 24, 2014, ConocoPhillips announced it had completed the country’s first commercial UAV flight off of Northwest Alaska in the Chuckchi Sea (case 2 in Table 2) with the aim to survey marine mammals and ice areas in the Arctic, something necessary to meet environmental and safety rules before drilling on the sea floor. The survey was performed with visible and IR cameras and video mounted on the ScanEagle® X200 UAV (Boeing Insitu) (Fig. 1b) which was launched from Fairweather’s Westward Wind research vessel during a week of flights. The ScanEagle® X200 is a waterproof fuel/gasoline engine vehicle with 3.1 m wingspan and ∼18 kg weight. ScanEagle® X200 is a long endurance (>24 h) platform with a maximum payload capacity of 3.4 kg. It is worth emphasizing that the Puma™ AE and ScanEagle® X200 are the first two UAVs approved by the US Federal Aviation Administration (FAA) for commercial applications. They both were given a restricted category type certificate, enabling operation in certain sectors of the Arctic area, at a maximum altitude of 600 m, 24 h a day for research and commercial purposes. This certification represents a precursor of open UAV commercial operations expected to be approved by US Congress.

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Aeronautics (a technological company established 1997 in Israel) employs the Aerostar® platform carrying an IR camera to patrol offshore oil fields for security in Angola (case 3 in Table 2). The Aerostar® is a 230-kg (MTOW) fuel powered platform, which can operate at the range of 250 km and can carry 50 kg of payloads. Aerostar is ideal for surveillance, and it has 12 h flight endurance and a slow loitering speed, allowing it to remain in the air the whole night long and inspect each rig thoroughly with a fully programmed flight path. Aerostar® is also employed to detect leaks in buried oil and water pipelines by repeatedly surveying the same area and applying differential thermal imaging.

In a different mission, British Petroleum employed the Aeryon Scout™ (Aeryon Labs Inc., Canada) carrying high-resolution visual and IR cameras to inspect pipelines for potential leaks in Alaska (case 4 in Table 2). The Aeryon Scout™ is a small battery-powered quadcopter (∼1.4 kg without payload) able to hover and fly close to the pipes in short missions of up to 25 min. It is particularly useful for observation from near ground level (flying altitude is below 150 m), with top and side pipeline flights. The flexibility of operations to carry on repetitive flies provides for a good change detection tool, and the thermal sensors on-board enable the detection of hotspots with thermal images.

Advantages and limitations of small UAVs for monitoring pipelines

Currently, the greatest limitation for general use of small UAVs lies in the absence of legislation and regulations to operate them in non-segregated airspace (i.e. sharing space with manned aircrafts) (Skrypietz 2012). Concerns supporting this restriction are related to safety, based on a UAV’s lack of on-board capacity to sense and avoid other aircraft. There are also claims of the need for specific air traffic management procedures. Other aspects still to resolve include problems of data protection and infringements on the right to privacy. Additionally, the safety of the technology and its potential for accidents are viewed with increasing scepticism. However, important efforts are being dedicated to develop sense and avoid technologies (Gökçe et al. 2015; Yu and Zhang 2015; Mcfadyen and Mejias 2016), and there is much interest to establish clear legislation. Other limitations of the UAV technology for monitoring infrastructure are technical aspects like the need for specialist expertise, the lack of standards and an insufficiently developed range of image analysis techniques (Kelcey and Lucieer 2012; Laliberte et al. 2011). Important investment and research efforts are currently made for fast development of means to overcome these limitations.

Regulations and legal issues

The capacity and responsibility to regulate UAVs rely on different bodies internationally (e.g. European Aviation Safety Agency (EASA) in Europe, FAA in the USA, Civil Aviation Administration (CAAC) in China, Directorate General of Civil Aviation (DGCA) in India). Whilst the FAA (FAA 2013) and the European RPAS Steering Group (ESRG 2013) have both a long-term plan to integrate remotely piloted aerial systems (RPAS) in non-segregated airspace in the USA and in Europe (Colomina and Molina 2014), the International Civil Aviation Organisation (ICAO) aims to coordinate global interoperability and harmonization of UAV rules (Hayes et al. 2014). ICAO has also an aim to ensure regularized civilian UAV flight by 2028 (European RPAS Steering Group (ERSG) 2013; Hayes et al. 2014). Lately, the rapid development of small UAV civil applications worldwide, coupled with a fair number of incidents and accidents concerning small UAVs and other airborne platforms, is triggering a growing call for tightening-up the rules and regulations for small UAV flights. Specific rules are being established for light vehicles worldwide; for instance, in Europe, the responsibility to regulate flights of vehicles below 150 kg concerns national authorities, and beyond national level a consortium funded by the European Commission, the Unmanned Aerial Systems in European Airspace (ULTRA) works to develop a master plan for the insertion of RPAS in the European air transport system. Progressively more countries require education and training of UAV operators and pilots. Despite differences in essential aspects concerning platform categories (EASA 2015), national rules typically impose some kind of restriction to fly the UAVs in certain localities (e.g. airports) and over certain heights (∼125–150 m) and distances (i.e. line of sight). Some sort of certification and insurance is also required for commercially based flights. In practice and to facilitate the legal use of UAV for civil applications respecting flight area restrictions, a number of free online mapping initiatives offer graphical information of restricted areas (e.g. Know before you fly (2016), B4Fly (FAA 2016), No fly drones (2016)), and some UAVs are pre-programmed with spatial information.

In 2007, a group of national authorities under the leadership of The Netherlands (the Joint Authorities for Rulemaking on Unmanned Systems—JARUS) joined in an effort to develop harmonized operational and technical regulations for UAS less than 150 kg. JARUS is open to participation from all civil aviation authorities and current participants are from European and non-European countries. JARUS focus on guidance and regulatory aspects (e.g. sense and avoid technology, command control and communication) and its primary outputs consist in recommended operational requirements and certification specifications.

In some countries, registration of civil UAV platforms has become necessary to fly small UAVs outdoors, and this practice will presumably be soon widely implemented. In the USA, UAV users unregistered with the FAA could face civil and criminal penalties. An online registration system went live on 21 December 2015 to register platforms weighting 0.25–25 kg. After 30 days of the system implementation, more than 300,000 owners had registered their platform (FAA 2015). Similarly, Russia’s State Duma approved regulations of the use of private UAV on December 22, 2015, introducing mandatory registration for all UAVs weighing more than 0.25 kg.

Beyond the impact on the airspace, the expansion of UAV applications will affect other industry areas whose regulations need adaptation. Radio frequencies for communication of UAV with the ground control system (CS) require sufficient band width. The International Telecommunication Union (ITU) has not yet allocated such bandwidth to UAVs, and they may have to use different radio frequencies in every country (Everaerts 2008), something to be considered by international operators and manufacturers.

Considerations for specifications of a small UAV system for monitoring oil and gas pipelines

Oil and gas pipelines monitoring scenarios

Based on monitoring needs of the oil and gas pipelines and the existing UAV technology (as outlined through this manuscript), in conclusion, the authors propose various monitoring scenarios where small UAVs can contribute and support the entire monitoring system. These scenarios (Table 5, Fig. 2) represent typical monitoring tasks and hypothetic examples of small UAV systems configuration, including basic characteristics of the platform and sensor. The authors provide examples of commercial vehicles and sensors to demonstrate the actual feasibility of the mission proposed, but other solutions are of course possible.

Table 5

Proposed scenarios for monitoring oil and gas pipelines with small UAV systems

Scenario 1: Proximity Survey/visual identification of pipe damage	Flying altitude	Very low (<50 m)
	Payload	<7 kg
	Endurance	<1 h
	Platform	Multicopter with hovering capacity and high manoeuvrability
	Sensor	High-resolution video camera with on the fly transmission
Scenario 2: Short Distance Survey/visual identification of leak	Altitude	Low (∼100 m)
	Payload	<25 kg
	Endurance	<1 to 5–6 h (depending on pipeline length)
	Platform	Fixed wing/rotary wing
	Sensor	Optical or IR camera, lidar
Scenario 3: Long Distance Survey/automatic sensing of soil properties	Altitude	Medium (1000 m)
	Payload	200 kg
	Endurance	30 h
	Platform	Medium size long endurance
	Sensor	Multisensor: radar, lidar, multispectral camera

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