Patent Grant
8362429
Oil spill detection methods can be broadly classified into global or local. Global detection schemes are typically satellite based (e.g., Landsat program managed by NASA and U.S. Geological Survey). Satellite systems perform large scale surveys; their primary limitations are low spatial resolution, low sampling rate and dependency on cloud cover. Local detection methods comprise of many different schemes including airborne (e.g., Light Detection and Ranging (LIDAR)) and shipboard (e.g., microwave radar) monitoring systems. Shipboard and airborne systems are capable of providing higher resolution than satellite based systems, but are not ideal for permanent monitoring applications. Therefore, such systems are designed as mobile units.
The current system for monitoring oil seeps from unmanned offshore platforms in the Gulf of Mexico includes daytime, fair-weather helicopter sorties. It is desirable to reduce the number of helicopter sorties, providing a fixed monitoring system that transmits the sensor data streams (e.g., image stream, video stream, etc.) via a wireless network to a manned platform where the data is processed. It is further desirable that an automated alert is generated when an oil spill occurs and the operator is notified such that upon further investigation if the alert is deemed to be genuine, a helicopter may be dispatched to the platform for a thorough on-site investigation. It is further desirable for the system to run 24/7 in all weather conditions to improve over current methodology, both in regularity and safety.
Thermal imaging was originally developed for military applications. The first practical barium strontium titanate (BST) ferroelectric infrared detectors (by Raytheon) and vanadium oxide (VOx) microbolometers (by Honeywell) became available for non-military commercial applications only recently in the 1990s. Thermal imaging is utilized in many industrial applications, as well as security, firefighting, and law enforcement. An advantage of thermal imaging is its nighttime capability without artificial illumination.
Previously mentioned mobile units have high power consumption and unreliable network connectivity. This aspect is addressed more fully in a related patent application Ser. No. 11/648,089 filed Dec. 29, 2006, which is hereby incorporated by reference. Related patent application Ser. No. 11/648,089 entitled “Method and Apparatus for Evaluating Data Associated with an Offshore Energy Platform”, in one or more embodiments, describes a system for transmitting data from an unmanned offshore energy platform to a manned offshore energy platform via a wireless network powered by solar panels, wind turbines, and other alternative energy generation schemes.
In general, in one aspect, the invention relates to a method for detecting the presence of hydrocarbons near an unmanned offshore oil platform. The method steps include monitoring reflected atmospheric and thermal radiation, detecting the presence of hydrocarbons, and generating an alert based on the presence of hydrocarbons.
In general, in one aspect, the invention relates to a method for detecting presence of hydrocarbons on a surface. The method steps include monitoring surface emission from the surface in an infrared band, providing a model for modeling emissivity contrast of the surface emission, wherein the emissivity contrast is induced by the presence of hydrocarbons on the surface, detecting the presence of hydrocarbons from the surface emission based on the model, and generating an alert based on the presence of hydrocarbons.
In general, in one aspect, the invention relates to a system for detecting presence of hydrocarbons on a surface. The system includes a plurality of sensors for monitoring reflected atmospheric radiation and surface emission from the surface, and a memory and a processor, embodying instructions stored in the memory and executable by the processor, the instructions comprising functionality to detect the presence of hydrocarbons based on the reflected atmospheric radiation and the surface emission according to a decision tree, where the decision tree is based on a model for modeling radiance contrast of the reflected atmospheric radiation and the surface emission in at least one selected from a group consisting of daytime condition, nighttime condition, and pre-determined weather condition, wherein the radiance contrast is induced by the presence of hydrocarbons on the surface and comprises at least one selected from a group consisting of reflection contrast, temperature contrast, and emissivity contrast, and generate an alert based on the presence of hydrocarbons.
In general, in one aspect, the invention relates to a system for detecting presence of hydrocarbons on a surface. The system includes a plurality of sensors for monitoring surface emission from the surface in an infrared band, and a memory and a processor, embodying instructions stored in the memory and executable by the processor, the instructions comprising functionality to detect the presence of hydrocarbons based on the surface emission using a model for modeling emissivity contrast of the surface emission, wherein the emissivity contrast is induced by the presence of hydrocarbons on the surface and comprises at least one selected from a group consisting of temperature contrast, spectral contrast and thickness contrast, and generate an alert based on the presence of hydrocarbons.
Other aspects and advantages of the invention will be apparent from the following description and the appended claims.
So that the above recited features and advantages of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the embodiments thereof that are illustrated in the appended drawings. It is to be noted, however, that the appended drawings illustrate only typical embodiments of this invention and are therefore not to be considered limiting of its scope, for the invention may admit to other equally effective embodiments.
Specific embodiments of the invention will now be described in detail with reference to the accompanying Figures. Like elements in the various Figures are denoted by like reference numerals for consistency.
In the following detailed description of embodiments of the invention, numerous specific details are set forth in order to provide a more thorough understanding of the invention. However, it will be apparent to one of ordinary skill in the art that the invention may be practiced without these specific details. In other instances, well-known features have not been described in detail to avoid unnecessarily complicating the description.
In general, embodiments of the invention provide a system and method for detecting presence of hydrocarbon on a surface. In one or more embodiments of the invention, oil spills from unmanned offshore platforms are permanently monitored using a combination of sensors (e.g., thermal, electromagnetic, chemical, etc.) to detect hydrocarbon films that appear on the surface of the water in the vicinity of platform.
The present invention provides monitoring capability during an entire 24 hour/7 days a week period. In one embodiment, the video streams from two cameras (e.g., for visible and long-wave-infrared (LWIR) bands) are transmitted via a wireless network to a manned platform as described in the related patent application Ser. No. 11/648,089, entitled “Method and Apparatus for Evaluating Data Associated with an Offshore Energy Platform” and filed Dec. 29, 2006, and incorporated by reference above. The data streams are then analyzed by the acquisition software on the host computer located on the manned platform.
In one embodiment, a LWIR (with nominally 7-14 μM wavelength, also known as far-infrared (far-IR or FIR)) thermal imager (sensor) provides a video stream, which is monitored by an operator at a remote location. A series of image processing operations are performed on individual frames from the video stream and an automated alert is triggered when a spill occurs to notify the operator. Additionally, data streams from a variety of other sensors, including but not limited to a visible camera with night vision capability, RF sensors, chemical sensors, Raman sensors, and fluorescence sensors, may be configured to provide additional cross checks on the alerts generated by the LWIR camera.
Image contrast C(Δλ, θ) occurs when there is a difference in the surface radiance L(Δλ, θ) between the oil covered surface and the native water surface as shown in Equation 1 below.
C(Δλ,θ)=Tr(d,Δλ)[LOil(Δλ,θ)=LWater(Δλ,θ)] Equation 1
Δλ is the wavelength band. θ is the detection angle. Tr(d, Δλ) is the transmission through the atmosphere. LOil(Δλ, θ) and LWater(Δλ, θ) are the surface radiance for the oil covered surface and the native water surface, respectively.
The surface radiance depends on reflected atmospheric radiation and surface emission as shown in Equation below.
L(Δλ,θ)=R(Δλ,θ)I(Δλ,θ)+ε(Δλ,θ)B(Δλ,θ) Equation 2
T is the temperature. L(Δλ, θ) is the surface radiance for the oil covered surface or the native water surface. R(Δλ, θ) is the reflectivity of the surface. I(Δλ, θ) is the intensity of the incident radiation (e.g., of the atmosphere). ε(Δλ, θ) is the emissivity of the surface. B(Δλ, t) is the thermal emission due to the Planck function as shown in Equation 3 below.
B(Δλ,θ)=C1λ−5[exp(C2/λT)−1]−1 Equation 3
The Planck function, also referred to as the black body radiation function, represents the maximum amount of radiation that a material can emit at a given temperature and wavelength. The emissivity ε(Δλ, θ) is defined as the ratio of emitted radiation to black body radiation.
Radiometric temperature is defined as the temperature T at which a black-body described by Equation 3 would yield an equivalent amount of emission over a band Δλ as the actual emission from a material measured by a sensor with an effective bandpass Δλ. Both higher physical temperature and higher emissivity of the material contribute to higher surface radiance therefore higher radiometric temperature. That is, a difference in the emissivity of two materials in thermal equilibrium results in an apparent radiometric temperature difference.
When an oil film (e.g., (120)) appears on a body of water (e.g., (121)) due to spillage, the emissivity difference between oil and water results in a radiometric temperature difference even if the oil film is in thermal equilibrium with the water. In certain conditions, the radiometric temperature difference may be approximately 1K in the IR range of the electromagnetic spectrum. In one or more embodiments of the invention, a thermal imaging camera with sensitivity better than 0.1K is able to detect image contrast between the oil covered surface and the native water surface at nighttime even without the contribution from reflected atmospheric radiation. In one or more embodiments of the invention, the detection region of IR imaging cameras utilizing either BST or VOx type sensors (nominally 8-14 microns) covers terrestrial radiation whose Planck distribution peaking at nominally 10 microns. Accordingly, the un-cooled detector having a thermal sensitivity of better than about 0.1K may be used to detect image contrast of the oil covered surface and the native water surface in the LWIR bands.
Because oil is a better absorber than water in the 8-14 micron wavelength band, differential heating from atmospheric radiation (e.g., solar radiation) causes the temperature of the oil film to rise higher relative to the surrounding water during daytime. In one or more embodiments of the invention, differential heating from incident atmospheric radiation of oil relative to water further increases the image contrast at daytime. In one or more embodiments of the invention, surface radiance contrasts from fluorescence and Raman scattering may also be induced by irradiating water surface from a source of electromagnetic radiation. This external radiation may be continuous or at discrete times to induce continuous or time gated radiance detection accordingly.
Generally speaking, image classification pertains to the adoption of decision rules for sorting pixels into classes. For example, images may be automatically categorized into classes (or themes) based on all pixels in each image. These may be performed based on either parametric methods using statistical parameters (e.g., mean and standard deviation of pixel distribution) or non-parametric methods to detect objects (e.g., polygons) in the feature space. These methods have been adopted in the remote sensing community, for example for classification of Landsat images into water, vegetation types, terrain types, etc. In one or more embodiments of the invention, image classification based on parametric method and/or non-parametric method may be applied using multiple spectral bands (or wavelength bands, e.g., visible, NIR, LWIR, etc.) for oil spill detection, as described below.
In accordance with the present invention,
As shown in
In one or more embodiments of the invention, frame grabs are periodically initiated by the data acquisition system, regardless of whether an operator is present to monitor the video stream. In accordance with the present invention,
In one or more embodiments of the invention, the histogram (B8) may be multi-dimensional based on pixel data obtained from multiple spectral bands (e.g., NIR, FIR, visible, etc.). Exemplary histograms with one to four resolved peaks are shown in detail in
As shown in
In one or more embodiments of the invention, the image processing operations described above may be performed in the background if an operator is available to actively monitor the video streams (A7) form both cameras, as shown in
In accordance with one embodiment, data stream (e.g., image stream, video stream, etc.) from each sensor is monitored for hydrocarbon presence in a series of decision boxes C7. Each sensor response may be compared with any other sensor response. For example, the VIS and LWIR image streams are compared in comparison box C8 as both cameras may detect contrast between oil covered water surface and native water surface during daylight. In another example, the VIS, LWIR, and Fluorescence image streams are compared in comparison box C9. If image streams from C1 and/or C2 indicate hydrocarbon presence, then the monitored area in question may be irradiated with a UV light source L1 for the response recorded by D1 to be considered in conjunction with the responses from C1 and/or C2 in the comparison box C9. In yet another example, if the Raman signal obtained by C4 from water is attenuated indicating the possible presence of hydrocarbons, image streams from sensors C1, C2, and C3 are cross validated with the Raman signal from C4 in the comparison box C10.
Generally speaking, different sensors perform differently under various environmental conditions (e.g., daytime condition, nighttime condition, various weather conditions, etc.). In one or more embodiments of the invention, measured radiance contrast from each sensor channel (i.e., each camera and associated processing resource as depicted in
In one or more embodiments of the invention, the decision tree depicted in
The automated alert system described above is advantageous for a number of reasons. As a first matter, helicopter flights are both expensive and dangerous. Secondly, a remote 24/7 monitoring system allows for improved detection frequency and reliability. The combination of a number of different sensor image streams reduces the number of false detections to an acceptable level. As a result, a helicopter needs to be dispatched to the platform following careful review of the alert history by an operator.
Initially, reflected atmospheric radiation (e.g., from solar illumination, atmospheric scattering, etc.) and surface emission (e.g., thermal emission) are monitored from the surface (e.g., water surface) (Step 700). In one or more embodiments, the monitoring maybe performed using multiple sensor channels (e.g., a visible camera, a NIR camera, a FIR or LWIR camera, etc.), as described with respect to
As described with respect to Equations 1-3 above, radiance contrast may be induced by the presence of hydrocarbons on the surface and may include reflection contrast, temperature contrast, emissivity contrast, or contrast based on other physical mechanisms. In Step 702, a model is provided for modeling the radiance contrast of the reflected atmospheric radiation and the surface emission. In one or more embodiments of the invention, the model is provided for modeling radiance contrast in daytime condition, nighttime condition, and/or other pre-determined weather conditions. In one or more embodiments of the invention, the model models measured radiance contrast obtained using an automated system of
In Step 704, a decision tree is defined based on the radiance contrast model to guide a workflow for detecting the presence of hydrocarbons (e.g., oil spill on the water surface). In one or more embodiments of the invention, the decision tree includes multiple sensor channels, decision boxes, comparison boxes, and alarm generation module, such as the decision tree depicted in
In Step 706, the model is calibrated without the presence of hydrocarbons in a calibration phase to generate historical data, which may be used as references in the decision tree in a subsequent monitoring phase. For example, an image stream from any of the sensors C1-C4 during monitoring phase may be compared to the historical data in a corresponding decision box of C7.
In one or more embodiments of the invention, the historical data may include statistics of images (e.g., mean and standard deviation of pixel intensity in a histogram of the images) obtained from monitoring the reflected atmospheric radiation and the surface emission without the presence of hydrocarbons. In one or more embodiments of the invention, the image stream obtained during calibration is classified based on a parametric classification method, i.e., by comparing the associated statistics to generate the historical data. In one or more embodiments of the invention, historical data may further include objects identified from the statistics based on rule based classification. For example, statistics derived from known images (e.g., a portion of the platform, a moving kelp bed, etc.) within the monitoring area during the calibration phase may be identified as a known object based on a heuristic rule. Furthermore, statistics derived from known images of oil film with known thickness and composition may also be identified as a known object to be included in the historical data based on the heuristic rule. For example,
In Step 708, an image stream is obtained from monitoring the reflected atmospheric radiation and the surface emission, for example using any of the sensors C1-C4. A statistical diagram (e.g., a histogram) may then be generated from the image stream (Step 710). In one or more embodiments of the invention, multiple image streams may be obtained from multiple sensors to generate a multi-dimensional statistical diagram (e.g., any one of the multi-dimensional histograms depicted in
In Step 712, clusters (e.g., clusters of dots depicted in
In Step 714, the presence of hydrocarbons is detected based on the clusters according to the decision tree described above. In one or more embodiments of the invention, the presence of hydrocarbons is detected by comparing the clusters to historical data generated during a calibration phase. In one or more embodiments of the invention, the hydrocarbon detection is validated by comparing the clusters to historical data (Step 716). In one or more embodiments of the invention, the comparison is performed by comparing statistics (e.g., means and standard deviation of a histogram) of the clusters and historical data in a parametric classification method. In one or more embodiments of the invention, the comparison is performed by comparing the clusters to objects (e.g., corresponding to known images such as portions of the platform, moving kelp bed, oil film with known thickness and composition, etc.) in the historical data using a rule based classification method.
In one or more embodiments of the invention, the surface may be irradiated using ultraviolet source, visible light source, and/or infrared source to improve the radiance contrast. In one or more embodiments of the invention, the surface may be irradiated using ultraviolet source and/or visible light source to generate fluorescence response. In one or more embodiments of the invention, the surface may be irradiated using ultraviolet source and/or visible light source to generate Raman signal.
In one or more embodiments of the invention, an area associated with the presence of the hydrocarbon on the surface is calculated and tracked for generating an alert based on the area exceeding a pre-determined threshold.
As described above, the radiance contrast model may include capabilities for modeling reflection contrast, temperature contrast, emissivity contrast, or suitable contrast based on other physical mechanisms.
In LWIR remote sensing, total radiance collected by a detector (or sensor) has four possible components: (a) emission from materials (e.g., air or smoke between the monitored surface and the detector) within the line of sight of the detector, (b) surface emission (e.g., thermal emission), (c) direct solar illumination, and (d) reflected sky radiance. Terms (a) and (c) can be neglected for short-range applications without directing the detector toward the sun. Thus, the radiance difference (ΔL) due to thermal emission (i.e., temperature contrast) between oil covered surface and native water surfaces can be described using Equation 4 below.
ΔL=Δεoil/water-waterΔBwater-sky+εoil/waterΔBoil/water-water Equation 4
Δεoil/water-water is the emissivity difference between the oil covered surface and the native water surface. ΔBwater-sky is the black body radiation difference between the native water surface and the sky. εoil/water is the emissivity of the oil covered water surface. ΔBoil/water-water is the black body radiation difference between the oil covered water surface and the native water surface. Note that Equation 4 is applicable to monochromatic radiation as well as polychromatic radiation as long as proper integration over a wavelength band is carried out.
Using the differences in black body radiation (i.e., ΔBwater-sky and ΔBoil/water-water) as independent variables, exemplary detection boundaries due to detector sensitivity are delineated in
In most practical conditions, both ΔBwater-sky and ΔBoil/water-water are positive, therefore the upper right quadrant of
In one or more embodiments of the invention, the thickness dependent emissivity model is described by Equations 5-7 below. Starting from radiative transfer theory, total emitted energy of oil covered water surface (e.g. (120) over (121) as depicted in
where r and t are the interfacial amplitude reflectivity and transmissivity, respectively. Subscripts ij denotes the direction of wave propagation from medium i to j where 1 represents air, 2 represents oil, and 3 represents water. {circumflex over (n)}i is the complex refractive index of medium i. θ2 is the angle of refraction in the film. By definition, the emissivity contributed by the film is obtained by the ratio of the Poynting vectors of the emitted intensity to the original intensity as shown in Equation 6 below.
Similarly, the emission from the water and the equivalent partial emissivity can be calculated as shown in Equation 7 below.
The total emissivity can then be calculated by summing the individual contributions from the oil and the water. It can be seen that as the film thickness approaches zero, the emissivity is contributed entirely by the underlying water, while on the other extreme all by the oil film.
Furthermore, since the oil film is unlikely to be spatially uniform in thickness, a detection mechanism similar to detecting the spectral contrast may be devised to detect spatial variations in the oil film thickness. As shown in
It is important to note that the spectral and thickness contrasts are effective detection mechanisms under all environmental conditions, for example day/night, warm/cold sky, and with/without differential heating. In one or more embodiments of the invention, the radiance contrast model models the spectral contrast and the thickness contrast for environmental conditions, including daytime, nighttime, warm sky, cold sky, with differential heating, and without differential heating.
Initially, surface emission (e.g., thermal emission) from a surface (e.g., water surface) is monitored in an infrared band (e.g., LWIR band) (Step 900).
In Step 902, a model is provided for modeling emissivity contrast of the surface emission. For example, the emissivity contrast may be induced by the presence of hydrocarbons on the water surface. In one or more embodiments of the invention, the model may be as described with respect to
In Step 904, the wavelength of the infrared band is adjusted to avoid the undetectable range to detect the hydrocarbon based on the temperature contrast. In one or more embodiments of the invention, the undetectable range is rotated in ΔBwater-sky/ΔBoil/water-water plane by adjusting the sub-wavelength channel according to the particular environmental condition (e.g., as depicted in
In one or more embodiments of the invention, the model comprises a positive range and a negative range for the temperature contrast (e.g., as depicted in
In one or more embodiments of the invention, the model comprises a wavelength dependent emissivity model (e.g., as depicted in
In one or more embodiments of the invention, the model comprises a thickness dependent emissivity model (e.g., as depicted in
In Step 912, the presence of hydrocarbon is detected from the surface emission based on the emissivity contrast model, for example by detecting the temperature contrast, the change in the temperature contrast, the spectral contrast, and/or the thickness contrast of the oil covered water surface with respect to the native water surface in a monitored area.
The invention has numerous advantages, such as, but not limited to those listed below. In one or more embodiments of the invention, the current invention provides an inexpensive, permanent monitoring sensory system by using thermal imaging capability that can be widely deployed over a wide geographic area. In one or more embodiments of the invention, the current invention overcomes other technical obstacles to deploying such systems in an offshore environment, including the lack of network infrastructure to convey the data from an unmanned platform to a manned platform and the lack of electrical power on most unmanned platforms.
It will be understood from the foregoing description that various modifications and changes may be made in the preferred and alternative embodiments of the present invention without departing from its true spirit. For example, sensors, image processing steps, decision tree workflow, radiance contrast model, and arrangement of the system may be selected or adjusted to achieve the desired detection. The method steps may be repeated according to the various configurations for different environmental conditions, and the results compared and/or analyzed. Although examples are given to describe oil spill detection, this detection technology may also be applied in hydrocarbon exploration, production, and refining.
This description is intended for purposes of illustration only and should not be construed in a limiting sense. The scope of this invention should be determined only by the language of the claims that follow. The term “comprising” within the claims is intended to mean “including at least” such that the recited listing of elements in a claim are an open group. “A,” “an” and other singular terms are intended to include the plural forms thereof unless specifically excluded.
The present application is a divisional application of and, thereby, claims priority under 35 U.S.C. 120 to U.S. application Ser. No. 12/188,141 filed Aug. 7, 2008, entitled, “METHOD AND APPARATUS FOR OIL SPILL DETECTION,” and incorporated herein by reference. U.S. application Ser. No. 12/188,141 claims benefit under 35 U.S.C. §119(e) of Provisional Patent Application No. 60/955,216 filed Aug. 10, 2007, which is hereby incorporated by reference.
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| Number | Date | Country | |
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| 20120095683 A1 | Apr 2012 | US |
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| Number | Date | Country | |
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| Parent | 12188141 | Aug 2008 | US |
| Child | 13286422 | US |
