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    • 专利摘要:
    method and system for collecting and analyzing operational information from a network of components associated with a liquid energy commodity. a method for collecting and analyzing operational information from a network of components associated with a liquid energy commodity comprises the steps of: (a) measuring an amount of the liquid energy commodity in storage at one or more storage facilities on the network, and store this measurement data; (b) determine a flow rate of the net energy commodity in one or more selected pipelines in the network, and store that flow rate data; and (c) check the operational status of one or more processing facilities on the network, and store that operational status information; (d) analyzing measurement data, flow rate data, and operational status information to determine a balance of the net energy commodity in the grid or a selected part of it at a given time; and (e) communicate information about the balance of the net energy commodity to a third market participant.
    公开号:BR112013021047B1
    申请号:R112013021047-8
    申请日:2012-02-16
    公开日:2021-03-30
    发明作者:Deirdre Alphenaar；Walter F. Jones；Abudi Zein
    申请人:Genscape Intangible Holding, Inc；
    IPC主号:
    专利说明:

    Cross Reference to Related Orders
    [001] The present application claims priority for U.S. Provisional Patent Application No. 61 / 443,510 filed on February 16, 2011, the description of which is incorporated herein by reference. Field of the Invention
    [002] The present invention is a method and system for collecting and analyzing operational information from a network of components associated with a liquid energy commodity, such as crude oil or liquid natural gas (LNG). Fundamentals of the Invention
    [003] Liquid energy commodities, such as crude oil, comprise a multi-billion dollar economic market. These commodities are bought and sold by many parties, and as with any traded market, information about traded commodities is very valuable to market participants. Specifically, the operations of the various components and installations of the production, transportation, storage and distribution systems for each of these commodities can have significant impacts on the price and availability of these commodities, making the information on said operations valuable. In addition, such information is generally not publicly described by the various component owners or operators, and access to said information is then limited.
    [004] Certain data is collected by organizations such as the United States Energy Information Administration (“EIA”), typically via surveys of selected owners and / or operators. However, the amount of time required to collect and compile this data and then disseminate it to the public or market participants can be in the range of days to months, so the data collected and compiled is generally delayed and of limited value for purposes. short-term trading. Summary of the Invention
    [005] The present invention is a method and system for collecting and analyzing operational information from a network of components associated with a liquid energy commodity, such as crude oil or liquid natural gas (LNG).
    [006] According to the method and system of the present invention, sensors or measuring devices are implanted at various points in a network to collect data. The method then generally comprises the steps of: (a) measuring a quantity of liquid energy commodities in storage at one or more storage facilities on the network, and storing this measurement data in a first database; (b) determine a flow rate of liquid energy commodities in one or more selected pipes in the network, and store these flow rate data in a second database at the central data processing facility; and (c) checking the operational status of one or more processing facilities on the network, and storing that operational status information in a third database at the central data processing facility; (d) analyzing measurement data, flow rate data, and operational status information to determine a balance of liquid energy commodities in the network or a selected part of it at a given time; and (e) communicating information about the balance of liquid energy commodities to a third party in the market.
    [007] With respect to storage facilities, in each storage facility selected in a particular network, there is a measurement of the amount of crude oil or other liquid energy commodities in storage. For example, most crude oil is stored in large above-ground tanks that either have: a floating roof, which is known as the External Floating Roof (EFR); or a fixed roof with a floating roof inside the tank, which is known as the Internal Floating Roof (IFR). Thus, each tank in a particular location can be searched using publicly available resources or visual inspection, and all relevant information about each tank, including volumetric capacity information, type of tank (ie, floating roof or fixed roof), and physical dimensions, it is stored in a database. Then, on a predetermined schedule, an inspection of each tank at the particular location that is conducted includes the collection of one or more photographic images (ie, visible spectrum) or video from each tank, and / or the collection of infrared images or video of each tank. The photographic images collected and the infrared images collected from each tank are then transmitted to a central processing facility for analysis to obtain a measurement of the amount of crude oil or other liquid energy commodities in storage.
    [008] With respect to pipelines, in order to maintain the pressure of liquid energy commodities, pumping stations are positioned along pipelines. The pumps used in each of these pumping stations are typically electrically driven induction motors. In order to perform a remote determination of the quantity and flow rate in a particular pipe at a given time, a preferred form of analysis is based on monitoring the electrical energy consumption in real time from any number of pumping stations. along the selected pipe. In an exemplified implementation, a monitoring device is deployed and used to monitor one or more power lines supplying electrical energy to each selected pumping station. The monitoring device is mainly comprised of sensing elements responsive to the electrical potential and the magnetic flux densities associated with one or more power lines, thus allowing periodic or continuous measurements of the electrical potential and the magnetic flux densities associated with one or more power lines, and thus an energy determination. The data from such monitoring devices is then transmitted to the central data processing facility. In the central data processing facility, a model of the pipelines and pumping stations in the particular network that is developed includes computations of the gain or loss of elevation between any monitored pumping station and the next pumping station downstream using standard geographic elevation data. The pressure differential between the outlet or discharge side of any particular monitored pumping station and the inlet side of the next downstream station is then estimated. A range of possible flow rates for the pipeline from the minimum possible flow to the maximum possible flow to the pipeline is plotted versus the equivalent expected energy consumption at the monitored pumping station.
    [009] Since such energy consumption determinations have been made for any particular pumping station, the changes in energy at each pumping station can be correlated with changes in flow through each pumping station. Thus, as the monitoring devices described above allow periodic or continuous measurements of the energy consumed at a particular pumping station, the data collected from these monitoring devices can be used to determine the flow through and between the pumping stations.
    [010] With respect to processing facilities, a liquid energy commodity enters a refinery or other processing facility somewhere in the network. In the method and system of the present invention, the operational status of such processing facilities is verified. A preferred method for monitoring the operation of processing facilities is to use fixed thermal imaging cameras. A thermal imaging camera may require thermal data and recorded images of emissions and thermal signatures from several key units that can be used to verify that a processing facility is performing as expected or not.
    [011] With data and information on the three fundamental components of a particular network - (i) storage facilities, (ii) pipelines, and (iii) processing facilities - it is possible to determine the “total balances” of liquid energy commodities . For example, the "balances" of interest to market participants with respect to crude oil include, but are not limited to: the amount of crude oil in storage in a given market region at a given time; the amount of crude oil flowing to a market region from adjacent market regions; and / or the amount of crude oil being processed into gasoline and other petroleum products. Once such an analysis has been completed, information on the balance of crude oil or other liquid energy commodities in the network can be communicated to market participants and other interested parties, that is, third parties who would not normally have immediate access to such information. Brief Description of Drawings
    [012] FIG. 1 is a schematic view of an exemplified network associated with the production of crude oil.
    [013] FIG. 2 is a schematic view of an exemplified network associated with the transportation and processing of crude oil.
    [014] FIG. 3 is an exemplified image in which the outline of three tanks was found and marked on the image using the Sobel edge detection method.
    [015] FIG. 4 is an exemplified image with the outline of three tanks of FIG. 3 overlaid on a subsequent collected image.
    [016] FIG. 5 is a graph that illustrates the one-dimensional shape of a border in an image.
    [017] FIG. 6 includes a pair of 3x3 convolution masks used in a Sobel edge detection method.
    [018] FIG. 7 illustrates how a convolution mask in a Sobel edge detection method is applied to an input image.
    [019] FIG. 8 is a flow rate graph (barrels per day) against expected energy consumption (MW) for an exemplary pumping station.
    [020] FIGs. 9 (a) - (d) are a series of thermal images that illustrate a reduction in the production volume (“ramp-down”) of a fluid catalytic cracking unit in a refinery.
    [021] FIG. 10 illustrates a storage core that is connected to three pipes.
    [022] FIG. 11 is a graph that illustrates how directly measured data is fitted using a standard regression mathematical model to historical data.
    [023] FIG. 12 is a schematic view of another exemplified network associated with the production of crude oil.
    [024] FIG. 13 is a flow chart representing the general functionality of an implementation of the method and system of the present invention in conjunction with the exemplified network of FIG. 12.
    [025] FIG. 14 is a schematic representation of the core components in an exemplified implementation of the method and system of the present invention. Detailed Description of the Invention
    [026] The present invention is a method and system for collecting and analyzing operational information from a network of components associated with liquid energy commodities, such as crude oil or liquid natural gas (LNG).
    [027] For example, as crude oil is a fossil fuel, it is typically drilled or extracted in locations where deposits or reservoirs are naturally occurring. Once collected in such a location (for example, from a well), the crude oil is typically pumped directly into a pipe or stored in above-ground storage (for example, tanks) or underground storage (for example, dome caves) saline). From such storage facilities, it can then be transported via pipelines to refineries or other processing facilities for processing. Thus, there is an interconnected network of crude oil wells, crude oil pipelines, crude oil storage facilities, and crude oil refineries.
    [028] For another example, natural gas is extracted in locations where there are naturally occurring reservoirs. The extracted natural gas is then processed into "dry" natural gas or "wet" natural gas in processing plants, the latter of which is called liquid natural gas (LNG). LNG is then transported using LNG pipelines and stored at LNG storage sites. LNG can then be separated into what are called "purity" products such as ethanol, propane and butane. These LNG products can then be processed in ethylene cracking facilities that obtain NGL products and process them into raw materials for the petrochemical industry such as ethylene, propylene, etc.
    [029] According to the method and system of the present invention, sensors or measuring devices are implanted at various points in a network to collect data. The method then generally comprises the steps of: (a) measuring a quantity of liquid energy commodities in storage at one or more storage facilities on the network, and storing that measurement data in a first database at a processing facility. central data; (b) determining a flow rate for liquid energy commodities in one or more selected pipelines in the network, and storing that flow rate data in a second database at the central data processing facility; and (c) checking the operational status of one or more processing facilities on the network, and storing that operational status information in a third database at the central data processing facility; (d) analyzing measurement data, flow rate data, and operational status information to determine a net energy commodity balance on the grid or a selected part of it at a given time; and (e) communicating information about the balance of net energy commodities to a third market participant.
    [030] As will be clear from the description that follows, many of the operational steps of the method and system of the present invention, including data collection and the various computational steps associated with the analysis of this collected data, are preferably achieved through use of a digital computer program, that is, computer-readable instructions stored and executed by a computer. Thus, the execution of the required routines and subroutines can be done using standard programming techniques and languages. With the benefit of the following description, such programming is readily achieved by one skilled in the art.
    [031] For example, with respect to crude oil, there is an interconnected network of crude oil wells, crude oil pipelines, crude oil storage facilities, and crude oil refineries. For purposes of the subsequent discussion, and as shown in FIGs. 1 and 2, a “net” for crude oil can thus be characterized as having three fundamental components: (i) crude oil storage facilities; (ii) crude oil pipes; and (iii) crude oil refineries or other processing facilities. Understanding and collecting information about the operation of these components and the flow of crude oil between these components allows network modeling and monitoring network dynamics in real time. In other words, by obtaining certain physical measurements of crude oil (or other liquid energy commodities) at various points in the network, it is possible to determine the total “balances” of crude oil in different functional parts of the network. For example, the "balances" of interest to market participants with respect to crude oil include, but are not limited to: the amount of crude oil in storage in a given market region at a given time; the amount of crude oil flowing to a market region from adjacent market regions; and / or the amount of crude oil being processed into gasoline and other petroleum products.
    [032] Still in relation to the crude oil market, in the United States, the amount of crude oil stored in tanks located at or terminals, storage cores, or oil refineries (including crude oil in transit in pipes) 340 million barrels. 88,514 km (55,000 miles) of pipelines transport crude oil from American production wells (notably in the states of Texas, Louisiana, Oklahoma, and Wyoming), import terminals (notably seaports in the Gulf of Mexico), or Overland across the Canadian border to various regional markets. These markets are divided into five large regions in the United States known as the Petroleum Administration for Defense (PAD) Districts. Crude oil pipes typically range in diameter from 20 to 76.20 cm (eight to thirty inches). Larger interregional pipelines, serving refineries or storage centers, are generally more relevant to general market dynamics than smaller intraregional pipelines. The relevance of the crude oil storage market varies depending on the purpose of the crude oil being stored. For example, crude oil stored in refineries is available for refinement at any particular point in time in gasoline and / or other petroleum products. Crude oil stored in larger oil storage cores may be indicative of the amount of crude oil being stored by financial speculators or suppliers to refineries downstream of the storage core. Other tank storages can mainly be used to maintain appropriate pressures and volumes in order to successfully operate the required flow dynamics in a particular pipeline. Storage Facilities
    [033] According to the method and system of the present invention, in each storage facility selected in a particular network, there is a measurement of the amount of crude oil or other liquid energy commodities in storage. For example, a preferred method for measuring the amount of crude oil being stored in a particular tank is described in the US patent application copending and assigned to the same assignee No. 13 / 089,674 entitled “Method and System for Determining an Amount of Crude Oil Stored in a Particular Location ”, which is incorporated here by reference.
    [034] As described in US Patent Application No. 13 / 089,674, most of the crude oil is stored in large above-ground tanks that either have: a floating roof, which is known as an External Floating Roof (EFR) ; or a fixed roof with a floating roof inside the tank, which is known as the Internal Floating Roof (IFR). Thus, each tank in a particular location can be searched using publicly available resources or visual inspection, and all relevant information about each tank, including information on volumetric capacity, type of tank (ie, floating roof or fixed roof), and physical dimensions, is stored in a database. Then, on a predetermined schedule, an inspection of each tan in the particular location that is conducted includes the collection of one or more photographic images (ie, visible spectrum) or video from each tank, and / or the collection of infrared images or video of each tank. Such images can be collected by aerial means, through the use of fixed cameras located on the ground, or by satellite image. In the case of an aerial image acquisition, such as a helicopter flyover, the helicopter preferably flies a defined and repeated flight path and adheres to a predefined sequence for image acquisition, which facilitates the subsequent analysis. Alternatively, thermal imaging cameras can take infrared images at predetermined intervals. In any case, the photographic images collected and the infrared images collected from each tan-which are then transmitted to a central processing facility for analysis.
    [035] Regarding the analysis of a tank with a floating roof, a preferential form of analysis is to determine the height of the roof in relation to the top of the selected tank using the techniques for determining the number of image pixels to dron. For example, tank levels can be measured by drawing two vertical lines, such as L1 and L2. When measuring tank levels for floating roof tanks, the L1 line is drawn inside the tank from the top of the tank down to the top of the lid and approaches the height of the roof that has been lowered. The L2 line is drawn on the outside of the tank from the top of the tan-down to the bottom of the tank and approximates the height of the tank. The respective lengths of lines L1 and L2 are then measured. Such measurement is optimized, for example, by ensuring an appropriate camera angle and distance from the tank, using high resolution equipment for image acquisition, and / or ensuring consistent and appropriate location of the L1 and L2 lines in the image. .
    [036] Based on the determined ceiling height (which is indicative of the liquid level) and the stored volumetric capacity information and / or the stored physical dimensions of the selected tank, the amount of crude oil in the tank can be calculated. For example, if the roof is at the midpoint, that is, 50% of the height to the top of a 200,000 barrel tank, and the tank in a typical cylindrical construction with a constant diameter from the bottom to the top, it is estimated that 100,000 barrels of crude oil are in the tank. Otherwise determined, the percentage capacity level of the tank can be calculated by 1 - (D1 / D2), where D1 and D2 are the respective measured lengths of L1 and L2 in image pixels. The percentage level of tank capacity is then multiplied by the tank's ability to calculate the number of barrels of crude oil in the tank.
    [037] Regarding the analysis of a tank with a floating roof, in another preferred form of image analysis, the top, the roof and the bottom of a tank are identified either in a photographic image or an infrared image. Automatic elliptical detection or adjustment algorithms employing mathematical transformations, such as a Hough transform, can then be used to adjust an elliptical plane on each of the top, roof, and bottom of the tank. Based on the determined height of the ceiling in relation to the base and / or the top of the tank (which again is indicative of the liquid level) and the capacity information of air-stored volume and / or the stored physical dimensions of the selected tank , the amount of crude oil in the tank can be calculated again.
    [038] With respect to tanks with fixed roofs, the level of liquid inside a selected tank can be verified from the collected infrared images, as the temperature of the stored oil is different from that of the gas above in the tank. A preferred form of analysis to determine the height of the liquid level in the tank is to measure the distance in pixels from the liquid-gas threshold to the base of the tank. Based on the liquid level verified inside the tank and on the stored volumetric capacity information and / or on the stored physical dimensions of the selected tank, the amount of crude oil in the tank can be calculated again.
    [039] In addition, with respect to tanks with fixed roofs and the determination of liquid level from the collected infrared images, a particular method of analysis is described in detail below.
    [040] In this particular analysis method, infrared images are collected for each tank of interest at selected intervals (for example, every five minutes) and transmitted to the central data processing facility for analysis. Although the camera that collects the infrared images is preferably fixed in position, it is known that there is often some minor movement of the camera. Thus, characteristic detection is used to find the location of the tank in each infrared image, thus ensuring an accurate calculation of the amount of crude oil in the tank.
    [041] The edges in images are areas with strong contrasts in intensity, that is, a significant change in intensity from one pixel to the next. There are several methods and techniques for detecting edges in an image, which can generally be grouped into two categories: gradient and Laplacian methods. The gradient method detects the edges aiming at the maximum and the minimum in the first derivative of the image. A Laplacian method searches for zero crossings in the second derivative of the image to find edges.
    [042] With reference now to FIG. 5, an edge has a one-dimensional shape of a ramp. Using a gradient method, the derivative of the unidimensional shape thus shows a maximum located in the center of the border. Based on this one-dimensional analysis, the theory can be performed for two dimensions as long as there is a precise approximation for calculating the derivative of a two-dimensional image. In that case, a Sobel operator is used to perform a two-dimensional spatial gradient measurement on a particular infrared image in order to find the approximate absolute gradient magnitude at each point in the infrared image. See R. Gonzalez and R. Woods, Digital Image Processing, Ad-dison Wesley (1992), p. 414 to 428. Sobel's edge detection method uses a pair of 3x3 convolution masks (FIG. 6), one estimating the gradient in the x direction (columns) (Gx) and the other estimating the gradient in the y direction (lines ) (Gy). A convolution mask is usually much smaller than the actual image. As a result, the mask is applied and slid over the image by manipulating a square of pixels at a time.
    [043] Specifically, in use, the mask is slid over an area of the input image (from the beginning of a line), changes the pixel value and shifts a pixel to the right, and then continues to the right until reaches the end of the line. It then starts at the beginning of the next line. FIG. 7 illustrates how a convolution mask in a Sobel edge detection method is applied to an input image, with the mask being applied over the upper left part of the input image and equation (1) below being used to calculate a particular pixel in the output image. The center of the mask is placed over the pixel being manipulated in the image; for example, pixel (a22) is converted to pixel (b22) by:

    [044] The Gx mask highlights the edges in the horizontal direction, while the Gy mask highlights the edges in the vertical direction. After obtaining the magnitude of both and adding, the resulting output detects edges in both directions.
    [045] In practice, a Sobel edge detection image is computed for each collected infrared image. Then, for each edge detection image, the location of the tank is found by determining the best fit for one or more characteristics. Each feature is a set of pixel locations where Sobel's edge detection image should contain a border and be black in color. FIG. 3 is an exemplified image in which the outline of three tanks has been found and marked on the image using Sobel's edge detection method, and FIG. 4 shows how this outline of the three tanks can be superimposed on a subsequent collected image.
    [046] In this particular analysis method, after finding the location of the tank, the tank level is computed based on a vertical line starting at the bottom of each tank, as also shown in FIG. 4. Each vertical line is checked upwards from the bottom of the tank for the location of the next edge, which is the location of the oil level. The number of pixels between the bottom of the tank and the location of the oil level (height in pixels) is used to compute the percentage of full tank as follows: Full percentage = 100 * (height in pixels) / (total height in tank pixels) (2)
    [047] Furthermore, in this particular method of analysis, the flow rate with respect to a certain tank can be calculated by the rate of change of storage levels within the tank:
    where Tank capacity is in barrels, and L_i is the percentage of full tank at hour i. where Flow_i <0, then Flow_i is set to zero since only oil flowing into a tank is being considered.
    [048] No matter which analysis technique is employed, the objective again is to obtain a measurement of the amount of crude oil in storage in the private network, which is stored in the central data processing facility.
    [049] With respect to the storage of LNG products, such as ethane, pro-cloth and butane, the collection and analysis of similar image in the vertical and horizontal tanks generally used to store such products can be performed in order to obtain a measurement of the amount of LNG product in storage on the private network. Pipes
    [050] Along with measuring the amount of crude oil or other liquid energy commodities in storage in a particular network, there is a determination of the quantity and flow rate in selected pipes in the particular network.
    [051] For example, a large interregional crude oil pipeline typically runs for hundreds of kilometers. In order to keep the crude oil pressure flowing, crude oil pumping stations are typically built every 128.74 to 160.93 km (80 to 100 miles). The pumps used in each of these pumping stations are typically electrically driven induction motors, with power (hp) in the range of 500 to 45,000 hp. Crude oil pipeline flow data in real time is generally only known to the owners, operators, and conveyors in the pipeline. In order to perform a remote determination of the amount and range of oil flow in a particular pipeline at a given time, a preferred form of analysis is based on monitoring the energy consumption in real time of any number of pumping stations along of a selected pipe.
    [052] Specifically, in an exemplified implementation, a monitoring device is deployed and used to monitor one or more power lines supplying electrical power to each selected pumping station. The monitoring device (also called "energy monitoring device" here) is mainly comprised of sensing elements that are responsive to the electrical potential and the magnetic flux densities associated with one or more power lines, thus allowing periodic or continuous measurements of electrical potential and magnetic flux densities associated with one or more power lines, and thus an energy determination. The construction and use of such monitoring devices are described in US Patent No. 6,771,058 assigned to the same assignee entitled “Apparatus and Method for the Measurement and Monitoring of Electrical Power Generation and Transmission”, and US Patent No. 6,714,000 entitled “Apparatus and Method for Monitoring Power and Current Flow”, each of which is incorporated here by reference.
    [053] The data from such monitoring devices is then transmitted to a central data processing facility. In the central data processing installation, a model of the pipelines and pumping stations in the particular network that is developed includes computations of the gain or loss of elevation between any monitored pumping station and the next pumping station downstream using the standard geographic elevation data. The pressure differential between the outlet or discharge side of any particular monitored pumping station and the inlet side of the next station downstream is then estimated. Pressure change calculations also take into account typical minimum and maximum pipe pressures for use as reasonable computation limit values.
    [054] For example, a preferential flow model takes into account the length of the pipe and the change in elevation between a monitored pumping station and the next pumping station downstream. Piping length, elevation change, and energy usage are used to estimate the pressure differential between the outlet side of the first pumping station and the inlet side of the next pumping station downstream. In other words, the difference in pressure due to friction or pressure loss (HeadLoss (H) in feet) between any two pumping stations in a selected pipe can be calculated from the variables presented below. See Pipeline Rules of Thumb Handbook, Gulf Professional Publishing (5th Edition) (2001). Sg = Specific oil gravity (API) Q = Flow rate (gal / min) H = Pump load differential (feet) D = Pipe diameter (feet) L = Pipe segment length (feet) E = Efficiency tubing V = Oil speed (feet / s) KV = Kinematic viscosity (cSt) HeadLoss = Head loss (feet)
    [055] The flow rate (Q) values are in the range of zero at the maximum flow rate of the pipeline. The flow rate (Q) is related to the oil speed as follows:

    [056] To obtain the kinematic viscosity, a CentiStokes (cSt) value is based on an API and temperature hypothesis, and is then converted into units of (feet2 / s):

    [057] The Fanning equation is then used to compute the frictional pressure drop (HeadLoss) between the pumping stations for a given flow rate (Q), pipe segment length (L) and elevation profile. The Fanning equation to express the frictional pressure drop of the oil flowing in a pipe is a function of a friction loss (f) derived from the Reynolds number (Re).

    Table 1: Re> 2,200 (Turbulent Flow)
    [058] The hydraulic power required to pump oil along a pipe segment is computed as follows, where H is the load differential (feet) on the discharge side of the pump:

    [059] The efficiency of the pump (E) is estimated in the range between 0.25 and 0.40. The energy consumed by any particular pump can then be computed directly from the pump's horsepower using a power conversion suit to energy unit c, which is equal to 0.000746.

    [060] Using equations (12) and (13) and setting H = (HeadLoss) (from equation (9)), a range of possible flow rates (Q) for the pipe from a minimum possible to maximum possible flow to the pipeline is plotted versus the equivalent expected energy consumption at the monitored pumping station.
    [061] For example, for a main US pipeline flowing from the American Gulf Coast to the main US storage core in Oklahoma, flow rates can be in the range of 0 to 350,000 barrels per day, with the diameter of the pipeline (D) = 0.74 m (2.44 feet). For the pumping station monitored at location x, the distance from the line (L) from that pumping station to the next pumping station downstream at location y = 112.18 m (368.062 feet). For a typical intermediate flow rate for piping of 200,000 barrels per day, the corresponding flow rate Q (gallons / minute) = 5,833.28. The kinematic viscosity v = 0.004 centiStokes. The elevation difference between the pumping station x and the pumping station y = 107.59 m (353 feet). The resulting head loss (HeadLoss) is 55.19 m (181.1 feet).
    [062] A graph of the flow rate (barrels per day) against expected energy consumption (MW) is shown in FIG. 8.
    [063] Since such energy consumption determinations have been made for any particular pumping station, the energy changes in each pumping station can be correlated with changes in flow through each pumping station. Thus, as the monitoring devices described above allow periodic or continuous measurements of energy consumed at a particular pumping station, the data collected from these monitoring devices can be used to determine the flow through and between the pumping stations.
    [064] Once the flow rate between consecutive pumping stations has been computed, a preferred method of deriving the total pipeline flow is to compute an average of the estimated flow rates at various pumping stations to determine the flow rate in the pipeline as a whole. The approach is often used when less than half of the pumping stations are monitored in a given pipeline.
    [065] Another preferred method of deriving total pipe flow uses Monte Carlo simulations to model energy use at all pumping stations along a given pipe and is used when half or more than half of pumping stations are monitored. The simulations use inputs from the monitored pumping stations, as well as predictions of energy use at the pumping stations along the pipeline that are not monitored. Energy use at unmonitored pumping stations is modeled with a uniform distribution from zero to maximum energy usage based on the number of pumps and the type of pumps at each pumping station. For a given flow value, each Monte Carlo simulation uses the same energy use observed for the monitored pumping stations and performs a random sampling of the uniform distributions of energy use for the unmonitored pumping stations. Equations (6) to (13) are used to simulate the pressure head profile along the entire pipe. If the pressure head profile along the pipeline goes below the minimum pressure or above the maximum pressure, the simulation is flagged as invalid. The flow rate in the pipeline, zero barrels per day for capacity, is divided into a finite number of intervals. For each flow value at the center of each flow interval, a large number of Monte Carlo simulations are performed and the number of valid simulations is recorded. A general pipeline flow is computed using the following expected value:
    where fi is the i flow value v_i is the number of valid simulations for f_i, and Totv is the total number of valid simulations for all flow intervals.
    [066] Finally, in certain circumstances, it may be impossible or impractical to monitor the electricity consumption in real time from any number of pumping stations along a selected pipeline. However, it would still be advantageous to know whether a particular pumping station is on or off. Consequently, a thermal imaging camera (such as those used to monitor storage facilities, as described above) can be used to assess the on / off condition of one or more pumping stations. Similarly, although electrically driven induction motors are generally used in pumping stations, some pumps can be driven by gas or diesel driven motors. Such engines typically discharge through one or more chimneys, so that the operation and operational levels (including the number of pumps on or off) of the pumping station can also be assessed using a thermal imaging camera directed at the chimneys or auxiliary equipment. . Processing Facilities
    [067] Crude oil invariably enters an oil refinery at some point in the network to be processed into gasoline and / or other petroleum products, such as diesel, jet fuel, heating oil, etc. The ability of the various units in the refinery to use the incoming crude oil is dependent on the proper functioning of such units. Refineries are highly complex facilities that are generally designed and designed to operate year round on a 24/7 schedule. However, equipment disruptions and malfunctions at these facilities occur on a relatively frequent basis and can have an immediate impact on market dynamics. Specifically, if private units in one or more refineries are shut down, there is a decrease in demand for crude oil in the affected refineries and a decrease in the supply of gasoline and other refined products in markets supplied by affected refineries. The so-called decrease in production volume (“ramp-down”) and increase in production volume (“ramp-up”) of the refinery unit are of particular interest in the market, but, in addition, there is also interest in the rates of flow of crude oil in each refinery and the amount of crude oil in storage at each refinery at any given time.
    [068] Then, in the method and system of the present invention, the operational status of one or more processing facilities, such as refineries, on the network is checked. With respect to the term “processing facilities”, this term is also intended to include any installation on a network in which there is some manipulation of liquid energy commodities that can be monitored, even if there is no change in liquid energy commodities, such as outbreak storage, transfer or discharge facilities. In any case, a preferred method for monitoring the operation of processing facilities is to use fixed thermal imaging cameras. A thermal imaging camera can acquire thermal data and record images of emissions and thermal signatures from several key units that can be used to verify that the processing facility is working as expected or not.
    [069] FIGS. 9 (a) - (d) are a series of thermal images that illustrate the decrease in the production volume (“ramp-down”) of a fluid catalytic cracking unit (FCCU) in a refinery. As reflected in FIGs. 9 (a) - (d), each primary unit in a refinery typically has one or more exhaust chimneys associated with it, which generally function as exhausts for heating devices, such as furnaces, heat exchangers, etc., or exhausts for emission control devices, such as wet gas purifiers, electrostatic dust precipitators, etc. In general, if a particular unit is operating normally, a characteristic level of heating is observed in a thermal image in the chimney. In addition, a characteristic emission via smoke emanating from the top of the chimney is also present and visible. When the unit is turned off, or not operating normally, heating and emissions from such chimneys are seen to be either completely absent or to exhibit abnormal characteristics (for example, overheating or excess emissions). Similarly, apart from chimneys, a characteristic level of heating can be observed in thermal images for many other types of equipment associated with a unit, including, but not limited to, vessels, piping, work ducts, heat exchangers, furnaces , and / or auxiliary equipment.
    [070] Returning to FIGs. 9 (a) - (d), in this particular example, the FCCU is to the right of the image, as illustrated by the arrow. In FIG. 9 (a), the FCCU is shown in normal operating mode. As shown in FIG. 9 (b), during the beginning of the decrease in production volume (“ramp-down”), emissions are seen from a chimney in the middle of the FCCU, and the FCCU itself shows relative cooling in relation to the neighboring units. In FIG. 9 (c), the FCCU body shows continuous cooling; emission chimneys remain hot, but emissions from them are reduced. In FIG. 9 (d), both the FCCU and the chimney have been completely cooled, and the decrease in the production volume (“ramp-down”) of the FCCU is complete.
    [071] Each primary unit in a refinery also has emergency control devices, such as burners, exhaust flues, and other devices that can burn or dissipate built-in flows of raw materials, processing chemicals, and associated by-products in the case. where the units need to be shut down quickly. Such emergency control devices can also be used in the normal operation of such units to control the quantities of raw materials, processing chemicals, and associated by-products in process flows. These emergency devices can also be observed by a thermal imaging camera operating at characteristic levels (typically low or thin) when the associated units are operating normally and at abnormal levels (typically emitting abnormal or high levels) when the associated units are experiencing problems, are being initialized, or are being shut down.
    [072] In any case, thermal images such as those shown in FIGs. 9 (a) - (d) can be analyzed visually or using automatic image analysis to check the operational status of the main units of a refinery. For further discussion of image analysis techniques that can be used, reference is made to the copending US Patent Application and assigned to the same assignee No. 13 / 269,833 entitled “Method and System for Providind In-formation to Market Participants About One or More Power Generating Units Based on Thermal Image Data ”, which is a continuation of US Patent Application No. 12/053. 139. Each of these patent applications is incorporated herein by reference.
    [073] Additionally, while the above discussion is directed at refineries that refine crude oil in gasoline and / or other petroleum products, the monitoring technology is also applicable to such processing facilities as: (a) fractional distillation operations, where LNGs are separated from crude oil for subsequent processing in such products as ethane, propane, and butanes; (b) breeding facilities, which process crude crude oils after extraction and prepare crude oils for delivery and subsequent refinement at crude oil refineries; (c) ethylene cracking facilities, where LNG products and / or petroleum liquids (such as naphtha) are processed into petrochemical raw materials such as ethylene, propylene, etc .; and (d) natural gas processing facilities, which produce LNG from natural gas. Sale
    [074] Now, having described the monitoring of the three fundamental components of a particular network - (i) storage facilities, (ii) pipelines, and (iii) processing facilities - it is possible to determine the total “balances” of the oil raw or other liquid energy commodities. For example, and as mentioned above, the "balances" of interest to market participants with respect to crude oil include, but are not limited to: the amount of crude oil in storage in a given market region at a given time; the amount of crude oil flowing to a market region from adjacent market regions; and / or the amount of crude oil being processed into gasoline and other petroleum products.
    [075] With respect again to FIG. 1, in order to determine the physical balances of crude oil or other liquid energy commodities in a particular network, the combined data from monitoring these three fundamental components can be used to estimate the physical balances of interest.
    [076] For example, FIG. 10 illustrates a storage core (ie, a collection of storage tanks) 100 that is connected to three pipes: Tubing A, Pipe B, and Pipe C. Using the analysis techniques described above, measuring the amount of crude oil in each storage tank is made, and then a sum of all measurements produces the collective amount in storage in the storage core 100 at a given time. Then, a determination of the incoming and outgoing flows in real time of oil in the storage core 100 can be made on a periodic basis from the data collected from the monitoring devices for the power lines supplying electricity to the selected pumping stations along each of the three pipelines. For example, if Piping A and Piping B are arriving, and Piping C is exiting, a net inflow to storage core 100 can be computed from a sum of the inflows minus any outflows: Coreinflux in the Core = (Influx Pipe + Bflow Pipe) - Flux Pipe (15)
    [077] Thus, with the measurement of the collective quantity in storage in the storage core 100 at a given time and the subsequent periodic determinations of incoming and outgoing flows, a substantially real-time determination can be made as the quantity of crude oil in storage at storage core 100 at a given time. In addition, additional modeling may then be possible to determine the operational parameters, such as the effect on storage levels in the storage core 100 for various operating conditions of the inlet and outlet pipes, the use of certain storage tanks to contain oil crude oil from certain pipes, crude oil that is in transit through the storage core 100 and crude oil that is in the storage core 100.
    [078] For another example, the data collected from the monitoring devices for the power lines supplying electricity to the selected pumping stations (PS1, PS2, PS3, PS4, PS5) along each one tu-bulations can be combined with information obtained from the analysis of thermal images of a refinery (not shown) connected to the pipelines to determine the balances of crude oil in transit to the refinery, in storage at the refinery, and being processed at the refinery at any given time.
    [079] It is further observed that, in addition to combining the measured data directly collected in different locations in a particular network as described, the data can also be obtained from third parties and publicly available data sources, such as those provided by United States Energy Information Administration (“EIA”), to deliver estimates and forecasts of market parameters of interest related to supplier, demand, and storage of commodities. For example, one such parameter of interest is the total volume of crude oil in storage in the PAD 2 market region at any given time. EIA publishes an amount for that amount weekly, typically on Wednesday morning at 10:30 am. Directly measured data and EIA data can be effectively combined using a mathematical regression model. Specifically, the standard regression mathematical model is used to fit the directly measured data to the PAD 2 crude oil storage inventory data published by EIA. The determined PAD 2 crude oil inventories are then estimated using the resulting model. With reference now to FIG. 11, in one example, the directly measured data are obtained using the techniques described above for: (i) the storage levels in a main storage core of the PAD 2; (ii) the crude oil flow rates for PAD 2 (collected from six pipes entering the PAD 2 region from PAD 3); and (iii) operational data from the refinery unit (collected from new PAD 2 refineries). These directly measured data are then adjusted using a standard regression mathematical model for the historical data of PAD 2 crude oil storage inventory by the EIA. The crude oil inventories determined on the basis of the model output (“Model” line in FIG. 11) can then be compared with the actual PAD 2 crude oil inventory data (“PAD 2” line in FIG. 11), and PAD 2 crude oil inventories can then be estimated using the resulting method.
    [080] For another example, FIG. 12 is a schematic view of another exemplified network associated with the production of crude oil. In FIG. 12, crude oil from an oil platform 200 (or other production source) is delivered to a pipe 210. Along pipe 210, there are four locations with sensors - S1, S2, S3, S4, as described further down in Table 2. Pipeline 210 is then connected and delivers the crude oil to a fractional distillation station 212, which is monitored by an S5 sensor, as also described below in Table 2. From the fractional distillation installation 212, crude oil flows to a storage facility 214, which is monitored by sensor (s) S6, as also described below in Table 2.
    [081] Finally, in this exemplified implementation, there is an additional data entry, as shown in FIG. 12 by S7. This additional data entry, S7, is used to further verify the collected data and the results of the various computational analyzes. Specifically, in the exemplified network shown in FIG. 12, crude oil at storage facility 214 is delivered to one or more ships at a marine terminal for export. Most of the data on vessels that transfer crude oil is publicly known and available, including the vessel's capacity and the location of the vessel via the Automatic Identification System (AIS) vessel tracking services. While a private ship is in port at the sea terminal, a visual camera or an infrared camera can be used to estimate the flow rate of oil delivered to the private ship by measuring the change in the ship's course (that is, the change in position relative to the waterline) over time. This oil delivery should be equal to the reduction in oil level at the storage facility 214. Of course, such technology can be similarly used when ships are delivering oil to a storage facility.
    Table 2
    [082] With reference now to FIG. 13, the outlets of S1, S2, S3 and S4 are used to determine the energy changes at each pumping station in pipeline 210, which can then be used to determine the flow rate of crude oil through pipeline 210, as indicated block 300 of FIG. 13, and this flow rate data is stored in a database at a central data processing facility. The output from S5 is used to determine the operational state of the fractional distillation installation 212, as indicated by block 302 of FIG. 13, and this operational status information is also stored in the database at the central data processing facility. The outlet of S6 is used to measure a quantity of the crude oil in storage in storage facility 214, as indicated by block 304 of FIG. 13, and this measurement data is also stored in a database at the central data processing facility.
    [083] In the central data processing facility, an analysis is performed on flow rate data, operational status information, and measurement data to determine the total “balances” of crude oil in different functional parts of the network, as indicated by block 310 of FIG. 13. For example, with respect to this exemplified network, the "balances" of interest to market participants would include, but are not limited to: the amount of crude oil flowing into the network at any given time, the amount of crude oil in storage on the network at a given time; and / or the amount of crude oil flowing out of the network at any given time.
    [084] With respect to FIG. 13, once the analysis has been completed, information about the balance of crude oil in the network can be communicated to market participants and other interested third parties, that is, third parties who would not normally have ready access to such information, as indicated by block 320. It is observed and preferred that such communication to third market participants can be achieved through the delivery of electronic mail and / or by exporting the data to a website of the controlled access Internet, which participants of the third party marketers can access through a common Internet browser program, such as Microsoft Internet Explorer®. Of course, the communication of information and data to third-party market participants can also be performed through a wide variety of other known means of communication without abandoning the spirit and scope of the present invention.
    [085] FIG. 14 is a schematic representation of central components in an exemplified implementation of the method and system of the present invention. As shown in FIG. 14, the central data processing facility 10 includes a first database 20, a second database 22, and a third database 24. Of course, these databases 20, 22, 24 could be integrated into a single database at the central data processing facility 10. In addition, the central data processing facility 10 hosts a digital computer program, that is, computer-readable instructions stored and executed by a computer, which includes appropriate modules for executing the routines and subroutines required to perform the operational steps of the present invention. Thus, an exemplified system for determining a quantity of a liquid energy commodity stored in a tank according to the present invention includes: (a) a storage metering module 40 for receiving and analyzing images collected from one or more facilities to measure a quantity of liquid energy commodities in storage at each of the one or more storage facilities, and to store this measurement data in a first database 20; (b) a flow rate determination module 42 for receiving and processing measurements of electrical potential and magnetic flux densities associated with power lines for pumping stations in a pipeline to determine a flow rate for liquid energy commodities in each selected pipe, and store this flow rate data in a second database 22; (c) an operational status module 44 for receiving and processing information about an operational status of a processing facility and storing that operational status information in a third database 24; (d) an analysis module 50 to query databases 20,22, 24 and analyze measurement data, flow rate data and operational status information to determine a balance of liquid energy commodities in the network or a selected part of it at a given time; and (e) a communication module 60 for communicating information about liquid energy commodities to a third market participant.
    [086] One skilled in the art will recognize that additional modalities and implementations are also possible without abandoning the teachings of the present invention. This detailed description, and particularly the specific details of the exemplified modalities and implementations described here, is given mainly for clarity of understanding, and no unnecessary limitations are understood from these, for modifications that will become obvious to those versed in the technique upon reading of this description and can be made without departing from the scope of the invention.
    权利要求:
    Claims (19)
    [0001]
    1. Method for collecting and analyzing operational information from a network of components associated with a liquid energy commodity, CHARACTERIZED by the fact that it comprises the steps of: measuring (304) a quantity of liquid energy commodities in storage. storage in one or more storage facilities on the network, and store these measurement data in a first database (20) in a central data processing facility (10); determine (300) a flow rate of liquid energy commodities in one or more selected pipelines in the network, and store this flow rate data in a second database (22) at the central data processing facility (10 ), where as part of the step of determining the flow rate of liquid energy commodities in a selected pipeline in the network, an energy monitoring device is positioned to monitor power lines supplying electricity to a particular pumping station in the selected piping in order to determine the energy consumed by the particular pumping station; verify (302) an operational status of one or more processing facilities on the network, and store that operational status information in a third database (24) at the central data processing facility (10); analyze (310) measurement data, flow rate data, and operational status information to determine a balance of liquid energy commodities in the network or a selected part of it at a given time; and communicating (320) information about the balance of liquid energy commodities to a third market participant.
    [0002]
    2. Method, according to claim 1, CHARACTERIZED by the fact that the liquid energy commodities are crude oil.
    [0003]
    3. Method, according to claim 1, CHARACTERIZED by the fact that the first database (20), the second database (22), and the third database (24) are integrated into a single database. data at the central data processing facility (10).
    [0004]
    4. Method, according to claim 1, CHARACTERIZED by the fact that the step of measuring (304) the quantity of liquid energy commodities in storage comprises the substeps of: periodically conducting an inspection of one or more tanks of an installation private storage facilities, including collecting one or more images of each tank; transmit the images collected from each tank to the central data processing facility; and analyze the images collected from each tank to determine a liquid level for each tank.
    [0005]
    5. Method, according to claim 4, CHARACTERIZED by the fact that the images collected are infrared images acquired by a thermal imaging camera.
    [0006]
    6. Method, according to claim 5, CHARACTERIZED by the fact that a method of detecting edges is applied to each image collected to find the location of tanks in each image collected and then identify the level of liquid in each tank.
    [0007]
    7. Method, according to claim 1, CHARACTERIZED by the fact that, as part of the step of verifying (302) the operational status of one or more processing facilities on the network, a thermal imaging camera is positioned to acquire thermal data from one or more units of a selected processing facility.
    [0008]
    8. Method, according to claim 7, CHARACTERIZED by the fact that the thermal imaging camera is positioned to acquire thermal data from one or more chimneys of the selected processing facility.
    [0009]
    9. Method for collecting and analyzing operational information from a network of components associated with a liquid energy commodity, CHARACTERIZED by the fact that it comprises the steps of: using a thermal imaging camera to collect images in one or more installations network storage, transmit the collected images to a central data processing facility, and analyze the collected images to measure (304) a quantity of liquid energy commodities in storage at each of the one or more storage facilities, and store these measurement data in a first database (20) at the central data processing facility (10); position one or more power monitoring devices to monitor power lines providing electrical power to particular pumping stations associated with selected pipelines in the network, each of the one or more power monitoring devices including sense elements - responsive electrical potential and magnetic flux densities associated with power lines, thus allowing a measurement of electrical potential and magnetic flux densities associated with power lines, and thus a determination of energy consumed by each power station. private pumping, which is then correlated (300) to a flow rate of liquid energy commodities in each selected pipeline in the network, and to store this flow rate data in a second database (22) at the central data (10); use a thermal imaging camera to check (302) an operational status of one or more processing facilities on the network, and store that operational status information in a third database (24) at the central data processing facility ( 10); analyze (310) measurement data, flow rate data, and operational status information to determine a balance of liquid energy commodities in the network or a selected part of it at a given time; and communicating (320) information about the balance of liquid energy commodities to a third market participant.
    [0010]
    10. Method, according to claim 9, CHARACTERIZED by the fact that the liquid energy commodities are crude oil.
    [0011]
    11. Method, according to claim 9, CHARACTERIZED by the fact that the first database (20), the second database (22) and the third database (24) are integrated into a single database in the central data processing facility (10).
    [0012]
    12. System for collecting and analyzing operational information from a network of components associated with a liquid energy commodity, CHARACTERIZED by the fact that it comprises: a storage measurement module (40) to receive and analyze images collected from one or more storage facilities to measure a quantity of liquid energy commodities in storage in each of the one or more storage facilities, storing such measurement data in a first database (20); a flow rate determination module (42) for receiving and processing measurements of electrical potential and magnetic flux densities associated with power lines to pumping stations in one or more pipelines to determine a flow rate of liquid energy commodities in each of said one or more pipes, storing such flow rate data in a second database (22); an operational status module (44) for receiving and processing information about an operational status of a processing facility, storing that operational status information in a third database (24); an analysis module (50) to query the first database (20), the second database (22), and the third database (24) and analyze the measurement data, flow rate data, and the operational status information to determine a balance of liquid energy commodities in the network or a selected part of it in a given time; and a communication module (60) for communicating information about the net energy modities to a third market participant.
    [0013]
    13. System, according to claim 12, CHARACTERIZED by the fact that the first database (20), the second database (22), and the third database (24) are integrated into a single database. data at the central data processing facility (10).
    [0014]
    14. Method for collecting and analyzing operational information from a network of components associated with the transport of crude oil, CHARACTERIZED by the fact that it comprises the steps of: using a thermal imaging camera to collect images from one or more storage tanks on the network, transmit the collected images to a central data processing facility, and analyze the collected images to measure (304) a quantity of crude oil in one or more storage tanks, and store these measurement data in a first database (20) in the central data processing facility (10); position one or more energy monitoring devices to monitor power lines supplying electrical energy to particular pumping stations associated with selected pipelines in the network, each of the one or more energy monitoring devices including sensing elements responsive to the electrical potential and the magnetic flux densities associated with the power lines, thus allowing a measurement of the electrical potential and magnetic flux densities associated with the power lines, and thus a determination of the energy consumed by each particular pumping station , which is then correlated (300) to a crude oil flow rate in each selected pipe in the network, and to store this flow rate data in a second database (22) at the central data processing facility (10 ); use a thermal imaging camera to check (302) an operational status of one or more processing facilities on the network, and store that operational status information in a third database (24) at the central data processing facility ( 10); analyze (310) the measurement data, flow rate data, and operational status information to determine a balance of crude oil in the network or a selected part of it at a given time; and communicating (320) information on the balance of crude oil to a third party in the market.
    [0015]
    15. Method, according to claim 14, CHARACTERIZED by the fact that the first database (20), the second database (22), and the third database (24) are integrated into a single database. data at the central data processing facility (10).
    [0016]
    16. Method, according to claim 14, CHARACTERIZED by the fact that the information communicated to the third market participant is a quantity of crude oil stored in the network at the given time.
    [0017]
    17. Method, according to claim 14, CHARACTERIZED by the fact that the information communicated to the third market participant is a quantity of crude oil flowing into the network over a given period of time.
    [0018]
    18. Method, according to claim 14, CHARACTERIZED by the fact that the information communicated to the third market participant is a quantity of crude oil flowing out of the network over a given period of time.
    [0019]
    19. Method for monitoring the transport of crude oil in a network that includes a production source, a pipe, a processing facility, and one or more storage tanks, CHARACTERIZED by the fact that it comprises the steps of: positioning one or more energy monitoring devices to monitor power lines supplying electricity to selected pumping stations associated with the pipeline that extends between the production source and the processing facility, each of the one or more monitoring devices of energy including sensing elements responsive to the electrical potential and the magnetic flux densities associated with the power lines, thus allowing a measurement of the electrical potential and magnetic flux densities associated with the power lines, and thus a determination of the energy consumed for each selected pumping station, which is then correlated (300) to an oil flow rate c ru in the pipeline, and store these flow rate data in a first database (20) at the central data processing facility (10); use a thermal imaging camera to check (302) an operational status of the processing facility, and store that operational status information in a second database (22) at the central data processing facility (10); use the thermal imaging camera to collect images from one or more storage tanks, transmit the collected images to the central data processing facility, and analyze the collected images to measure (304) an amount of crude oil in a or more storage tanks, and store the measurement data in a third database (24) at the central data processing facility (10); analyze (310) flow rate data, operational status information, and measurement data to determine a crude oil balance in the network at a given time; and communicating (320) information on the balance of crude oil to a third party in the market.
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    同族专利:
    公开号 | 公开日
    EP2676232A2|2013-12-25|
    EP2676232B1|2018-09-05|
    US9886673B2|2018-02-06|
    MX2013009414A|2014-01-24|
    US20140200936A1|2014-07-17|
    JP2014511527A|2014-05-15|
    EP3432249A1|2019-01-23|
    CN103477359A|2013-12-25|
    WO2012112759A2|2012-08-23|
    CA2827314A1|2012-08-23|
    CA2827314C|2018-06-26|
    EP3432249B1|2020-04-08|
    WO2012112759A3|2012-11-08|
    US20120206595A1|2012-08-16|
    BR112013021047A2|2016-10-18|
    US8717434B2|2014-05-06|
    CN103477359B|2016-11-16|
    SG192799A1|2013-09-30|
    AU2012217632A1|2013-09-05|
    AU2012217632B2|2015-07-02|
    US20180101796A1|2018-04-12|
    EP2676232A4|2016-10-12|
    JP5767344B2|2015-08-19|
    MY164743A|2018-01-30|
    US10692022B2|2020-06-23|
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    法律状态:
    2018-12-18| B06F| Objections, documents and/or translations needed after an examination request according [chapter 6.6 patent gazette]|
    2019-10-22| B06U| Preliminary requirement: requests with searches performed by other patent offices: procedure suspended [chapter 6.21 patent gazette]|
    2020-12-15| B06A| Patent application procedure suspended [chapter 6.1 patent gazette]|
    2021-03-09| B09A| Decision: intention to grant [chapter 9.1 patent gazette]|
    2021-03-30| B16A| Patent or certificate of addition of invention granted [chapter 16.1 patent gazette]|Free format text: PRAZO DE VALIDADE: 20 (VINTE) ANOS CONTADOS A PARTIR DE 16/02/2012, OBSERVADAS AS CONDICOES LEGAIS. |
    优先权:
    申请号 | 申请日 | 专利标题
    US201161443510P| true| 2011-02-16|2011-02-16|
    US61/443,510|2011-02-16|
    PCT/US2012/025418|WO2012112759A2|2011-02-16|2012-02-16|Method and system for collecting and analyzing operational information from a network of components associated with a liquid energy commodity|
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