SYSTEM AND METHOD FOR DETECTING PRESSURE CHANGES IN PIPE SECTIONS

Abstract

The present embodiments disclose a system and method for generating a predictive model to detect leak in undersea pipes. The system includes an administrator processor and one or more pipe sections. The method proceeds with observing changes in pressure, generating a predictive model configured to determine if a leak has occurred, and closing one or more pipes responsible for the leak.

Description

FIELD OF DISCLOSURE

The present disclosure relates to a system and method for generating a predictive model configured to detect significant changes in pressure or temperature among pipe sections.

BACKGROUND AND SUMMARY

Leaks can be difficult to detect in areas with low visibility, particularly within bodies of water. To observe an underwater pipe section that may have a leak, typically an unmanned or remote observation vehicle (ROV) fitted with a camera would be the best option. However, these vehicles can be slow, inefficient, and require a significant amount of skill to operate. Even if an ROV can identity a specific leak, the ROV itself can only observe one section of pipe at a time. As a means of determining whether a leak is occurring, an ROV is not ideal.

Therefore, there is a need to identify leaks more quickly and efficiently.

Aspect of the disclosed embodiments include a system and method for detecting change in pressure and temperature among pipe sections.

Embodiments of the present disclosure include a system for detecting changes in pressure and temperature within section of a pipe, the system comprising one or more pipe sections comprising one or more valves further configured to open and close for a predetermined time period. The system also includes an administrator processor configured to open the one or more valves for a first predetermined time period and close the one or more valves for a second predetermined time period. Next, the system can record any changes in one or more pressure levels and temperature levels between the first predetermined time period and the second predetermined time period. Then, the system compares the recorded changes to one or more historical pressure levels and temperature levels and generates, upon comparing the record changes to the historical pressure levels and temperature levels, a predictive model configured to determine whether a significant change in the pressure levels or temperature levels has occurred. Next, the system can determine by the predictive model whether a significant change in the pressure levels or temperatures has occurred and close, upon determining that a significant change in the pressure levels or temperature levels has occurred, one or more valves.

Embodiments of the present disclosure also include a method comprising the steps of opening, by a processor, one or more valves associated with one or more pipe sections for a first predetermined time period and closing, by the processor, the one or more valves for a second predetermined time period. Next, the method proceeds with recording, by the processor, any changes in one or more pressure levels and temperature levels between the first predetermined time period and the second predetermined time period and comparing the recorded changes to one or more historical pressure levels and temperature levels. Next, the method proceeds with generating, upon comparing the record changes to the historical pressure levels and temperature levels, a predictive model configured to determine whether a significant change in the pressure levels or temperature levels has occurred. Then, the predictive model determines whether a significant change in the pressure levels or temperatures has occurred. Then, the method concludes with closing, upon determining that a significant change in the pressure levels or temperature levels has occurred, one or more valves.

Further features of the disclosed systems and methods, and the advantages offered thereby, are explained in greater detail hereinafter with reference to specific example embodiments illustrated in the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to facilitate a fuller understanding of the present invention, reference is now made to the attached drawings. The drawings should not be construed as limiting the present invention, but are intended only to illustrate different aspects and embodiments of the invention.

The invention relates generally to generating a predictive model made to determine whether a leak has occurred, is presently occurring, or will occur.

Generally, the system can include an administrator processor and pipe sections. Certain pipe sections can further include a valve that can be opened and closed. Furthermore, the pipes can include a measurement device configured to measure pressure and temperature. The administrator processor can control the valves. The processor can begin by opening and closing the valves for a predetermined time period. The processor can observe and record any changes in pressure and temperature during the time period. Having recorded any changes, the processor can compare these records with historical data related to temperature and pressure. The historical data can be about different or similar pipe sections acting under similar or difference conditions. Upon comparing the current information to historical information, the processor can generate a predictive model configured to detect, based on observed pressure and temperature, whether a leak has occurred or will occur. The model can determine that a leak has occurred, then close the valve or section of pipe that is responsible for the leak.

Systems and methods of the present disclosure provide numerous advantages over typical methods. Rather than rely on an ROV to detect leaks, the predictive model can quickly alert users to potential leaks. This saves the user time and frustration. Furthermore, the model is not limited to a single section of pipe—it can observe many sections of pipe at once. The model can detect changes in pressure and temperature that are hidden from human observation. By learning from past data, the model can even predict when a small leak will become a big leak. Furthermore, the predictive model can consolidate data from other pipeline locations. Thus, information about leak-prevention can now be shared to anyone with authorized use. Overall, the disclosed systems and methods provide significant improvements on past solutions.

FIG. 1 illustrates a system according to an exemplary embodiment.

FIG. 2 illustrates a method according to an exemplary embodiment.

FIG. 3 illustrates a system according to an exemplary embodiment.

FIG. 4 illustrates a system according to an exemplary embodiment.

DETAILED DESCRIPTION OF THE DRAWINGS

Exemplary embodiments of the invention will now be described in order to illustrate various features of the invention. The embodiments described herein are not intended to be limiting as to the scope of the invention, but rather are intended to provide examples of the components, use, and operation of the invention.

Furthermore, the described features, advantages, and characteristics of the embodiments may be combined in any suitable manner. One skilled in the relevant art will recognize that the embodiments may be practiced without one or more of the specific features or advantages of an embodiment. In other instances, additional features and advantages may be recognized in certain embodiments that may not be present in all embodiments.

The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions.

FIG. 1 illustrates a system 100 according to an example embodiment. The system 100 may comprise an administrator processor 110, a network 120, a data storage unit 130, a server 140, and a pipe section 150. Although FIG. 1 illustrates single instances of components of system 100, system 100 may include any number of components.

System 100 may include an administrator processor 110. The administrator processor 110 may be a network-enabled computer device. Exemplary network-enabled computer devices include, without limitation, a server, a network appliance, a personal computer, a workstation, a phone, a handheld personal computer, a personal digital assistant, a thin client, a fat client, an Internet browser, a mobile device, a kiosk, a contactless card, or other computer device or communications device. For example, network-enabled computer devices may include an iPhone, iPod, iPad from Apple® or any other mobile device running Apple's iOS® operating system, any device running Microsoft's Windows® Mobile operating system, any device running Google's Android® operating system, and/or any other smartphone, tablet, or like wearable mobile device.

The administrator processor 120 may include a processor 111, a memory 112, and an application 113. The processor 111 may be a processor, a microprocessor, or other processor, and the administrator processor 110 may include one or more of these processors. The processor 111 may include processing circuitry, which may comprise additional components, including additional processors, memories, error and parity/CRC checkers, data encoders, anti-collision algorithms, controllers, command decoders, security primitives and tamper-proofing hardware, as necessary to perform the functions described herein.

The processor 111 may be coupled to the memory 112. The memory 112 may be a read-only memory, write-once read-multiple memory or read/write memory, e.g., RAM, ROM, and EEPROM, and the administrator processor 120 may include one or more of these memories. A read-only memory may be factory programmable as read-only or one-time programmable. One-time programmability provides the opportunity to write once then read many times. A write-once read-multiple memory may be programmed at one point in time. Once the memory is programmed, it may often not be rewritten, but it may be read many times. A read/write memory may be programmed and re-programed many times after leaving the factory. It may also be read many times. The memory 112 may be configured to store one or more software applications, such as the application 113, and other data, such as user's private data and other information.

The application 113 may comprise one or more software applications, such as a mobile application and a web browser, comprising instructions for execution on the administrator processor 110. In some examples, the administrator processor 110 may execute one or more applications, such as software applications, that enable, for example, network communications with one or more components of the system 100, transmit and/or receive data, and/or perform the functions described herein. Upon execution by the processor 111, the application 113 may provide the functions described in this specification, specifically to execute and perform the steps and functions in the process flows described below. Such processes may be implemented in software, such as software modules, for execution by computers or other machines. The application 113 may provide graphical user interfaces (GUIs) through which a user may view and interact with other components and devices within the system 100. The GUIs may be formatted, for example, as web pages in HyperText Markup Language (HTML), Extensible Markup Language (XML) or in any other suitable form for presentation on a display device depending upon applications used by users to interact with the system 100.

The administrator processor 110 may further include a display 114 and input devices 115. The display 114 may be any type of device for presenting visual information such as a computer monitor, a flat panel display, and a mobile device screen, including liquid crystal displays, light-emitting diode displays, plasma panels, and cathode ray tube displays. The input devices 115 may include any device for entering information into the administrator processor 110 that is available and supported by the administrator processor 110, such as a touchscreen, keyboard, mouse, cursor-control device, microphone, digital camera, video recorder or camcorder. These devices may be used to enter information and interact with the software and other devices described herein.

System 100 may include one or more networks 120. In some examples, the network 120 may be one or more of a wireless network, a wired network or any combination of a wireless network and a wired network and may be configured to connect the user device 110, the server 140, and the data storage unit 140. For example, the network 120 may include one or more of a fiber optics network, a passive optical network, a cable network, an Internet network, a satellite network, a wireless local area network (LAN), a Global System for Mobile Communication, a Personal Communication Service, a Personal Area Network, Wireless Application Protocol, Multimedia Messaging Service, Enhanced Messaging Service, Short Message Service, Time Division Multiplexing based systems, Code Division Multiple Access based systems, D-AMPS, Wi-Fi, Fixed Wireless Data, IEEE 802.11b, 802.15.1, 802.11n and 802.11g, Bluetooth, NFC, Radio Frequency Identification (RFID), Wi-Fi, and/or the like.

In addition, the network 120 may include, without limitation, telephone lines, fiber optics, IEEE Ethernet 902.3, a wide area network, a wireless personal area network, a LAN, or a global network such as the Internet. In addition, the network 120 may support an Internet network, a wireless communication network, a cellular network, or the like, or any combination thereof. The network 120 may further include one network, or any number of the exemplary types of networks mentioned above, operating as a stand-alone network or in cooperation with each other. The network 120 may utilize one or more protocols of one or more network elements to which they are communicatively coupled. The network 120 may translate to or from other protocols to one or more protocols of network devices. Although the network 120 is depicted as a single network, it should be appreciated that according to one or more examples, the network 120 may comprise a plurality of interconnected networks, such as, for example, the Internet, a service provider's network, corporate networks, and home networks. The network 120 may further comprise, or be configured to create, one or more front channels, which may be publicly accessible and through which communications may be observable, and one or more secured back channels, which may not be publicly accessible and through which communications may not be observable.

System 100 may include a data storage unit 130. The data storage unit 130 may be one or more data storage units configured to store technical or other data, including without limitation, private data of users or operators, accounts of users or operators, identities of users o operators, and certified and uncertified documents. The data storage unit 130 may comprise a relational data storage unit, a non-relational data storage unit, or other data storage unit implementations, and any combination thereof, including a plurality of relational data storage units and non-relational data storage units. In some examples, the data storage unit 130 may comprise a desktop data storage unit, a mobile data storage unit, or an in-memory data storage unit. Further, the data storage unit 130 may be hosted internally by the server 140 or may be hosted externally of the server 140, such as by a server, by a cloud-based platform, or in any storage device that is in data communication with the server 140.

System 100 may include a server 140. The server 140 may be a network-enabled computer device. Exemplary network-enabled computer devices include, without limitation, a server, a network appliance, a personal computer, a workstation, a phone, a handheld personal computer, a personal digital assistant, a thin client, a fat client, an Internet browser, a mobile device, a kiosk, a contactless card, or other a computer device or communications device. For example, network-enabled computer devices may include an iPhone, iPod, iPad from Apple® or any other mobile device running Apple's iOS® operating system, any device running Microsoft's Windows® Mobile operating system, any device running Google's Android® operating system, and/or any other smartphone, tablet, or like wearable mobile device.

The server 140 may include a processor 141, a memory 142, and an application 143. The processor 141 may be a processor, a microprocessor, or other processor, and the server 140 may include one or more of these processors. The processor 141 may include processing circuitry, which may contain additional components, including additional processors, memories, error and parity/CRC checkers, data encoders, anti-collision algorithms, controllers, command decoders, security primitives and tamper-proofing hardware, as necessary to perform the functions described herein.

The processor 141 may be coupled to the memory 142. The memory 142 may be a read-only memory, write-once read-multiple memory or read/write memory, e.g., RAM, ROM, and EEPROM, and the server 140 may include one or more of these memories. A read-only memory may be factory programmable as read-only or one-time programmable. One-time programmability provides the opportunity to write once then read many times. A write-once read-multiple memory may be programmed at a point in time after the memory chip has left the factory. Once the memory is programmed, it often may not be rewritten, but it may be read many times. A read/write memory may be programmed and re-programed many times after leaving the factory. It may also be read many times. The memory 142 may be configured to store one or more software applications, such as the application 143, and other data, such as user's private data and account information.

The application 143 may comprise one or more software applications comprising instructions for execution on the server 140. In some examples, the server 140 may execute one or more applications, such as software applications, that enable, for example, network communications with one or more components of the system 100, transmit and/or receive data, and perform the functions described herein. Upon execution by the processor 141, the application 143 may provide the functions described in this specification, specifically to execute and perform the steps and functions in the process flows described below. For example, the application 143 may be executed to perform receiving web form data from the user device 120 and the storage device 160, retaining a web session between the user device 120 and the storage device 160, and masking private data received from the user device 120 and the storage device 160. Such processes may be implemented in software, such as software modules, for execution by computers or other machines. The application 143 may provide GUIs through which a user may view and interact with other components and devices within the system 100. The GUIs may be formatted, for example, as web pages in HyperText Markup Language (HTML), Extensible Markup Language (XML) or in any other suitable form for presentation on a display device depending upon applications used by users to interact with the system 100.

The server 140 may further include a display 144 and input devices 145. The display 144 may be any type of device for presenting visual information such as a computer monitor, a flat panel display, and a mobile device screen, including liquid crystal displays, light-emitting diode displays, plasma panels, and cathode ray tube displays. The input devices 145 may include any device for entering information into the server 140 that can be available and supported by the server 140, such as a touchscreen, keyboard, mouse, cursor-control device, microphone, digital camera, video recorder or camcorder. These devices may be used to enter information and interact with the software and other devices described herein.

In some examples, exemplary procedures in accordance with the present disclosure described herein can be performed by a processing arrangement and/or a computing arrangement (e.g., a computer hardware arrangement). Such processing/computing arrangement can be, for example entirely or a part of, or include, but not limited to, a computer/processor that can include, for example one or more microprocessors, and use instructions stored on a non-transitory computer-accessible medium (e.g., RAM, ROM, hard drive, or other storage device). For example, a computer-accessible medium can be part of the memory of the contactless card 110, the user device 120, the server 140, the network 140, and the data storage unit 130 or other computer hardware arrangement.

In some examples, a computer-accessible medium (e.g., as described herein, a storage device such as a hard disk, floppy disk, memory stick, CD-ROM, RAM, ROM, etc., or a collection thereof) can be provided (e.g., in communication with the processing arrangement). The computer-accessible medium can contain executable instructions thereon. In addition or alternatively, a storage arrangement can be provided separately from the computer-accessible medium, which can provide the instructions to the processing arrangement so as to configure the processing arrangement to execute certain exemplary procedures, processes, and methods, as described herein above, for example.

The pipe section 150 can include one or more section of pipe connected together. The pipe sections can comprise one or more valves that can be opened or closed. The pipe sections can be associated with an oil well, oil pipeline, drilling operation, gas well, gas pipeline, water injection well, or water injection pipeline. The sections can be connected in party to a hydraulic power unit, one or more headers, a subsea control module, or valve actuators. The sections can be outfitted to transport one or more fluids. The valves can be configured to measure one or more elements of the pipes or areas surrounding the pipes. As a nonlimiting example, the valves can measure temperature, pressure within the pipe, visibility, the presence of other physical objects, salinity, the presence of other chemicals or fluids, seismic activity, and other observations. The pipes can have a wired connection to the administrator processor or server.

FIG. 2 is a flowchart illustrating a method according to an exemplary embodiment.

The method can involve communication between an administrator processor, one or more pipe sections, and a server. In action 205, one or more valves associated with one or more pipe sections can be opened. This action can be performed by the administrator processor. In action 210, the administrator processor can close the one or more valves that were opened in action 205. In actions 205 and 210, the valves can be opened and closed for a predetermined time period. The time period can be set automatically by a model or manually inputted. In action 215, the administrator processor can record any pressure and temperature changes. For example, the processor can record a drop or increase in pressure. Actions 205-215 can be iterated any number of times. Although FIG. 2 mentions only temperature and pressure, it is understood that the valves can record other elements including without limitation salinity, current, seismic data, visibility, liquid composition, and the presence of other physical objects. In action 220, the administrator processor can compare the recorded changes with historical data. The historical data can be retrieved from a data storage unit or server. The historical data can include temperature, pressure, salinity, current, seismic data, visibility, liquid composition, and the presence of other physical objects. The comparison can determine whether the current records are similar or different to historical data regarding potential changes on or near the pipe section. Upon comparing the current data to historical data, in action 225 the administrator processor can generate a predictive model configured to determine in action 230 whether, based on the current and historical data, a significant change in conditions have occurred. For example, the predictive model can determine that a significant drop in pressure has occurred. As another example, the model can determine that a sudden, unusual, or significant change in temperature or visibility has occurred. Upon determining that a significant change has occurred, the administrator processor can close one or more valves associated with one or more pipe sections. The processor can close a number of valves that are near or closely linked to a pipe section where a leak is occurring. After closing one or more valve, it is understood that the processor may monitor the valves and pipe sections to determine whether the significant change has subsided or otherwise been stopped. If not, the processor can again consult the model or otherwise determine how to proceed, including opening or closing more valves. In addition to closing one or more valves, the administrator processor can generate and transmit an auditory or visual alert in response to determining that there is a leak. The alert can be sent over a wired or wireless network. The alert can be sent to a user device or server.

FIG. 3a-3b illustrate a system of pipe section and an exemplary graphical record of pressure levels according to an exemplary embodiment. In FIG. 3a, one or more sections of pipe can include a positive displacement pump that moves a fluid by repeatedly enclosing a fixed volume and moving it mechanically through the system. The pumping action is cyclic and can be driven by pistons, screws, gears, rollers, diaphragms or vanes. The pipe sections can further include a hydraulic power unit. When a hydraulic power unit begins functioning, the gear pump pulls hydraulic fluid out of the tank and moves it into an accumulator. A hydraulic accumulator is a pressure storage reservoir in which an incompressible hydraulic fluid is held under pressure that is applied by an external source of mechanical energy. The external source can be an engine, a spring, a raised weight, or a compressed gas. This process continues until the pressure within the accumulator reaches a predetermined level, at which point a charging valve switches the pumping action to begin circulating fluid. After the accumulator can be one or more headers such as header 1A and header 1B. Each of the headers 1A and 1B can include one or more valves configured to measure pressure.

FIG. 3b illustrates an example of the recordation made by the administrator processor of the pressure levels from one or more valves. The graph of the pressure of header 1A illustrates a small, insignificant drop in in pressure. But the graph of the pressure of header 1B illustrates steep, significant drops in pressure. Thus, the predictive model may determine that header 1B is experiencing a leak. It is understood that the administrator processor can record the pressure of any number of pipe sections not including headers.

FIG. 4 illustrates an example of the pipe sections according to another exemplary embodiment. The pipe sections can include headers A and B. In addition to a hydraulic power unit, the pipe sections can include a subsea control module, and functions lines and actuators. The predictive model can work in concert with the pipe elements to determine whether a leak has occurred. The developed digitalization process and the algorithm quantifies the leakage rate, identifies topside header (branch) comparative consumption, and finds the leak's subsea source without using a flowmeter by just monitoring pressure sensors. The method is immune to sensor uncertainty. This method minimizes the need for costly ROV deployments and can eliminate the use of highly toxic UV-sensitive dye in hydraulic fluid. The algorithm uses the Hydraulic Power Unit pressure and header pressures and statistically correlates pressure signatures to detect leakage. The statistical method makes the algorithm immune to sensor drifts. The second example of leak detection with pressure coupling monitoring is subsea pipeline liquid dominant low rate production leak detection.

When production-filled pipeline isolated, during ESD (Emergency Shutdown), the subsea manifold pressure is different (usually lower) than the hydrostatic pressure due to specific gravity of the fluid, thermal cooling, and valve isolation sequence. However, if there is a pipeline leak, the manifold pressure couples with the hydrostatic pressure rapidly. By monitoring the pressure coupling behavior, one can detect even minimal pipeline leak rates.

Although embodiments of the present invention have been described herein in the context of a particular implementation in a particular environment for a particular purpose, those skilled in the art will recognize that its usefulness is not limited thereto and that the embodiments of the present invention can be beneficially implemented in other related environments for similar purposes. The invention should therefore not be limited by the above described embodiments, method, and examples, but by all embodiments within the scope and spirit of the invention as claimed.

The predictive models described herein can utilize a Bidirectional Encoder Representations from Transformers (BERT) models. BERT models utilize use multiple layers of so called “attention mechanisms” to process textual data and make predictions. These attention mechanisms effectively allow the BERT model to learn and assign more importance to words from the text input that are more important in making whatever inference is trying to be made.

The exemplary system, method and computer-readable medium can utilize various neural networks, such as convolutional neural networks (CNNs) or recurrent neural networks (RNNs), to generate the exemplary models. A CNN can include one or more convolutional layers (e.g., often with a subsampling step) and then followed by one or more fully connected layers as in a standard multilayer neural network. CNNs can utilize local connections, and can have tied weights followed by some form of pooling which can result in translation invariant features.

A RNN is a class of artificial neural network where connections between nodes form a directed graph along a sequence. This facilitates the determination of temporal dynamic behavior for a time sequence. Unlike feedforward neural networks, RNNs can use their internal state (e.g., memory) to process sequences of inputs. A RNN can generally refer to two broad classes of networks with a similar general structure, where one is finite impulse and the other is infinite impulse. Both classes of networks exhibit temporal dynamic behavior. A finite impulse recurrent network can be, or can include, a directed acyclic graph that can be unrolled and replaced with a strictly feedforward neural network, while an infinite impulse recurrent network can be, or can include, a directed cyclic graph that may not be unrolled. Both finite impulse and infinite impulse recurrent networks can have additional stored state, and the storage can be under the direct control of the neural network. The storage can also be replaced by another network or graph, which can incorporate time delays or can have feedback loops. Such controlled states can be referred to as gated state or gated memory, and can be part of long short-term memory networks (LSTMs) and gated recurrent units.

RNNs can be similar to a network of neuron-like nodes organized into successive “layers,” each node in a given layer being connected with a directed e.g., (one-way) connection to every other node in the next successive layer. Each node (e.g., neuron) can have a time-varying real-valued activation. Each connection (e.g., synapse) can have a modifiable real-valued weight. Nodes can either be (i) input nodes (e.g., receiving data from outside the network), (ii) output nodes (e.g., yielding results), or (iii) hidden nodes (e.g., that can modify the data en route from input to output). RNNs can accept an input vector x and give an output vector y. However, the output vectors are based not only by the input just provided in, but also on the entire history of inputs that have been provided in in the past.

For supervised learning in discrete time settings, sequences of real-valued input vectors can arrive at the input nodes, one vector at a time. At any given time step, each non-input unit can compute its current activation (e.g., result) as a nonlinear function of the weighted sum of the activations of all units that connect to it. Supervisor-given target activations can be supplied for some output units at certain time steps. For example, if the input sequence is a speech signal corresponding to a spoken digit, the final target output at the end of the sequence can be a label classifying the digit. In reinforcement learning settings, no teacher provides target signals. Instead, a fitness function, or reward function, can be used to evaluate the RNNs performance, which can influence its input stream through output units connected to actuators that can affect the environment. Each sequence can produce an error as the sum of the deviations of all target signals from the corresponding activations computed by the network. For a training set of numerous sequences, the total error can be the sum of the errors of all individual sequences.

The models described herein may be trained on one or more training datasets, each of which may comprise one or more types of data. In some examples, the training datasets may comprise previously-collected data, such as data collected from previous uses of the same type of systems described herein and data collected from different types of systems. In other examples, the training datasets may comprise continuously-collected data based on the current operation of the instant system and continuously-collected data from the operation of other systems. In some examples, the training dataset may include anticipated data, such as the anticipated future workloads, currently scheduled workloads, and planned future workloads, for the instant system and/or other systems. In other examples, the training datasets can include previous predictions for the instant system and other types of system, and may further include results data indicative of the accuracy of the previous predictions. In accordance with these examples, the predictive models described herein may be training prior to use and the training may continue with updated data sets that reflect additional information.

In the invention, various embodiments have been described with references to the accompanying drawings. It may, however, be evident that various modifications and changes may be made thereto, and additional embodiments may be implemented, without departing from the broader scope of the invention as set forth in the claims that follow. The invention and drawings are accordingly to be regarded in an illustrative rather than restrictive sense.

The invention is not to be limited in terms of the particular embodiments described herein, which are intended as illustrations of various aspects. Many modifications and variations can be made without departing from its spirit and scope. Functionally equivalent systems, processes and apparatuses within the scope of the invention, in addition to those enumerated herein, may be apparent from the representative descriptions herein. Such modifications and variations are intended to fall within the scope of the appended claims. The invention is to be limited only by the terms of the appended claims, along with the full scope of equivalents to which such representative claims are entitled.

It is further noted that the systems and methods described herein may be tangibly embodied in one or more physical media, such as, but not limited to, a compact disc (CD), a digital versatile disc (DVD), a floppy disk, a hard drive, read only memory (ROM), random access memory (RAM), as well as other physical media capable of data storage. For example, data storage may include random access memory (RAM) and read only memory (ROM), which may be configured to access and store data and information and computer program instructions. Data storage may also include storage media or other suitable type of memory (e.g., such as, for example, RAM, ROM, programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic disks, optical disks, floppy disks, hard disks, removable cartridges, flash drives, any type of tangible and non-transitory storage medium), where the files that comprise an operating system, application programs including, for example, web browser application, email application and/or other applications, and data files may be stored. The data storage of the network-enabled computer systems may include electronic information, files, and documents stored in various ways, including, for example, a flat file, indexed file, hierarchical database, relational database, such as a database created and maintained with software from, for example, Oracle® Corporation, Microsoft® Excel file, Microsoft® Access file, a solid state storage device, which may include a flash array, a hybrid array, or a server-side product, enterprise storage, which may include online or cloud storage, or any other storage mechanism. Moreover, the figures illustrate various components (e.g., servers, computers, processors, etc.) separately. The functions described as being performed at various components may be performed at other components, and the various components may be combined or separated. Other modifications also may be made.

Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device.

Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like, and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, to perform aspects of the present invention.

These computer readable program instructions may be provided to a processor of a general-purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified herein. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the functions specified herein.

The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions specified herein.

Claims

1. A system for detecting changes in pressure and temperature within section of a pipe, the system comprising:

one or more pipe sections comprising one or more valves further configured to open and close for a predetermined time period; and

an administrator processor configured to: open the one or more valves for a first predetermined time period; close the one or more valves for a second predetermined time period; record any changes in one or more pressure levels and temperature levels between the first predetermined time period and the second predetermined time period; compare the recorded changes to one or more historical pressure levels and temperature levels; generate, upon comparing the record changes to the historical pressure levels and temperature levels, a predictive model configured to determine whether a significant change in the pressure levels or temperature levels has occurred; determine by the predictive model whether a significant change in the pressure levels or temperatures has occurred; and close, upon determining that a significant change in the pressure levels or temperature levels has occurred, one or more valves.

2. The system of claim 1, wherein the pipe sections are associated with an oil well, oil pipeline, or a drilling operation.

3. The system of claim 1, wherein the pipe sections are associated with a gas well, gas pipeline, water injection well, or water injection pipeline.

4. The system of claim 1, wherein the administrator processor is further configured to store the recorded changes in temperature levels and pressure levels in a data storage unit.

5. The system of claim 1, wherein the administrator processor is further configured to generate or more graphical representations of the current and historical temperature levels and pressure levels.

6. The system of claim 1, wherein the administrator processor is further configured to send an alert to one or more user devices in response to determining that a significant change in the pressure levels or temperatures has occurred.

7. A method for detecting leaks and ceasing the flow of fluids within one or more pipe sections, the method comprising the following steps:

opening, by a processor, one or more valves associated with one or more pipe sections for a first predetermined time period;

closing, by the processor, the one or more valves for a second predetermined time period;

recording, by the processor, any changes in one or more pressure levels and temperature levels between the first predetermined time period and the second predetermined time period;

comparing the recorded changes to one or more historical pressure levels and temperature levels;

generating, upon comparing the record changes to the historical pressure levels and temperature levels, a predictive model configured to determine whether a significant change in the pressure levels or temperature levels has occurred;

determining, by the predictive model, whether a significant change in the pressure levels or temperatures has occurred; and

closing, upon determining that a significant change in the pressure levels or temperature levels has occurred, one or more valves.

8. The method of claim 7, wherein the steps further comprise storing, by the processor, the recorded changes in temperature levels and pressure levels in a data storage unit.

9. The method of claim 7, wherein the steps further comprise generating, by the processor, one or more graphical representations of the current and historical temperature levels and pressure levels.

10. The of claim 7, wherein the steps further comprise sending, by a processor over a network, an alert to one or more user devices in response to determining that a significant change in the pressure levels or temperatures has occurred.