SYSTEM OF MONITORING THE DISPLACEMENT OF POSITIVE DISPLACEMENT PUMPS WITHIN OIL AND GAS INDUSTRIES USING SENSORY AND CLOUD TECHNOLOGY

Abstract

This invention provides a real-time monitoring solution for the displacement of Positive Displacement Pumps (DPP) in the oil and gas industries. Utilizing the DPP_MMCMote system, which integrates a Raspberry Pi Processor Board, GPU Stick, Accelerometer, Vibration Sensor, HMI Display, and cloud technology, the method allows continuous displacement tracking and data analysis through machine learning algorithms. The cloud server stores and processes data for remote access, offering operators and maintenance teams insights via a web dashboard. This system enhances operational efficiency and reduces downtime by enabling proactive maintenance and real-time response to potential pump issues.

Specification

Description:FIELD OF THE INVENTION
This invention relates to System of Monitoring the Displacement of Positive Displacement Pumps within Oil and Gas Industries using Sensory and Cloud Technology.
BACKGROUND OF THE INVENTION
The purpose of this state-of-the-art monitoring system is to track displacement in Positive Displacement Pumps (DPP) in the oil and gas industries in real-time. The system uses a combination of sensors and cloud computing to collect raw data from vibration sensors and accelerometers placed at crucial points while the pumps are operating. Then, this information is sent to a specified cloud server, where sophisticated machine learning algorithms process and calculate the displacement. The results are then communicated to operators and authorized staff via an intuitive interface, providing them with real-time and historical data, analytics, and alarms.
One of the major challenges faced by the oil and gas industry is effectively managing the Positive Displacement Pump (DPP) displacement. Current monitoring methods often do not provide real-time functionality and comprehensive data analysis, which can lead to unexpected downtime and operational inefficiencies. It is imperative that a ground-breaking solution that seamlessly integrates cloud-based systems and sensory technologies be developed to address this difficulty.
US10871157B2 - Embodiments of the present invention relate generally to certain types of reciprocating positive-displacement pumps (which may be referred to hereinafter as "pods," "pump pods," or "pod pumps") used to pump fluids, such as a biological fluid (e.g., blood or peritoneal fluid), a therapeutic fluid (e.g., a medication solution), or a surfactant fluid. The pumps may be configured specifically to impart low shear forces and low turbulence on the fluid as the fluid is pumped from an inlet to an outlet. Such pumps may be particularly useful in pumping fluids that may be damaged by such shear forces (e.g., blood, and particularly heated blood, which is prone to hemolysis) or turbulence (e.g., surfactants or other fluids that may foam or otherwise be damaged or become unstable in the presence of turbulence). Sensor based Cloud Technology to monitor the Displacement of Positive Displacement Pumps that helps in maintenance and replacement is the novelty of the system.
AU2016203015B2 - The invention relates to a positive displacement pump, comprising a drive unit and a pump unit having several working chambers, several displacement elements, and at least three cylinders, preferably exactly three cylinders, wherein the pump unit is double-acting. The invention further relates to a piston diaphragm pump forming a positive displacement pump, wherein a diaphragm stroke is caused by means of working liquid present on the first side of a diaphragm and a medium to be pumped is conducted through a diaphragm chamber bounded by the second side of the diaphragm due to the diaphragm stroke, and the diaphragm stroke is caused at a diaphragm position different from a vertical position of the diaphragm. Sensor based Cloud Technology to monitor the Displacement of Positive Displacement Pumps that helps in maintenance and replacement is the novelty of the system.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
Using a combination of sensor devices and cloud technology, the DPP_MMCMote monitoring system is designed to supervise the displacement of Positive Displacement Pumps (DPP) in the Oil and Gas Industries. The Raspberry Pi Processor Board, GPU Stick, Accelerometer, Vibration Sensor, HMI Display, Buzzer, and Power Supply are some of its essential parts. The initial step in using DPP_MMCMote is gathering raw data from sensors that have been deployed, such as the accelerometer and vibration sensor. These sensors are placed in strategic locations to get relevant data regarding the displacement of Positive Displacement Pumps when they are operating. A dedicated cloud server created to satisfy the particular needs of this innovation receives the collected data after then. Following its transfer to the cloud, machine learning (ML) algorithms are used to process the raw material. By using observable patterns and characteristics in the sensor data, these algorithms are trained to compute the displacement of Positive Displacement Pumps.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: SYSTEM ARCHITECTURE
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms "a"," "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises," "comprising," "includes" and/or "including," when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", "third", and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
Using a combination of sensor devices and cloud technology, the DPP_MMCMote monitoring system is designed to supervise the displacement of Positive Displacement Pumps (DPP) in the Oil and Gas Industries. The Raspberry Pi Processor Board, GPU Stick, Accelerometer, Vibration Sensor, HMI Display, Buzzer, and Power Supply are some of its essential parts. The initial step in using DPP_MMCMote is gathering raw data from sensors that have been deployed, such as the accelerometer and vibration sensor. These sensors are placed in strategic locations to get relevant data regarding the displacement of Positive Displacement Pumps when they are operating. A dedicated cloud server created to satisfy the particular needs of this innovation receives the collected data after then. Following its transfer to the cloud, machine learning (ML) algorithms are used to process the raw material. By using observable patterns and characteristics in the sensor data, these algorithms are trained to compute the displacement of Positive Displacement Pumps.
The authorized staff and operators receive precise and up-to-date information regarding the displacement of the pumps from the ML-powered computations. Numerous channels, such as the device's own Human-Machine Interface (HMI) Display, provide access to this data. In addition, a user-friendly online dashboard that can be accessed via the internet offers real-time and trending data, comprehensive analytics, and notifications. The web dashboard and the on-device HMI Display together make up the system's user interface, which provides a comprehensive and easy-to-use platform for Positive Displacement Pump monitoring. Real-time tracking of displacement data, performance trend analysis, and notifications for abnormalities or possible problems are all available to users.

BEST METHOD OF WORKING
A method for monitoring the displacement of Positive Displacement Pumps (DPP) in the oil and gas industries, comprising a DPP_MMCMote system with a Raspberry Pi Processor Board to process real-time data and integrate cloud connectivity for proactive maintenance.
A method including an Accelerometer and Vibration Sensor strategically placed to capture real-time data on pump displacement, supporting predictive maintenance and operational efficiency.
A method incorporating a GPU Stick that accelerates the processing of machine learning algorithms, enabling quick analysis of sensor data for accurate displacement tracking.
A method using a cloud server for data storage and remote access, providing authorized personnel with real-time analytics and historical data via a web dashboard.
A method wherein an HMI Display offers on-site visualization of displacement data, analytics, and alerts, allowing operators to monitor pump performance effectively.
A method including a Buzzer that provides auditory alerts for immediate response to detected anomalies, enhancing maintenance efficiency.
A method with a Power Supply that maintains consistent operation of the monitoring system, ensuring reliable real-time tracking of Positive Displacement Pumps in oil and gas applications.
ADVANTAGES OF THE INVENTION
1. The DPP_MMCMote uses machine learning algorithms, cloud computing, and state-of-the-art sensors to provide a sophisticated real-time monitoring solution that is revolutionizing the oil and gas industry. With the help of this cutting-edge technology, Positive Displacement Pump displacement is carefully tracked, guaranteeing optimal performance, preventative maintenance, and reduced downtime.
2. DPP_MMCMote's integration of the GPU Stick greatly increases the system's processing capacity, enabling the analysis of sensor data quickly and effectively as well as accelerating sophisticated machine learning algorithms. The oil and gas industry's Positive Displacement Pumps can monitor displacement with greater precision thanks to this acceleration.
3. To record dynamic movement data and vibrations related to positive displacement pumps, the integrated accelerometer and vibration sensor in the DPP_MMCMote collaborate. In the oil and gas sector, this cooperative feature promotes precise displacement monitoring, predictive maintenance, and operational effectiveness.
4. The HMI Display in DPP_MMCMote serves as an easy-to-use user interface that gives operators access to real-time and trending displacement data, analytics, and alarms. In the oil and gas sector, this functionality guarantees effective monitoring and facilitates prompt decision-making for Positive Displacement Pumps.
, Claims:1. A system for monitoring the displacement of Positive Displacement Pumps (DPP) in the oil and gas industries, comprising a DPP_MMCMote (100) system with a Raspberry Pi Processor Board (170) to process real-time data and integrate cloud connectivity for proactive maintenance.
2. The system as claimed in Claim 1, wherein an Accelerometer and Vibration Sensor are strategically placed to capture real-time data on pump displacement, supporting predictive maintenance and operational efficiency.
3. The system as claimed in Claim 1, further comprising a GPU Stick that accelerates the processing of machine learning algorithms, enabling quick analysis of sensor data for accurate displacement tracking.
4. The system as claimed in Claim 1, incorporating a cloud server for data storage and remote access, providing authorized personnel with real-time analytics and historical data via a web dashboard.
5. The system as claimed in Claim 1, wherein an HMI Display offers on-site visualization of displacement data, analytics, and alerts, allowing operators to monitor pump performance effectively.
6. The system as claimed in Claim 1, including a Buzzer that provides auditory alerts for immediate response to detected anomalies, enhancing maintenance efficiency.
7. The method as claimed in Claim 1, wherein a Power Supply maintains consistent operation of the monitoring system, ensuring reliable real-time tracking of Positive Displacement Pumps in oil and gas applications.

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