Oilwell Monitoring and Control based on Wireless Sensor Networks ...

International Journal of Electronics and Computer Science Engineering Available Online at www.ijecse.org

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ISSN- 2277-1956

Oilwell Monitoring and Control based on Wireless Sensor Networks using ARM V. Viknesh Velavan 1, R.Chandralekha 2 P.G.scholar (1), Assistant Professor (2) Veltech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College Email: Id: [email protected] 1, [email protected] 2 Abstract- The existing oil-pumping unit (OPU) system has a high power consuming process. It has the incapability of OPU’s structural health monitoring. A sensor network based intelligent control is proposed for power economy and efficient oilwell health monitoring using wireless sensor network. The proposed system consists of three-level sensors: First level sensors (FLS) – designed with a temperature sensor, a voltage sensor, a current sensor, level sensor, gas sensor and a pressure sensor used for oilwell data sensing. Intelligent sensors (IS) - designed mainly for an oilwell’s data elementary processing, main fault alarm indication, typical storage/indication, data/status transmission up to the third level sensor (TLS), data/status transmission between IS, and command transmission down to the OPU motor. Third level sensors (TLS) Software-defined (SD) control centres with an embedded database.

The TLS are designed for hundreds of oilwell’s data storage/management, data processing malfunction detection, malfunction alarm/indication; stroke-adjustment command transmission down to a specific IS for power economy and the malfunction report to the maintenance staff. Timer, Keyboard, A/D, communication interruptions are controlled by intelligent sensor. Delay aware data collection network structure is used for power economy and to reduce time delay in wireless sensor networks. Index Terms - Monitoring, Intelligent sensor control, Wireless sensor network, Power consumption. I.INTRODUCTION Sensor networks have drawn much attention for their broad practical applications-investigate specific sensors and sensor networks for air-craft structural health and performance monitoring. A real-time radiological area monitoring sensor network is developed in for emergency response. In this paper, a sensor network based intelligent system is proposed for remote oilwell health monitoring and automatic oil-pumping control. The motivation of developing this system is that 1) due to the special nature of oil exploration and oil drilling, the majority of oil pumping units are spread over barren hills, mountains and deserts, and 2) the existing oil-pumping systems still adopt manual control. Existing manual control systems have three evident drawbacks: 1) The OPU administrators have to frequently go to the oilfield to check the OPU status and collect its health analysis data. For the sake of the harsh oilfield environment, especially in the winter when it is chilly and snowing overspreading the whole oilfield, it is quite difficult to effectively manage and maintain all OPU manually. 2) Power consumption for OPU is huge during the oil-pumping process. Especially in barren oilwells, power wastage is extremely high because each oil-pumping is not filled under such condition and thus oil production greatly drops even though the OPU pumping stroke remains high. And 3), since an administrator has to take charge of a number of oilwells, an OPU malfunction is difficult to locate and repair in a reasonable time, which causes an oil production drop. To overcome these three disadvantages of the existing manual control system, a sensor network based automatic control system is proposed for OPU management and oilwell health monitoring. This proposed system consists of three-level sensors: the fist level sensors (FLS), the intelligent sensors (IS) and Third level sensors (TLS).

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IJECSE,Volume1, Number 4 V. Viknesh Velavan and R.Chandralekha

II. RELATED WORKS Wireless sensor networks consist of large amounts of wireless sensor nodes, which are compact, light-weighted, and battery-powered devices that can be used in virtually any environment. Because of these special characteristics, sensor nodes are usually deployed near the targets of interest in order to do close-range sensing. The data collected will undergo in network processes and then return to the user who is usually located in a remote site. Most of the time, wireless sensor nodes are located in extreme environments, where are too hostile for maintenance. Sensor nodes must conserve their scarce energy by all means and stay active in order to maintain the required sensing coverage of the environment. Due to the energy constraint of individual sensor nodes, energy conservation becomes one of the major issues in sensor networks. In wireless sensor networks, a large portion of the energy in a node is consumed in wireless communications. The amount of energy consumed in a transmission is proportional to the corresponding communication distance. Therefore, long distance communications between nodes and the base station are usually not encouraged. One way to reduce energy consumption in sensor networks is to adopt a clustering algorithm. A clustering algorithm tries to organize sensor nodes into clusters. Within each cluster, one node is elected as the cluster head. The cluster head is responsible for: 1) collecting data from its cluster members; 2) fusing the data by means of data/decision fusion techniques; and 3) reporting the fused data to the remote base station. In each cluster, the cluster head is the only node involved in long distance communications. Energy consumption of the whole network is therefore reduced. III.NETWORK STRUCTURE The proposed network structure is a tree structure. To deliver the maximum data collection efficiency, the number of nodes N in the proposed network structure has to be restricted to N=2p, where p=1, 2.... It is shown in a later part that such restriction can be relaxed by giving up some performance. Each cluster member will be given a rank, which is an integer between 1 and p. A node with rank k will form data links k-1 with k-1 nodes, while these k-1 nodes are with different ranks starting from 1, 2.., up to k-1. All these k-1 nodes will become the child nodes of the node with rank k. The node with rank k will form a data link with a node with a higher rank. This higher rank node will become the parent node of the node with rank k. The cluster head will be considered as a special case. The cluster head is the one with the highest rank in the network. Instead of forming a data link with a node of higher rank, the cluster head will form data link with the base station. An example of the network with N=16 is shown in Fig.1. In this example, it takes 5xT, for the base station to collect all data from 16 nodes. By dividing the time domain into time slots of durations T, the above process will last for five time slots.

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2441 Oilwell Monitoring and Control based on Wireless Sensor Networks using ARM

Fig.1. Network structure with network size N=16 A) Top-Down Approach: The top-down approach is a kind of centralized control algorithm. In this approach, the base station is assumed to have the coordinates of all sensor nodes in the network. The whole approach is going to be executed at the base station. At the end of the optimization process, the base station will instruct the sensor nodes to establish the essential data links and form the appropriate network structure. B) Bottom-Up Approach: Basically, the operation of the bottom-up approach is to join clusters of the same size together. The bottom-up approach is, when comparing with the top-down approach, more scalable. It can be implemented in either centralized or decentralized manner. IV.NETWORK TOPOLOGY The proposed system comprised of our developed Third level sensor, each of which is wirelessly communicates with hundreds of IS. Each IS designed with the capability of data transferability with a set of FLS. The adjacent IS and its corresponding TLS has the capability of command transmission to its OPU motor. Each group of FLS, including a voltage sensor, a current sensor, temperature sensor, gas sensor, level sensor and a pressure sensor, are utilized for data sensing from an OPU. The FLS convert all measurements into electrical signals and then transport them into its corresponding IS. The developed IS has the following feature: • Setting oilwell static parameters: manual input, edition and interface indication • Storage and indication of sensing data from FLS • Significant malfunction detection and indication based on the elementary processing of data, such as short circuit and overcurrent • Relay protection: the power will be cut off when the phase is missing or overcurrent occurs • Relay data/status transmission from another IS to FLS when there is a communication failure between another IS and FLS • Receiving TLS command and transmitting it to OPU motor for stroke adjustment and power efficiency

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Fig.2. System topology for OPU monitoring and control The developed SD-TLS can be summarized as follows: • Conducting a regular data request on all its IS every eight hours via a developed wireless communication protocol, where all IS, one after another, transmit their data to TLS. • Conducting a data request on a specific IS whenever it is required by staff. • Storage (using database) and indication of data from all IS, where data commonly consists of OPU static parameters, significant malfunction reports, sensing data and elementary processing data. • Further data processing for recommending/transmitting the optimal pumping stroke to the IS for power economy as well as more oil production. • False alarm and false indication once any malfunction has been found out. • Sending the detected oilwell malfunction out to the maintenance staff using GSM-SMS.

V.SYSTEM DESCRIPTIONS The IS consists of the following six modules: a central processing unit(CPU) module, a sensing module, a relay protection module, a frequency converter module, a wireless communication module and a user interface module. 1) CPU Module: The CPU in our system is the ARM LPC2148 microcontroller based on a 16-bit/32-bit ARM7TDMI-SCPU with real-time emulation and embedded trace support, that combine microcontroller with embedded high speed flash memory ranging from 32 kB to 512 kB. Serial communications interfaces ranging from a USB 2.0 Full-speed device, multiple UARTs, SPI, SSP to I2C-bus and on-chip SRAM of 8 kB up to 40 kB.32-bit timers, single or dual 10-bit ADC, 10-bit DAC, PWM channels and

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45 fast GPIO lines with up to nine edge or level sensitive external interrupt pins make these microcontrollers suitable for industrial control and medical systems. 2) Sensing Module: Sensing module contains temperature sensor, a voltage sensor, a current sensor, level sensor, gas sensor and a pressure sensor. The voltage sensor and the current sensor are to measure the instantaneous voltage and the current of power supply, respectively. The oil pressure sensor is to measure the oil pressure of the oil pipe. Temperature sensor used to measure temperature of oilwell. Level sensor is to measure level of oilwell. Gas sensor is to measure gas effect in oilwell. 3) Relay Protection Module: Relay protection module consists of a circuit breaker, a contactor and a connection circuit. This module is used for detecting the motor operation by analyzing sensing data from the voltage sensor and the current sensor. Once a malfunction, such as a short circuit, a phase missing or an overcurrent, occurs on the motor power, relay protection module will immediately cut off the power supply. 4) Interface Module: Interface module includes keyboard, LCD, indicator lights, a buzzer, power switch, start button and stop button. The module is the interface between staff and IS, by which OPU can be started or stopped, the stroke of OPU can be changed, preset and the malfunction information can be acquired. 5) Frequency Converter Module: Frequency converter module contains frequency converter and a braking resistor. It reduces inrush to OPU and motor. This module can adjust the motor speed according to the received command from CPU so that the stroke of OPU is adjusted and the power is saved. 6) Wireless Communication Module: ZIGBEE module is used for wireless transmission between third level sensor and intelligent sensor. VI.EMBEDDED SOFTWARE DEVELOPMENT The proposed IS, embedded software development for the CPU contains 4 tasks and 4 interruptions.4 tasks consist of sensing data collection from FLS, data elementary processing, significant malfunction scan and user interface response. Sensing data collection indicates collecting dynamic sensing data and automatically storing these data in the IS. Data elementary processing means to process the raw sensing data to acquire the required typical data, such as the maximal/minimal/average value, the active/reactive power and the system efficiency, etc... Significant malfunction scan is to detect the severe malfunctions, such as short circuit, missing phase and overcurrent, and report them. Also, the user interface is in charge of the external input, the maximal oil production with optimal power efficiency. Therefore, the optimal pumping stroke is obtained by maximizing the oil production or the system efficiency. The priority of the 4 tasks, where sensing data collection has the highest priority, the next is data elementary processing, followed by significant malfunction scan and the last is the user interface response.

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Fig.3. CPU Interruptions The 4 interruptions timer interruption, A/D interruption, communication interruption and keyboard interruption are controlled by CPU. Timer interruption is to precisely control sampling rate. A/D interruption guarantees the sampling process. Communication interruption is used for data wireless transmission to other sensors and for data/command reception from other sensors. Keyboard interruptions guarantees that the external input can be responded to CPU in time. VII.DEVELOPMENTS OF TLS A) Malfunction diagnosis: This project considers the 4 most important oilwell malfunctions including (1) Underground oil shortage, (2) gas effect, (3) pumping rod broken off and (4) oil pump serious leakage. The malfunction transmission to the related maintenance staff is accomplished using GSM SMS. The subfunction of GSM short message transmission sends the OPU malfunction name to all maintenance staffs one by one. The malfunctions are stored in the database of processor for further reference.

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Fig.4. TLS data processing: Parameter calculations B) Pumping Stroke Adjustment: Pumping stroke adjustment is another significant feature the proposed sensor network based automatic control system. This is especially true in a oilfield where automatic pumping stroke adjustment guarantees both oil production and power efficiency. The OPU is a huge power consuming device and automatic pumping stroke adjustment may save a considerable power. Due to the constraint of the motor belt, the OPU’s pumping stroke availably ranges from 2 to 10. The recommended pumping stroke always leads to an optimal performance for a given situation, i.e., the oilwell static parameter storage and the static parameter update.

VIII CONCLUSIONS Most oil pumping units have been using manual control in oil field. In this paper, a sensor network based oilwell remote health monitoring and intelligent control system was proposed. This proposed system consists of three-level sensors: the FLS, the IS and the TLS. The FLS have been used for an oilwell’s data sensing, including a temperature sensor, a voltage sensor, a current sensor, level sensor, gas sensor and a pressure sensor for each oilwell. The IS was designed mainly for an oil well’s data elementary processing, main fault alarm/indication, typical data storage/indication, data/status transmission up to the TLS, data/status transmission between IS, command transmission to the OPU motor. The SD TLS was designed for hundreds of oilwell’s data storage/management, data processing malfunction detection, malfunction alarm/indication; stroke-adjustment command transmission to a specific IS. TLS also used for power economy and malfunction reporting to maintenance staff via GSM SMS. Delay aware data collection network structure is used for power economy and to reduce time delay in wireless sensor networks. REFERENCES [1] C. Cheng, C. Tse, and F. Lau, “A delay-aware data collection network structure for wireless sensor networks,” IEEE Sensors J., vol. 11, no. 1,Apr. 2011. [2] D. G. Senesky, B. Jamshidi, K. Cheng, and A. P. Pisano, “Harsh environment silicon carbide sensors for health and performance monitoring of aerospace systems: A review,” IEEE Sensors J., vol. 9, no. 11, pp. 1472–1478, Nov. 2009. [3] C. Cheng, C. Tse, and F. Lau, “A clustering algorithm for wireless sensor networks based on social insect colonies,” IEEE Sensors J., vol. 11, no. 1, Apr. 2011.

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[4] M. C. Rodriguez-Sanchez, S. Borromeo, and J. Hernandez-Tamames, “Wireless sensor network for conservation and monitoring cultural assets,” IEEE Sensors J., vol. 11, no. 1, Apr. 2011. [5] Q. Ling, Z. Tian, Y. Yin, and Y. Li, “Localized structural health monitoring using energy-efficient wireless sensor networks,” IEEE Sensor J., vol. 9, no. 11, pp. 1596–1604, Nov. 2009.

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