Intelligent well completion has been used in various applications including but not limited to fields with multiple reservoirs. In such applications, estimation and allocation of downhole flow rates at each reservoir are critical for efficient reservoir management. One way of estimating downhole flow rates is the deployment of dedicated physical zonal flowmeters, using virtual flowmeter techniques based on the architecture of the intelligent well completion or combination of both options. This paper describes the methodology and field wide application of using real time data from an intelligent well to estimate flow rates without the need for a physical flow meter. The real time data are from the installed downhole gauges in the well. This is combined with interval control valves, static and dynamic well information to provide reliable estimate of well production rate. Since downhole pressure, temperature and ICV information is already available in an intelligent well, this technique provides a lower cost option of obtaining zonal and total well production and injection rates. The methodology used incorporates analytical choke equations, tubing performances, and nodal analysis (inflow performance relationship) with other reservoir parameters to build a flow estimation algorithm and model. Various downhole equipment (interval control valves, packers, pressure and temperature sensors etc) and related well information are captured into the system to set initial and final boundary conditions. Well test data can be used to calibrate the system and improve the accuracy of the model. In the field application described, results vary from well to well with field average estimates within +/−10% when compared to measurements from normal surface metering systems. Well tests from the surface measurements were used to calibrate and improve accuracy. The result shows an operating envelope that covers a range of pressure drop across the ICV. The method is capable of handling single and two phase system. Further enhancements are been made to handle multiphase and systems outside the steady state regime. In addition, enhanced data filtering techniques implemented in the system help managed noisy data. The analytical techniques described enhance digital oilfield capability in optimizing production through affordable flow rate estimation for intelligent wells. The technique presented can also be used to increase the reliability of applicable wells since no additional physical hardware is required.

Power supply at any level in the Nigerian Niger Delta is a challenge due to the high rate of vandalism, the lack of infrastructure and the dependence of the Smartfields implementation on the availability of highly reliable power supply systems. For power supply systems to meet the availability and reliability criteria for Smartfields operations, the systems must be vandal proof and renewable with 100% availability and minimal negative impact on the environment. This paper presents the results of field trials of a renewable power supply system suitable for the deployment of Smartfields in remote locations in the oil and gas industry. The power solution is implemented using lithium polymer batteries, explosion proof enclosures and a thermoelectric generator to create a renewable power supply solution. This solution is capable of providing power to all the well head valves and communication infrastructure of both the gas lifted and natural flowing wells with high reliability and availability and also with a very low susceptibility to vandalization and very negligible negative impact on the environment.

This paper presents the report of a pipeline intruder detection system using the Optical Time Domain Reflectometry (OTDR) based Distributed Vibration Sensor technology (DVS). A 12 km optic fiber cable was buried under a 0.9m thick slab of concrete buried 1.6m deep along an SPDC 18" pipeline within the pipeline right of way (ROW). The application of the OTDR was to detect some predefined types of intrusion (walking, digging, driving, etc.,) normally associated with vandalism and bunkering activities along the pipeline ROW. The system was able to identify with sufficient signal clarity; the footsteps of a man weighing about 80kg walking up to 3m near the buried sensor, digging activities at about 3m away from the optic fiber cable, a moving herd of cattle crossing the pipeline ROW from 15m to the ROW, the presence of a wheeled 4x4 vehicle 10m away from the buried cable and a 5 ton truck 50m away from the cable was also detected. The results were displayed on a graphical user interface with different colour codes for each intrusion event or category. The system was found to be able to detect the different possible types of intrusion activities prevalent around pipelines. The concrete slab was found to have minimal effect on the sensitivity of the optic fiber with respect to its ability to detect intrusion activities up to 5m from the optic fiber cable, but for areas without the concrete slab, the system sensitivity much better, hence the received intrusion signal strength was found to be very high. This system can be deployed along our pipeline ROW to provide intrusion detection for SPDC pipelines and provide an early warning system for malicious intents on the pipelines.

The accurate and timely localization of the vandalization and leak point on an oil pipeline provides operators with information to aid with the development of robust security response and intervention plans. These plans have the potential of reducing the impact of leaks on the environment by enabling operators to take actions to mitigate their effect. A major challenge with current leak and vandalization detection systems is the generation of spurious signals which in time slows down the response to these alerts. This paper presents results of the field trial of a Fiber Optic Cable based Oil/ Gas leak and intruder detection system. Oil and Gas leaks were simulated on a pipeline section buried in a swamp location with 1mm and 2mm Orifices located at the 0o, 90o and 180o positions on the pipe with a section of the pipe exposed for third party intruder detection tests. The orifices were connected to compressed air and water used in place of Oil and gas. The fiber optic cable was buried on both sides of the pipeline and hooked up to the Helios Integrator. The system was able to detect and localize leaks from the orifices with the signal intensity proportional to the leak intensity. It was able to detect third party activities such as cutting and the use of hammers on the pipeline and also walking near the pipeline. The results coupled with the security intervention plan which is developed to provide varying levels of response will eliminate response to spurious signals thus providing a robust response and intervention plan to oil and gas leak and intruder detection. File Size 338 KB Number of Pages 8 Some of the OnePetro partner societies have developed subject-specific wikis that may help.

Manual data acquisition of pressure readings in order to build a static pipeline hydraulic profile for a pipeline network, increases the security exposure of personnel, logistics cost with the attendant delay in determining and reporting pipeline pressure used in Hydrocarbon accounting, making it prone to errors, inaccurate and unrepresentative of the actual situations on the network. This paper presents results of a wireless pipeline real time (RT) pressure monitoring system using low power wireless transmitters installed at selected pressure points on the pipeline networks located in remote areas of SPDC operations. The system utilizes secure wireless transmission and special encryption systems designed to protect the data transmission from interference and degradation. The system achieved a data transmission over a 10km range from a pipeline pressure point to the gateway, with a battery life of over 6 months. Longer battery life durations can be achieved by the deployment of exception based reporting. The system provides a means of monitoring the pipeline pressure and thus enables the development of a dynamic pipeline pressure profile for the monitored pipelines. The data from the system can also be used with special algorithms to monitor the pipeline for leaks. The system versatility has also been tested as means of collating vital well data to a data concentrator enroute an enterprise intranet