Data processing: measuring – calibrating – or testing – Testing system – Of sensing device
Reexamination Certificate
2002-04-26
2004-08-03
Noland, Thomas P. (Department: 2856)
Data processing: measuring, calibrating, or testing
Testing system
Of sensing device
C073S001340
Reexamination Certificate
active
06772082
ABSTRACT:
BACKGROUND OF INVENTION
1. Field of Invention
The present invention relates to production systems for the production of oil and gas, and in particular to the validation of sensors installed in such systems for measuring physical properties of the flow through the production system.
2. Description of the Prior Art
Oil and gas production systems typically contain sensors for measuring physical properties of the flow. Sensor measurements in the production system are for instance used as input (initial values) for simulations of the reservoir.
A well-known problem in the oil industry is that sensors in the production system become inaccurate, or even fail completely, after some time in operation. The problems are mainly due to the harsh conditions under which such equipment is operated, such as the high pressure, the high temperature, or the corrosive environment present in the production system.
New oil wells are being equipped with sensors increasingly often. An example of a sensor that is being used in an oil and gas production system is a venturi down-hole flow meter. A venturi down-hole flow meter is a sensor which in itself comprises several sensors such as pressure sensors. It is getting more and more common to install venturi down-hole flow meters in new wells. Venturi down-hole flow meters feed other parts of the production system with essential information: the values from venturi down-hole flow meters are essential for critical functions such as well allocation.
An example of a flow meter based on a process model of an oil well implemented in a data processing system is the software product Wellocate™ (currently known as OptimizeIT Well Monitoring System) supplied by ABB AS of Billingstad, Norway, the assignee of the present application. A paper, “Oil Well Allocation: the Ultimate Interpolation Problem” (L. T. Biegler, A. Bramilla, C. Scali, G. Marchetti (editors), Advanced Control of Chemical Processes 2000, Elsevier, 2001) describes how Wellocate™ identifies the flow rate in an oil well by checking the consistency of an assumed flow rate with the observed pressure and temperature drop over the tubing and the production choke in an oil well.
The sensors in the production system constitute an integral part of the operating philosophy of the oil field and the work routines of the oil field operator. Therefore, it would be beneficial to be able to detect whether a sensor is trustworthy or whether it fails. A common approach to this question has been to define a set of allowable values for a particular sensor. Such an allowable set of values may be constant over time, e.g., the operator may specify a minimal and a maximal value. The allowable set may also depend on historical measurement data, e.g., the operator may specify a maximal rate of change. Unfortunately, it is often very difficult if not impossible to specify an allowable set of values. On the one hand, the set must have a limit that is small enough or sensitive enough to discriminate between correct and incorrect readings of a particular sensor other than those indicating complete failure of the equipment. On the other hand, the allowable set must have a range large enough to accommodate for a wide variety of possible operating conditions of the system, which can be considerable in number and extent.
Another problem related to failing sensors is that, unfortunately, it is often prohibitively expensive to replace such sensors by new ones. This is particularly true when the sensors are installed in remote locations in the production system, such as down in a well or on the bottom of the sea or other body of water. Therefore, it would also be beneficial to have a back-up value or reading available instead of the sensor itself for monitoring purposes when the sensor fails. A possible solution to this problem is to install multiple sensors of the same kind close to each other in the production system in order to obtain redundancy. In case any one of the sensors fails, one of the other sensors may be used to supply the data. This approach is costly and inefficient, since multiple sensors are involved to do the work of one. Another drawback of this approach is that such sensors of the same kind may be subject to the same errors due to the same well conditions.
SUMMARY OF INVENTION
The present invention provides a method performed in a data processing system for detecting sensor failure in an oil and gas production system. The method is performed under control of a set of computer-readable instructions contained in a computer program storage device. With the present invention, an expected value for a measurement from a sensor in the production system is generated. The expected value is compared against an actual measurement. If the expected value is within some acceptable specified limit of the actual measurement, the validity of the actual measurement is confirmed. If such is not the case, a failure of the sensor is indicated. The invention provides a model-based method for generating an expected, or back-up, value for a measurement from a specific sensor. This expected value is the value which is most consistent with the measurements from the other sensors in the system. The model used in the invention comprises descriptions of subsystems of the entire production system.
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Allen John
Bringedal Bjorn Oyvind
Morud Svein Arne
van der Geest Robert Anton Bernard
ABB AS
Noland Thomas P.
Rickin, Esq. Michael M.
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