GB2553299A

GB2553299A - Monitoring operational performance of a subsea pump for pumping product from a formation

Info

Publication number: GB2553299A
Application number: GB1614613.6A
Authority: GB
Inventors: Høy Christian; Hornaes Kristin; Arnesen Gudbrand
Original assignee: Aker Solutions Ltd
Current assignee: Aker Solutions Ltd
Priority date: 2016-08-29
Filing date: 2016-08-29
Publication date: 2018-03-07
Anticipated expiration: 2036-08-29
Also published as: GB201614613D0; GB2553299B

Abstract

A system for monitoring the operational performance of a subsea pump for pumping product from a formation uses stored data representative of pump performance and the operating conditions of the pump, derived from a pump performance prediction model. In use, a controller receives an operating condition data stream derived from a subsea sensing system. Predicted pump performance data is contemporaneously retrieved from the stored pump performance prediction model based on a latterly received instant of the operating condition data. Actual pump performance data is also continually received, derived from the sensor system of subsea pump. The controller continually provides a comparison of synchronised latterly retrieved predicted pump performance data with a latterly received actual pump performance data. A method for generating the model data is also disclosed comprising controlling a pump to operate in various conditions, with various densities and gas volume fractions of product, and storing data representative of pump performance.

Description

(71) Applicant(s):

Aker Solutions Limited

Building 6 Chiswick Park, 566 Chiswick High Road, London, Greater London, W4 5HR, United Kingdom (56) Documents Cited:

WO 2015/153621 A1 WO 1997/008459 A1 US 20100257410 A1 KR20150108976

WO 2015/128680 A1 US 20150300156 A1 (58) Field of Search:

(72) Inventor(s):

Christian Hoy Kristin Hornaes

INT CL E21B, F04B Other: WPI, EPODOC, TXTA

Gudbrand Arnesen (74) Agent and/or Address for Service:

HGF Limited

Document Handling - HGF - (London), 1 City Walk, LEEDS, LS11 9DX, United Kingdom (54) Title of the Invention: Monitoring operational performance of a subsea pump for pumping product from a formation

Abstract Title: Monitoring subsea pump performance by comparing measured and model data (57) A system for monitoring the operational performance of a subsea pump for pumping product from a formation uses stored data representative of pump performance and the operating conditions of the pump, derived from a pump performance prediction model. In use, a controller receives an operating condition data stream derived from a subsea sensing system. Predicted pump performance data is contemporaneously retrieved from the stored pump performance prediction model based on a latterly received instant of the operating condition data. Actual pump performance data is also continually received, derived from the sensor system of subsea pump. The controller continually provides a comparison of synchronised latterly retrieved predicted pump performance data with a latterly received actual pump performance data. A method for generating the model data is also disclosed comprising controlling a pump to operate in various conditions, with various densities and gas volume fractions of product, and storing data representative of pump performance.

FIG. 7

At least one drawing originally filed was informal and the print reproduced here is taken from a later filed formal copy.

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0

MONITORING OPERATIONAL PERFORMANCE OF A SUBSEA PUMP FOR PUMPING

PRODUCT FROM A FORMATION [0001] This disclosure relates to condition and performance monitoring of subsea apparatus. In particular, it relates to apparatuses, methods and computer readable media for monitoring operational performance of a subsea pump for pumping product from a formation

BACKGROUND [0002] To extract oil and gas product from geological formations in subsea reservoirs located under the sea bed, a wellbore can be drilled to the formation and a subsea production system can be installed to interface between the downhole apparatus and topside and effect the recovery of product through a pipeline. Some or all of the components making up the subsea production system may be provided on one or more of the seabed itself, in a floating location subsea, or at a topside location. Subsea production systems thus comprise apparatus that may be installed in relatively difficult-to-access locations and to be effective in their operation the may be reliably controllable and operable for timespans on the order of the productive life of the well in such a way as to efficiently recover the product from the well. To aid this effective and reliable control and operation, the condition and performance of components of the subsea apparatus may be monitored to determine the integrity and operational effectiveness of the subsea production system.

[0003] It is in the above context that the disclosure of the present application has been devised.

BRIEF SUMMARY OF THE DISCLOSURE [0004] The subsea production system may include one or more subsea pumps to control the production or to transport the hydrocarbons from the well to the sea surface or to land. Monitoring of the condition and performance of the pump system is of importance to achieving the efficient and effective operation of the subsea production system [0005] Where monitoring systems for subsea pumps are provided that provide data for use in assessing the condition of the pump off line, sensor data taken as intermittent samples from a pump sensor system is logged at intervals on the order of at least a minute. When retrieved, this logged data enables periodic off line analysis, typically performed topside or onshore, of pump condition which allows a degree of diagnostic and prognostic assessment to be made, which may help with planning pump maintenance.

[0006] In accordance with some of the examples the present disclosure, as will be described in greater detail below, the subsea production apparatus may include a condition and performance monitoring system, particularly a pump monitoring system that may monitor the operating condition of the pump at a high sampling rate and may provide an operator with a continually updated report of the actual performance of the pump online at the same time the pump is in operation, effectively in real time in relation to the instant performance of the pump. The reporting of the actual performance of the pump may be at a user interface of a workstation located topside or even onshore, for example in the form of a live pump performance plot.

[0007] In accordance with the examples described above, the condition and performance monitoring system for the subsea pump enables an instant assessment of the pump condition and performance by an operator of the subsea production apparatus at a work station.

[0008] The online reporting of the report of the actual performance of the pump may be provided by the pump monitoring system together with a comparison with a synchronised predicted performance of the pump on the fly,. The predicted pump performance can be retrieved in real time based on latterly received pump operating conditions from a pump performance prediction model storing data representative of the pump performance and the operating conditions of the pump.

[0009] This continuous and seamless data creation and monitoring of the live performance and condition of the pump and the comparison with a synchronised predicted pump performance enables a seamless and instant view to be taken by a human operator or control system as to the pump performance and condition. The system may enable the instant comparison of the operation of the pump against its predicted, or as-new, performance, and its operation outside its operational envelope or operation at one or more monitored operational states or conditions, which can lead to excessive wear and damage of the pump. Further, the system may enable the operator or a monitoring and control system to see online when the pump is not operating at its maximum efficiency. This may facilitate the online control of the operation of the pump, for example, to adjust the operation of the pump to increase its efficiency or avoid excessive wear conditions. For example, without the pump condition and performance monitoring system providing online and seamless data on pump condition and performance relative to predicted pump performance, the operator cannot easily assess whether the pump is operating at optimum efficiency. Indeed, the system may provide a prediction of the most efficient operating point. Similarly, without example systems of the present disclosure, the operator cannot easily determine when the pump is operating in a state where it is actually experience excessive wear. As a result, without information provided by example pump monitoring systems of the present disclosure, a pump operator may choose to avoid operating a pump at shaft speeds where the risk of potentially damaging vibration or cavitation in the pump occurring is higher, regardless of whether the pump could be most efficiently operated in these conditions. In contrast, when example pump performance and condition monitoring systems of the present disclosure are used, by which instant and seamless efficiency and condition information can be provided, the operator may choose to operate the pump to balance the maximum efficiency achievable while taking into account the actual instant operating state of the pump so as to intervene and take action to mitigate any wear. Without this information, or with intermittent information only being available offline, the operation of the pump is carried out blind and sub-optimal performance and higher wear is realised. Further still, without this information, the pump may regularly be operated in excessive wear states without the operator knowing, even after offline analysis, which can shorten the maintenance lifecycle of the pump. Thus in accordance with certain examples of the present disclosure, immediate-term and long term improvements in pump performance and condition are provided in that pump operation is enabled in such a way as to allow improved overall pump performance and operation efficiency, and to reduce wear and extend pump maintenance lifecycles.

[0010] In accordance with some of the examples the present disclosure, the pump performance prediction model data may be generated at least partly empirically through testing of a standard pump of the type to be monitored or through factory acceptance testing of the actual pump to be monitored. The pump performance prediction model data may also be generated partly based on a theoretically-derived standard model of the performance of a pump of the type being monitored. The pump performance prediction model data may be stored in a Look-Up Table, from which it is easily retrievable. In this way, to allow a comparison with the actual instant pump performance, the performance of the pump can be predicted in an as new state can be retrieved in real time synchronisation, online.

[0011] In accordance with some of the examples the present disclosure, the subsea production apparatus may enable monitoring of the operational performance of a subsea pump for pumping product from a formation against pump operational state alert criteria. In this way, the operation of the pump against certain criteria, for example set operational limits for the pump, may be tracked in real time. Operational state alert criteria may be defined that provide an operational state model for the pump, for example defining normal or abnormal, desired or undesired, unremarkable and remarkable operation of the pump. An envelope of operational states representing acceptable performance can in this way be defined and operation in states outside this envelope to be monitored during use. Ongoing analysis of seamless pump operation data in context of an operational state model for the pump thus allows a persistent record of pump operational history, in particular the occurrence of operational states that contribute to the (excessive) wear and tear of the pump. In this way, where the pump is effectively monitored in real time, the entire operational history of the pump can be captured, with all occurrences of the pump being operated in a certain way which may have an impact on the life and maintenance lifecycle of the pump being recorded. This recording of the operational history of the pump would otherwise not be captured with only intermitted, after-the-event monitoring of pump performance. Having knowledge of pump operation in this way allows pump operators and providers to better manage the provision of pump assets. For example, by knowing if the pump has been operated in an acceptable way so as to avoid excessive wear, the scheduling, cause of and liability for maintenance work on the pump can be better understood. Additionally, having access to this operational state record of a pump’s operational experience and additionally having pump operating condition data can allow pumps to be provided to operators in different ways, for example by provision ofthe pump as a service, where the pump service can be delivered as hours of normal operation, volume pumped, etc.

[0012] According to some of the example embodiments, the disclosure may provide apparatus for monitoring the operational performance of a subsea pump for pumping product from a formation, the apparatus comprising: machine readable storage comprising a pump performance prediction model storing data representative of the pump performance and the operating conditions of the pump; and a controller configured to, in operational use: continually receive an operating condition data stream comprising data representative of an operating condition of the pump derived from a subsea sensing system; contemporaneously retrieve predicted pump performance data from the stored pump performance prediction model based on a latterly received instant of the operating condition data stream data; continually receive an actual pump performance data stream comprising data representative ofthe actual operational performance ofthe pump derived from the sensor system of subsea pump; and continually provide a comparison of synchronised latterly retrieved predicted pump performance data with a latterly received actual pump performance data.

[0013] According to some of the example embodiments, the pump performance prediction model data may be generated based on one or more of: a theoretically-derived standard model of the performance of a pump of the type being monitored; a standard model of the performance of a pump of the type being monitored derived empirically through testing or theoretically or semi-empirically through testing and pump theory; a factory acceptance test ofthe actual subsea pump being monitored.

[0014] According to some of the example embodiments, the controller may be further configured to continually update the predicted pump performance data by retrieving data from the stored pump performance prediction model at intervals as more recent instants of operating condition data stream data are received.

[0015] According to some of the example embodiments, the controller being configured to continually receive the operating condition data stream and actual pump performance data stream and contemporaneously retrieve predicted pump performance data may cause the continually provided comparison of predicted pump performance data with actual pump performance data to be effectively updated in real time as the data streams are received and as the pump is pumping product from the formation.

[0016] According to some of the example embodiments, the apparatus may further comprise the controller being configured to provide the comparison of the synchronised predicted pump performance data with the actual pump performance data as graph plots for rendering on a display.

[0017] According to some of the example embodiments, the normal update interval of input sensing data in the received operating condition data stream and actual pump performance data stream data may be at a sample rate of at least 100 Hz, optionally at least 1000 Hz, optionally at least 5000 Hz, optionally at least 10 kHz, optionally 12 kHz.

[0018] According to some of the example embodiments, the normal update interval for the predicted pump performance data and the actual pump performance data may be at most 1 minute, optionally at most 30 seconds, optionally at most 10 seconds, at most 5 seconds, at most 1 second, and optionally wherein the predicted pump performance data and the actual pump performance data is generated periodically based on a windowed average of the input sensing data in the received operating condition data stream and actual pump performance data stream data.

[0019] According to some of the example embodiments, the memory may store the pump performance prediction model data generated from a factory acceptance test of the subsea pump as a Lookup Table (LUT), usable to retrieve data representative of the pump performance based on data representative of an operating condition of the pump.

[0020] According to some of the example embodiments, the pump may have a motor driving a shaft supporting an impeller for creating a pressure differential across the pump.

[0021] According to some of the example embodiments, the pump performance prediction model may store retrievable predicted pump performance data for given values in tested ranges of: gas volume fractions and density ratios of a pumped product; and shaft speeds driven by the motor.

[0022] According to some of the example embodiments, the predicted pump performance data retrieved from the pump performance prediction model may be indicative of one or more of: the predicted variation of differential pressure created by the subsea pump under test; the predicted variation of the shaft power of the subsea pump under test; the predicted variation of the efficiency of the subsea pump under test; the above predicted variations being at different volumetric pump flow rates for the sensed operating condition of the pump.

[0023] According to some of the example embodiments, the received operating condition data stream may comprise data representative of a sensed gas volume fraction and a density ratio of the pumped product, and a shaft speed driven by the motor.

[0024] According to some of the example embodiments, the received actual pump performance data stream may comprise data representative of volumetric pump flow rate, the actual operational shaft power, the actual operational efficiency of the pump, and the actual operational differential pressure created by the pump.

[0025] According to some of the example embodiments, the controller may be further configured to calculate the actual efficiency of the pump and actual differential pressure of the pump using at least one of: temperature of the product; rate of flow of the product; suction pressure of the product; and discharge pressure of the product; the above being as sensed by the subsea sensing system or the sensor system of subsea pump and being received by the controller in a data stream.

[0026] According to some of the example embodiments, the actual shaft power may be calculated using at least one of: motor current; motor votage; motor frequency; and motor cooling temperature; the above being as sensed by the sensor system of subsea pump and being received by the controller in a data stream.

[0027] According to some of the example embodiments, the controller may be further configured to: contemporaneously retrieve predicted pump performance envelope data from the stored pump performance prediction model based on a latterly received instant of the operating condition data stream data; wherein the predicted pump performance envelope data may comprise data representative of the predicted variation of differential pressure created by the subsea pump under test at different volumetric pump flow rates for the sensed operating condition of the pump and for at least one of: a maximum pump shaft speed; a minimum pump shaft speed; a maximum volumetric pump flow rate; a minimum volumetric pump flow rate.

[0028] According to some of the example embodiments, the controller may be further configured to provide a comparison of the synchronised predicted pump performance envelope data with the actual pump performance data; said controller optionally being further configured to provide the synchronised predicted pump performance envelope data with the actual pump performance data as graph plots for rendering on a display.

[0029] According to some of the example embodiments, the controller may be further configured to determine, from the comparison of the actual pump performance data and the predicted pump performance data, a most efficient operating condition for the pump.

[0030] According to some of the example embodiments, the controller may be further configured to determine the most efficient operating condition for the pump as being the volumetric flow rate at which the highest efficiency is achieved for the current running speed.

[0031] According to some of the example embodiments, the controller may be further configured to provide to a user interface of the apparatus the determined most efficient operating condition for the pump, and/or wherein the controller is further configured to generate, based on the determined most efficient operating condition for the pump, control data usable to adjust the operational parameters of the pump.

[0032] According to some of the example embodiments, the apparatus may further comprise the pump.

[0033] According to some of the example embodiments, the controller may be provided by one or more processors operating under program control to together or individually configure the controller as claimed, wherein one or more or all of the processors are located subsea or one or all of the configured functions of the controller are implemented by one or more processors located subsea, with the remainder or some or all of the processors or functions of the controller being implemented at one or more locations: topside; onshore; in the cloud.

[0034] According to some of the example embodiments, the one or both of the operating condition data stream and the actual pump performance data stream may be received from one or more subsea electronics modules (SEMs).

[0035] According to some of the example embodiments, the controller may be further configured to compare the received operating condition data or the received actual pump performance data or both with one or more pump operational state alert criteria, and to generate pump state event record data when one or more of the pump operational state alert criteria are satisfied.

[0036] According to some of the example embodiments, the controller may be further configured to store or update a pump state event record data in memory to provide a record of the pump’s state experience during operation against alert criteria.

[0037] According to some of the example embodiments, the pump operational state alert criteria may include one or more of: the number of successful starts; the number of failed starts; the number of stops; the number of emergency stops; the operation of the pump within a critical or resonant frequency region; the operation of the pump outside operational limits for the pump; the operation of the motor outside operational limits for the motor; and the operation of the pump in a high vibration state.

[0038] According to some of the example embodiments, the received operating condition data stream may comprise data representative of a shaft speed driven by the motor, and wherein the controller is further configured to count, based on the shaft speed data, the number of successful starts, failed starts, stops and emergency stops.

[0039] According to some of the example embodiments, the received operating condition data stream may comprise data representative of a shaft speed driven by the motor, and wherein the controller is further configured to monitor, based on the shaft speed data, the time during or frequency with which the shaft speed corresponds to a resonant frequency of the pump.

[0040] According to some of the example embodiments, the controller may be further configured to retrieve predicted pump performance envelope data from the stored pump performance prediction model based on received instants of the operating condition data stream data, the predicted pump performance envelope data defining the operational limits for the instant performance of the pump; wherein the controller is further configured to monitor the time during or frequency with which the actual pump performance data is outside the synchronised predicted pump performance envelope data defining the operational limits for the instant performance of the pump.

[0041] According to some of the example embodiments, the predicted pump performance envelope data may comprise data representative of the predicted variation of differential pressure created by the subsea pump under test at different volumetric pump flow rates for the sensed operating condition of the pump and for at least one of: a maximum pump shaft speed; a minimum pump shaft speed; a maximum volumetric pump flow rate; a minimum volumetric pump flow rate.

[0042] According to some of the example embodiments, the controller may be further configured to receive, in a data stream from a sensor system of the subsea pump, motor operation data including at least one of: a motor current; a motor voltage; a motor frequency; and a motor cooling temperature; wherein the controller may be configured to monitor based on the motor operation data the time during or frequency with which the motor operates outside operational limits defined for the motor.

[0043] According to some of the example embodiments, the controller may be further configured to receive, in a data stream from a sensor system of the subsea pump, pump vibration data including at least one of: pump shaft proximity at a drive end of the pump and/or the motor; pump shaft proximity at a non-drive end of the pump and/or the motor; pump shaft vibration at a drive end of the pump and/or the motor; pump shaft vibration at a non-drive end of the pump and/or the motor; wherein the controller may be configured to monitor based on the pump vibration data the time during or frequency with which the pump vibrates in a high vibration state defined for the pump.

[0044] According to some of the example embodiments, the disclosure may provide apparatus for generating an empirically or semi-empirically derived pump performance prediction model comprising stored machine readable data generated based on a factory acceptance test of a subsea pump to be monitored in operation or testing of a standard subsea pump of a type to be monitored, said data being representative of the pump performance and the operating conditions of the pump; the apparatus may comprise: a separator to, in use, separate the components of a product to adjust the gas volume fraction and/or density ratio of the product; a motor driving a shaft supporting an impeller for creating a pressure differential across the pump to pump the product around a test flow line and through the separator; a sensing system coupled to the separator to, in use; measure the gas volume fraction of the product; measure the density ratio of the product; a controller operatively coupled to each of the separator, motor and sensing system and configured to, in use: vary one or more of: the speed of the shaft as driven by the motor; the gas volume fraction of the product; or the density ratio of the product; the controller being further configured to: receive from the sensor system the data representative of a measured gas volume fraction and density ratio of the product; receive data representative of a measure of the speed of the motor shaft; receive data representative of a measure of the volumetric pump flow rate, the actual operational shaft power, the actual operational efficiency of the pump, and the actual operational differential pressure created by the pump; and to generate a pump performance prediction model data based on the received data.

[0045] According to some of the example embodiments, the controller may be further configured to store the generated pump performance prediction model data in a lookup table (LUT).

[0046] According to some of the example embodiments, the controller may be further configured to generate the pump performance prediction model data based also on a theoretically-derived standard model of the performance of a pump of the type being monitored.

[0047] According to some of the example embodiments, the disclosure may provide apparatus for monitoring the operational performance of a subsea pump for pumping product from a formation against pump operational state alert criteria, the apparatus comprising a controller configured to, in operational use: continually receive an operating condition data stream comprising data representative of an operating condition of the pump derived from a subsea sensing system; continually receive an actual pump performance data stream comprising data representative of the actual operational performance of the pump derived from the sensor system of subsea pump; compare the received operating condition data or the received actual pump performance data or both with one or more pump operational state alert criteria, and to generate pump state event record data when one or more of the pump operational state alert criteria are satisfied.

[0048] According to some of the example embodiments, the controller may be configured to continually receive the operating condition data stream and actual pump performance data stream in real time as the data streams are received and as the pump is pumping product from the formation.

[0049] According to some of the example embodiments, the normal update interval of input sensing data in the received operating condition data stream and actual pump performance data stream data may be at a sample rate of at least 100 Hz, optionally at least 1000 Hz, optionally at least 5000 Hz, optionally at least 10 kHz, optionally 12 kHz.

[0050] According to some of the example embodiments, the controller may be further configured to store or update a pump state event record data in memory to provide a record of the pump’s state experience during operation against alert criteria.

[0051] According to some of the example embodiments, the pump operational state alert criteria may include one or more of: the number of successful starts; the number of failed starts; the number of stops; the number of emergency stops; the operation of the pump within a critical or resonant frequency region; the operation of the pump outside operational limits for the pump; the operation of the motor outside operational limits for the motor; and the operation of the pump in a high vibration state.

[0052] According to some of the example embodiments, the received operating condition data stream may comprises data representative of a shaft speed driven by the motor, and wherein the controller is further configured to count, based on the shaft speed data, the number of successful starts, failed starts, stops and emergency stops.

[0053] According to some of the example embodiments, the received operating condition data stream may comprise data representative of a shaft speed driven by the motor, and wherein the controller is further configured to monitor, based on the shaft speed data, the time during or frequency with which the shaft speed corresponds to a resonant frequency of the pump.

[0054] According to some of the example embodiments, the apparatus may further comprise machine readable storage comprising a pump performance prediction model storing data representative of the pump performance and the operating conditions of the pump; wherein the controller is further configured to retrieve predicted pump performance envelope data from a pump performance prediction model based on received instants of the operating condition data stream data, the predicted pump performance envelope data defining the operational limits for the instant performance of the pump; wherein the controller is further configured to monitor the time during or frequency with which the actual pump performance data is outside the synchronised predicted pump performance envelope data defining the operational limits for the instant performance of the pump.

[0055] According to some of the example embodiments, the predicted pump performance envelope data may comprise data representative of the predicted variation of differential pressure created by the subsea pump under test at different volumetric pump flow rates for the sensed operating condition of the pump and for at least one of: a maximum pump shaft speed; a minimum pump shaft speed; a maximum volumetric pump flow rate; a minimum volumetric pump flow rate.

[0056] According to some of the example embodiments, the controller may be further configured to receive, in a data stream from a sensor system of the subsea pump, motor operation data including at least one of: a motor current; a motor voltage; a motor frequency; and a motor cooling temperature; wherein the controller is configured to monitor based on the motor operation data the time during or frequency with which the motor operates outside operational limits defined for the motor.

[0057] According to some of the example embodiments, the controller may be further configured to receive, in a data stream from a sensor system of the subsea pump, pump vibration data including at least one of: pump shaft proximity at a drive end of the motor; pump shaft proximity at a non-drive end of the motor; pump shaft vibration at a drive end of the pump; pump shaft vibration at a non-drive end of the pump; wherein the controller is configured to monitor based on the pump vibration data the time during or frequency with which the pump vibrates in a high vibration state defined for the pump.

[0058] According to some of the example embodiments, the disclosure may provide a method of monitoring the operational performance of a subsea pump for pumping product from a formation, comprising: continually receiving, at a controller, an operating condition data stream comprising data representative of an operating condition of the pump derived from a subsea sensing system; contemporaneously retrieving, by the controller and based on a latterly received instant of the operating condition data stream data, predicted pump performance data from an pump performance prediction model storing data representative of the pump performance and the operating conditions of the pump; continually receiving, at the controller, an actual pump performance data stream comprising data representative of the actual operational performance of the pump derived from the sensor system of subsea pump; and continually providing, by the controller, a comparison of synchronised latterly retrieved predicted pump performance data with a latterly received actual pump performance data.

[0059] According to some of the example embodiments, the disclosure may provide a method of generating an empirically or semi-empirically derived pump performance prediction model comprising stored machine readable data generated based on a factory acceptance test of a subsea pump to be monitored in operation or testing of a standard subsea pump of a type to be monitored, said data being representative of the pump performance and the operating conditions of the pump; the method comprising: separating, using a separator, the components of a product to adjust the gas volume fraction and/or density ratio of the product; driving a shaft of a motor, the shaft supporting an impeller for creating a pressure differential across the pump to pump the product around a test flow line and through the separator; measuring the gas volume fraction of the product; measuring the density ratio of the product varying one or more of: the speed of the shaft as driven by the motor; the gas volume fraction of the product; or the density ratio of the product; receiving, at a controller, data representative of a measured gas volume fraction and density ratio of the product; receiving, at the controller, data representative of a measure of the speed of the motor shaft; receiving, at the controller, data representative of a measure of the volumetric pump flow rate, the actual operational shaft power, the actual operational efficiency of the pump, and the actual operational differential pressure created by the pump; and generating, by the controller, pump performance prediction model data based on the received data.

[0060] According to some of the example embodiments, the disclosure may provide a method of monitoring the operational performance of a subsea pump for pumping product from a formation against pump operational state alert criteria, the method comprising: continually receiving, at a controller, an operating condition data stream comprising data representative of an operating condition of the pump derived from a subsea sensing system; continually receiving, at the controller, an actual pump performance data stream comprising data representative of the actual operational performance of the pump derived from the sensor system of subsea pump; comparing, by the controller, the received operating condition data or the received actual pump performance data or both with one or more pump operational state alert criteria, and to generate pump state event record data when one or more of the pump operational state alert criteria are satisfied.

[0061] According to some of the example embodiments, the disclosure may provide a computer readable medium comprising instructions which when executed by one or more processors, cause the processor or processors together to provide a controller for monitoring the operational performance of a subsea pump for pumping product from a formation, the controller being configured to: continually receive an operating condition data stream comprising data representative of an operating condition of the pump derived from a subsea sensing system; contemporaneously retrieve predicted pump performance data from a stored pump performance prediction model based on a latterly received instant of the operating condition data stream data, the pump performance prediction model storing on machine readable storage data representative of the pump performance and the operating conditions of the pump; continually receive an actual pump performance data stream comprising data representative of the actual operational performance of the pump derived from the sensor system of subsea pump; and continually provide a comparison of synchronised latterly retrieved predicted pump performance data with a latterly received actual pump performance data.

[0062] According to some of the example embodiments, the disclosure may provide a computer readable medium comprising instructions which when executed by one or more processors configure the processor or processors together to provide a controller for generating an empirically or semi-empirically derived pump performance prediction model comprising stored machine readable data generated based on a factory acceptance test of a subsea pump to be monitored in operation or testing of a subsea pump of a type to be monitored, said data being representative of the pump performance and the operating conditions of the pump; the controller being configured to: control a separator to, in use, separate the components of a product to adjust the gas volume fraction and/or density ratio of the product; control a motor driving a shaft supporting an impeller for creating a pressure differential across the pump to pump the product around a test flow line and through the separator; control a sensing system coupled to the separator to, in use; measure the gas volume fraction of the product; measure the density ratio of the product; vary one or more of: the speed of the shaft as driven by the motor; the gas volume fraction of the product; or the density ratio of the product; the controller being further configured to: receive from the sensor system the data representative of a measured gas volume fraction and density ratio of the product; receive data representative of a measure of the speed of the motor shaft; receive data representative of a measure of the volumetric pump flow rate, the actual operational shaft power, the actual operational efficiency of the pump, and the actual operational differential pressure created by the pump; and to generate a pump performance prediction model data based on the received data.

[0063] According to some of the example embodiments, the disclosure may provide a computer readable medium comprising instructions which when executed by one or more processors configure the processor or processors together to provide a controller for monitoring the operational performance of a subsea pump for pumping product from a formation against pump operational state alert criteria, the controller being configured to: continually receive an operating condition data stream comprising data representative of an operating condition of the pump derived from a subsea sensing system; continually receive an actual pump performance data stream comprising data representative of the actual operational performance of the pump derived from the sensor system of subsea pump; compare the received operating condition data or the received actual pump performance data or both with one or more pump operational state alert criteria, and to generate pump state event record data when one or more of the pump operational state alert criteria are satisfied.

[0064] Within the scope of this application it is expressly envisaged that the various aspects, embodiments, examples and alternatives set out in the preceding paragraphs, in the claims and/or in the following description and drawings, and in particular the individual features thereof, may be taken independently or in any combination. Features described in connection with one aspect or embodiment or example of the disclosure are applicable to all aspects or embodiments or examples, unless such features are incompatible.

BRIEF DESCRIPTION OF THE DRAWINGS [0065] Examples of the disclosure are further described hereinafter with reference to the accompanying drawings, in which:

Figure 1 is a schematic illustration of a subsea production system in accordance with some examples of the present disclosure;

Figure 2 is a schematic illustration of a subsea production control system and components which may support a subsea production control system in accordance with some examples of the present disclosure;

Figure 3 is an illustration of an abstracted view of a software architecture of a subsea production control system and data flows within it in accordance with some examples of the present disclosure;

Figure 4 is an illustration of some components of a Field Management Server (FMS) and subsea production control system in accordance with some examples of the present disclosure;

Figures 5A and 5B show an illustration of certain components of a condition and performance monitoring apparatus for a pump apparatus in accordance with some examples of the present disclosure, with Figure 5B being a representative cross sectional view through the pump shaft showing the positioning of the proximity sensors for measuring the orbit of the pump shaft;

Figure 6 is an illustration of data flows from a condition and performance monitoring apparatus for a pump apparatus to a pump application and of the dataprocessing therein to produce a real-time pump plot in accordance with some examples of the present disclosure;

Figure 7 is a process flow diagram illustrating a method for monitoring the operational performance of a subsea pump for pumping product from a formation in accordance with some examples of the present disclosure;

Figures 8A and 8B show an illustration of a real-time pump plot provided by a pump application in accordance with some examples of the present disclosure;

Figure 9 is a process flow diagram illustrating a method for monitoring the state of the operational performance of a subsea pump for pumping product from a formation against pump operational state alert criteria in accordance with some examples of the present disclosure;

Figure 10 is an illustration of a simplified piping and instrumentation diagram of a testing apparatus for generating an empirically or semi-empirically derived pump performance prediction model in accordance with some examples of the present disclosure; and

Figure 11 is a process flow diagram illustrating a method for use in generating an empirically or semi-empirically derived pump performance prediction model in accordance with some examples of the present disclosure.

DETAILED DESCRIPTION [0066] Throughout the description and claims of this specification, the words “comprise” and “contain” and variations of them mean “including but not limited to”, and they are not intended to (and do not) exclude other moieties, additives, components, integers or steps. Throughout the description and claims of this specification, the singular encompasses the plural unless the context otherwise requires. In particular, where the indefinite article is used, the specification is to be understood as contemplating plurality as well as singularity, unless the context requires otherwise.

[0067] Features, integers, characteristics, compounds, chemical moieties or groups described in conjunction with a particular aspect, embodiment or example of the invention are to be understood to be applicable to any other aspect, embodiment or example described herein unless incompatible therewith. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and/or all of the steps of any method or process so disclosed, may be combined in any combination, except combinations where at least some of such features and/or steps are mutually exclusive. The invention is not restricted to the details of any foregoing embodiments. The invention extends to any novel one, or any novel combination, of the features disclosed in this specification (including any accompanying claims, abstract and drawings), or to any novel one, or any novel combination, of the steps of any method or process so disclosed.

[0068] Referring now to Figure 1, to extract oil and gas product from a subsea reservoir R found in a geological formation located under the sea bed, a wellbore W has been drilled to the formation. The wellbore W may be a cased wellbore or open hole and may include a production tubing for conveying the oil and gas product up the wellbore W.

[0069] A subsea production system 100 extracting the oil and gas product from the wellbore W is provided. A subsea production system 100 may extract product from plural wellbores, although only a single well is shown in Figure 1. The subsea production system 100 can be installed to interface between the apparatus downhole in the well W and topside and effect the recovery of product to onshore locations through a flowline F. Some or all of the components of the subsea production system 100 may be provided at subsea locations on the seabed or floating subsea, at topside locations at the sea surface in the field, or at onshore locations away from the field.

[0070] In terms of equipment, the subsea production system 100 may include wellhead apparatus 110 located at the head of the well. The wellhead apparatus 110 and may include subsea structures and manifolds which may include a Christmas tree to seal the well and provide valves, spools, connectors and a manifold system. The well may be tiedin through the Christmas tree to a subsea flowline system F through which the product may be retrieved to an onshore location 150. The subsea structures and production system 100 may also provide a subsea access or intervention system to allow access to the well for monitoring and control of the downhole apparatus such as chokes and valves, cabling, sensors and strain gauges, and other tools, and also for interventions and workovers. The subsea production system 100 may at the wellhead apparatus 110 also include one or more subsea pumps to control the production or to transport the hydrocarbons from the well W to the sea surface or to land, for example through flowline F.

[0071] The subsea production system 100 may also include subsea apparatus 120 providing production system equipment located on the seabed or floating subsea. The subsea apparatus 120 may be coupled to one or more wellhead apparatuses 110 at one or more subsea wells. The subsea apparatus 120 may also include one or more pumps for pumping product from one or more wellbores or for pumping product along one or more flow lines. The subsea apparatus 120 may also include one or more Subsea Control Modules (SCM) to facilitate the management and monitoring of the subsea production system 100. The SCM may include plural Subsea Electronics Modules (SEM) providing a data communications interface with components of the subsea production system 100, including various Condition and Performance Monitoring apparatus (CPM). The SCMs, SEMs and CPMs will be described further below.

[0072] The subsea production system 100 may also include an umbilical apparatus 130 configured to couple the subsea apparatus 120 to a topside apparatus 140 of the subsea production system 100. The umbilical apparatus 130 may carry hydraulic and other fluid flow lines, and electrical and other signal-carrying cabling to transmit control and monitoring signals between the components of the subsea production system 100 located topside and subsea.

[0073] The topside apparatus 140 may be located at one or more topside platforms provided on rigs or semi-submersibles to perform one or more production functions such as allowing an operator of the subsea production system 100 in the field to manage the operation of the apparatus of the subsea production system 100. The topside apparatus 140 may include a Master Control Station (MCS) provided as a centralised hardware and software platform for enabling the monitoring, control and logging of the subsea production system and the condition and performance monitoring of the components. The MCS may also provide an operator with one or more user interfaces at a workstation to monitor and control the subsea production system. The MCS may be coupled to plural SCMs located subsea via umbilical 130.

[0074] The subsea production system 100 may also include onshore apparatus 150 by which the operator of the production system may monitor and control certain components and functions of the subsea production system 100 from onshore, live and in real time, or offline. For example, the logical components and software and data storage architecture making up the subsea production control system (described in more detail below) maintained in the MCS or in particular the FMS thereof) may be duplicated or synchronised to one or more onshore locations. Further, while not shown, plural subsea production systems 100 may be coupled, for example via onshore apparatuses 150, to a centralised production monitoring system, which may, for example, be used to aggregate information about the performance and operation of the plural subsea production systems 100 across different fields. Analysis of the aggregated information be shared among operators of subsea production systems to facilitate their management and optimisation, and may be used to facilitate and improve the designing and installation of production systems to improve yields and efficiency.

[0075] Referring to Figure 2, at the equipment level, across one or more of the locations the subsea production apparatus 100 shown in Figure 1, the subsea production apparatus 100 may include a subsea production control system 200 in accordance with certain examples of the present disclosure.

[0076] The subsea production control system 200 may include various components configured to operate together to enable the monitoring of the condition and the performance of and the control of various components and subsystems of the subsea production system 100, such as the pumps. The various components supporting the subsea production control system 200 may be provided at or across one or more of the locations of the subsea production apparatus 100 as described in relation to Figure 1. It is to be understood that the example arrangement of the components of the subsea production control system 200 described in the following passages is not intended to be limiting. Indeed, variations in the locations ofthe various components and data processing are envisaged to fall within the scope of the present disclosure.

[0077] The subsea production control system 200 may include one or more subsea control modules SCMs 210a,b...n. These are typically provided in one or more centralised locations as subsea apparatus 120 and may be configured as shown to contain plural Subsea Electronics Modules SEMs 212a,b...n sealed inside a cylindrical canister in a controlled environment.

[0078] The SEMs 212a,b...n provide a data communications interface with components of the subsea production system 100, including various Condition and Performance Monitoring apparatuses CPMs 240a,b...n. The different CPMs 240a,b...n comprise different sets of sensors or other instruments or hardware arranged to monitor the condition and performance of different components of the subsea production apparatus 100. Each of the CPMs 240a,b...n provides one or more of the SEMs 212a,b...n with sensor data which the SEMs 212a,b...n may pass on or partially or fully process and relay to a Master Control Station (MCS) 220.

[0079] In the example, the MCS 220 may be located topside and is coupled to the SCMs 210a,b...n and SEMs 212a,b...n to send and receive monitoring and control data there with via an umbilical (as shown in Figure 1). However, it is specifically envisage that one or more or all of the components of the MCS 220 may be provided at locations exclusively subsea, or the components of the MCS 220 may be provided across various locations, for example subsea and topside.

[0080] In examples of the present disclosure, the MCS 220 may include a Field Management Server (FMS) 222, which may be provided as a server in a server rack of the MCS 220. The FMS 222 may be provided to receive and process condition and performance monitoring data, received as streaming sensor data, from the CPMs 240a,b...n via the SEMs 212a,b...n. As will be explained in further detail below, the FMS 222 may, in accordance with examples of the present disclosure, implement in software a Pump App that provides a pump monitoring system that may monitor the operating condition of the pump based on the pump CPM data at a high sampling rate and may provide an operator with a continually updated report of the actual performance of the pump online at the same time the pump is in operation, effectively in real time in relation to the instant performance of the pump. The reporting of the actual performance of the pump may be at a user interface of a workstation 230 located topside or even onshore, for example in the form of a live pump performance plot. In the example embodiment, the FMS 222 is co-located or located in the MCS 220 but in other examples, one or more or all of the hardware and/or software components of the FMS 222 could be provided at locations subsea, topside and/or onshore.

[0081] By monitoring the condition and performance of these subsea production apparatus 100 using the subsea production control system 200, the subsea production control system 200 may automatically, semi-automatically or by manual operation by an operator at workstation 230 generate one or more control signals which may be transmitted to one or more components of the subsea production apparatus 100, for example via the umbilical, to control their operation and configuration.

[0082] For example, the MCS 220 may generate control signals for the operation of various valves and instruments 250a,b...n of one or more components of the subsea production apparatus 100 which may be transmitted to those components via one or more of the SEMs 212a,b...n. Similarly the MCS 220 may generate control signals for the operation of a control system 260 or a variable speed drive VSD 270, for example of a motor driving a pump for pumping product. In accordance with some examples of the present disclosure, the operation of the pump application at the FMS 222 may identify, effectively in real time, a pump shaft speed that would produce an optimal operational efficiency for pumping the product and this may enable the MCS 222 to automatically, semi-automatically or manually, by user operation at a workstation 230, to control a shaft speed of a pump (for example using VSD 270) to efficiently pump the product from the well. Similarly, the operation of the pump application at the FMS 222 may identify, effectively in real time, when the pump is being operated in a state which risks excessive wear on the pump components and this may enable the MCS 222 to automatically, semiautomatically or manually, by user operation at a workstation 230, to control a shaft speed of a pump (for example using VSD 270) or other operational control mechanisms to pump the product from the well in a way such that excessive wear of the pump is avoided.

[0083] Turning now to Figure 3, in an abstracted view of example subsea production control systems 200 of the present disclosure, a software architecture of a subsea production control system 200 is provided that may enable improved operational efficiency and effectiveness by providing a platform which is flexible and scalable enough to meet the requirements from a wide range of applications, such as CPMs 240a,b...n. According to some of the example embodiments, such a scalable architecture may be provided with a Service Orientated Architecture to allow for decoupling between components and ease the process of building and changing system components incrementally. Patterns for asynchronous and event driven information exchange are used to make sure that the platform scales and may handle increased processing capacity requirements.

[0084] Referring also Figure 4, this shows components which may be included in a Field Management Server (FMS) 222 and subsea production control system 100 in accordance with some examples of the present disclosure. The FMS 222 may include a communications module 410 for transmitting and receiving data from various components of the subsea production control system 100, including receiving data from the CPMs 240a,b...n received via SEMs 212a,b...n, and transmitting and receiving data with workstation 230. The workstation 230 may be provided by a general purpose computer accessing software, such as a Pump App, or data, served by FMS 222.

[0085] The FMS 222 further includes a bus 405 logically coupling the communications module 410 with one or more processors 420a,b... n and data storage 430 to transfer data and signals therebetween. The storage 430 may include volatile memory such as random access memory (RAM) 432 which the processors 420a,b...n may read from and write to to instantiate one or more applications in a runtime session and implement instructions codifying these applications read from persistent storage 434 such as a solid-state drive. The processors 420a,b...n may also read data from persistent storage to facilitate the operation of various applications. For example, the processors 420a,b...n may retrieve data from a pump performance prediction model 436 stored as a lookup table (LUT) to enable the pump’s application 432 to provide a real-time plot of actual versus predicted pump performance.

[0086] The implementation of application software in the FMS 222 may provide processing circuity that implements a data or logic controller. Processing circuitry or circuitry such as the circuitry implemented in the controller provided by the Pumps App 432 or SCM app 434 of examples described herein may be, as described above, general purpose processor circuitry configured by program code to perform specified processing functions. In other examples, the processing circuitry may be special purpose processing circuitry for implementing the corresponding function by modification to the processing hardware. Configuration of the circuitry to perform a specified function may be entirely in hardware, entirely in software or using a combination of hardware modification and software execution. Machine-readable instructions may be used to configure logic gates of general purpose or special-purpose processing circuitry to perform a processing function. Program instructions may be provided on a non-transitory medium such as storage 430 or via a transitory medium. The transitory medium may be a transmission medium.

[0087] Processing hardware may comprise, for example, one or more processors such as processors 420a,b...n, or in other examples, very large scale integration (VLSI) circuits or field programmable gate arrays, off-the-shelf semiconductors such as logic chips, transistors, or other discrete components. The storage medium 430 readable by the processor, may include volatile memory such as RAM 432 and non-volatile memory such as persistent storage 434 and/or storage elements. The volatile and non-volatile memory and/or storage elements may be a random access memory (RAM), erasable programmable read-only memory (EPROM), flash drive, optical drive, magnetic hard drive, or other medium for storing electronic data.

[0088] Program code or machine-readable program instructions for implementing the CPM apps or other layers in the hierarchy of the software architecture as described herein in relation to Figure 3 may be implemented in a high level procedural or object-oriented programming language. However, the code may be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language, and combined with hardware implementation.

[0089] Referring again to Figure 3 in particular, data from the different CPMs 240a,b...n is received via SEMs 212a,b...n at a hardware and software platform 320 of the FMS 222. Typically, the CPM data is received as a live data stream at a high sampling rate. For example, for the pump CPM sensors, proximity and vibration sensor data is received in a data stream with a sampling rate of 12 kHz. An appropriate protocol for the data stream, such as Real-time Transport Protocol (RTP), is used.

[0090] From there, be received CPM data is passed up to the next player in the software stack in which the data is processed by fundamental methods and data validation software 330, which may be based on inventory information regarding the CPM sensors and other data models in order to validate data and prepare it to conform to a universal data format. From there, the processed CPM data is immediately published onto a data bus. The data bus may function as a messaging (publish/subscribe) system that is capable of handling large amount of data from different producers (publishers). Each producer may typically publish data from a single data source, but each source might receive data from many data points. Consumers may receive the data by subscribing to them. This will allow real time data to flow from a producer to one or more consumers.

[0091] At the next level up in the FMS software architecture, one or more modularised software components may be provided as CPM “Apps” 340. As described in relation to Figure 4, these may be instantiated in a RAM 432 of the FMS 222 by one or more of the processors of the FMS 222 carrying out instructions that codify the CPM Apps 340 stored in persistent storage 434. Each CPM App 340 effectively causes the FMS to provide a data or logic controller by implementing and operating appropriately configured processing circuitry in the FMS hardware.

[0092] As can be seen in Figure 4, the FMS 222 can implement a Pumps App 342 for monitoring the condition and performance of one or more subsea pumps, and a Subsea Control Module (SMC) App 344 for monitoring the condition and performance of one or more SMCs 210a,b,...n. Further CPM Apps 346, 348... can be developed and implemented by FMS 222.

[0093] In accordance with the software architecture, CPM software apps can be developed and implemented in a modularised fashion that subscribe to and consume selected data published to the data bus by the FMS 222. If the subsea production control apparatus 200 is configured such that the CPM data is published to the data bus in a universal data format independently of the different specific configuration and operation of the CPM apparatuses 240a,b...n, then the CPM Apps 340 can be developed to operate in a decoupled fashion, independently of the different CPM apparatus used, and the data processing and the monitoring and control provided by the CPM Apps 340 can be more standardised and comparable between equipment, wells and fields.

[0094] Referring again to Figure 4, the persistent storage 434 may also be used as a permanent store of data received from CPMs 240a,b...n published data bus, which may provide a historical log of the operation and performance of the various components of the subsea production apparatus 100. Some or all of this data may also be conveyed to onshore locations 150, for example by intermittent or continuous transmission by a wired or wireless connection, or by physical transfer by one or more physical persistent data storage devices. The collection of live or historical CPM data in a universal data format from one or more fields at a centralised location, for example onshore, permits significant insights into the operational performance and condition of various subsea production apparatuses, for example by using big data analytics.

[0095] The operation of the Subsea Production Control Apparatus 200 to publish data to data bus in a universal format may perform the reformatting in the software stack of the FMS as shown in Figure 3 based an inventory of the different CPM apparatus 240a,b...n installed in the subsea production system 100. The inventory of the CPM apparatus 240a,b...n may be populated supported by the SEMs 212a.b...n. The SEMs 212a.b...n are generally provide as physical cards that support electronic assemblies (such as printed circuit boards, PCBs) arranged in slots connected by a backplane all contained within a robust housing of the Subsea Control Modules 210a,b... n that can withstand the extreme high pressure environment subsea.

[0096] The different types of equipment, instruments and sensors provided downhole that are required to be electronically interfaced with for control and sensor data processing are often supplied from a range of different manufacturers and there has been little standardisation of the electronic operation and interfacing protocols. Thus there would be required a different card type to perform each specific role for each specific equipment type from each different manufacturer. This can lead to a proliferation of hardware requirements that would significantly complicate the SEM design and assembly process to fulfil a desired functional specification for the SEM [0097] As a result, the SEMs 212a.b...n normally consist of specific electronic assemblies (cards) dedicated to control a certain type of instrument, actuator or other equipment. Often, the cards must be themselves provided by the manufacturer of the equipment. As a result, for each type of equipment, a specific type of card may be necessary for the SEMs 212a.b...n to be able to communicate with the equipment, leading to a high number of card variants. The functionality of the card is fixed at the time of subsea deployment and the card type is programmed in the centrally stored configuration database of the SEMs 212a.b...n to allow it to function.

[0098] This approach can achieve the processing of the CPM data into a universal data format in FMS 222.

[0099] To facilitate interfacing with the CPMs and processing of the CPM data at the

FMS 222 into a universal data formal, an electronic inventory of the different components making up the CPMs and the subsea apparatus connected through the SEMs 212a.b...n may be built up, for example, in the MCS 210 or the FMS 222 by cooperation with the

SEMs 212a.b...n. The SEMs 212a.b...n may be configured to be software configurable or self-configurable and to contribute to the maintenance of an electronic inventory of the CPM apparatus 240a,b...n.

[00100] The FMS 222 may store in its persistent memory 424, besides monitored data, information associated with the components of the CPM apparatuses 240a,b...n. An example of information associated with the components of the CPM apparatuses 240a,b...n is the inventory. The inventory may comprise operational information or a configuration of the components of the CPM apparatuses 240a,b...n.

[00101] The inventory may include, for the different components, an identification of the component manufacturer, type, software version, communication protocol, and other identification information. The knowledge of the component identification information through the inventory may allow the MCS 210 and the FMS 222 to communicate with the CPM apparatuses 240a,b...n via the SEMs 212a.b...n for control thereof and to process and understand the data received for the monitoring of the condition and performance of the subsea apparatus monitored by that CPM apparatus.

[00102] United Kingdom patent application publication no. GB2531032 discloses SEMs provided by software-configurable generic card types having an electronic assembly thereon for communication with different equipment from different manufacturers. This may be achieved by providing in the electronic assembly a hardware architecture which is capable of communicating with a range of different supported equipment from different manufacturers. Then, program instructions provided on a memory storage of the generic card type in the form of firmware is capable of configuring the card in used to perform a set of electronic functions for a selected one of a plurality of defined card roles, and the different defined card roles allow the electronic assembly to communicate successfully with the different equipment from the different manufacturers. In this way, a generic card type is provided that, by the use of firmware, can be selectively configured to emulate the operation of a plurality of different proprietary cards required in the art for communication with different equipment from different manufacturers.

[00103] In this way, an inventory of the different subsea apparatus and components making up the different CPM apparatuses 240a,b...n connected through the SEMs 212a.b...n can be built up. As indicated above, this may be stored at locations in the subsea production apparatus, for example, in the MCS 210 or the FMS 212. The inventory of the different subsea apparatus may be built up in part automatically by the SEMs selfconfiguring for example on connection to the CPM apparatuses 240a,b...n, for example by the SEMs recognizing the components making up the CPM apparatuses 240a,b...n through, for example, a handshake protocol. The inventory of the subsea apparatus may be built up in part manually, by the operator using the workstation to configure the or each SEM of an SCM with the component identification information. This may be appropriate for example in brownfield sites where legacy systems are installed that cannot be automatically recognized by the SEM, or where the subsea apparatus is not designed and configured on the SEMs on or before installation. The inventory of the different subsea apparatus may be built up in part semi-automatically. That is, the SEMs may attempt to automatically generate at least part of the identification information for the CPM apparatus for the inventory, which may be checked and validated manually by an operator.

[00104] To facilitate monitoring ofthe subsea apparatus to ensure the identity ofthe subsea components is known to the MCS 210 and FMS 212, the inventory may comprise a configuration fingerprint comprising information ofthe CPM apparatus relating for example its geographical location, an apparatus type, sensor values, or measurement meta data. The SEMs 212a.b...n or the FMS 210 or MCS 212 may be configured to detect a change in the inventory or monitoring data and record a corresponding timestamp when the detected change occurs. This change detection may occur by comparing the configuration fingerprint for a CPM apparatus with the configuration fingerprint for the CPM apparatus stored in the inventory. In this way, the subsea production system 100 can identify when changes occur to component apparatus, for example by way of software updates or installations, etc, and the inventory can be updated accordingly. Thus the CPM apparatuses 240a,b...n can be interfaced with effectively.

[00105] The SEMs 212a.b...n or the FMS 210 or MCS 212 may correlate changes in the inventory or monitoring data with an operational state of a plurality of CPM apparatuses and further to provide a graphical representation of the correlated changes with respect to the recorded timestamps.

[00106] In operation, the different SEMs 212a.b...n may initiate a transmission, for example, to the FMS 212, of an inventory specifying an operational configuration of a subsea component associated with a corresponding CPM apparatus 240a,b...n or an operational configuration ofthe CPM apparatus 240a,b...n itself. The FMS 212 may utilize information from the inventory to determine an expected data format for incoming data from the associated CPM apparatus 240a,b...n. Using the knowledge of the expected data format, the FMS 212 may reformat the incoming data into a universal data format. The FMS 222 may be coupled to a standard reference timer for the subsea production control system 200, and the received data may, on being reformatted into the universal data format, include a timestamp with reference to the standard reference timer. This may facilitate historical logging and synchronisation of data received from the CPM apparatuses 240a,b...n.

[00107] It should be appreciated that each CPM apparatus 240a,b...n is associated with a different CPM component and therefore obtaining data in a variety of formats. Thus, the reformatting of received data into the universal data format allows for analytics to be performed across different types of data. The FMS 212 may also transmit any reformatted data or data analytics to a centralized data bus, as indicated above. The FMS 212 may regulate the disclosure of data on the centralized bus based on a user subscription.

[00108] Turning now to the CPM apparatuses 240a,b...n in more detail, the CPM apparatuses may be located in distinct physical locations and associated with different monitored components of the subsea oil and gas production system.

[00109] For example, one or more CPM apparatuses may also be associated with and monitor the Subsea Control Modules (SCM) themselves, which may be subsea-located components of the subsea production control system 200. The SCM CPM apparatus may include sensers: for monitoring the electrical power supply provided by a Power Supply Module (PSM); for monitoring the connectivity status, uptime, and traffic and error statistics of data-carrying communications channels; for monitoring the environmental conditions including the pressure, temperature and humidity inside the canisters providing the housing of the subsea control modules (SCM) enclosing and supporting the SEMs; for monitoring the status and utility of the SEM cards themselves and software modules implemented thereby; and for monitoring the integrity and condition of hydraulic components in the subsea production system, such as valves, valve profiles and hydraulic distribution lines.

[00110] For example, one or more CPM apparatuses may be associated with and monitor the operations of a pump. The Pump CPM apparatus may include: vibration sensors for sensing a vibration state and a vibration frequency spectrum of the pump; proximity sensors for sensing the position of the drive shaft of the pump for plotting its rotational orbit; motor cartridge sensors may sense torque, slip, efficiency and cooling temperature; power system sensors for sensing motor current, voltage, and frequency, power usage, quality and earth insulation integrity; barrier fluid system sensor for sensing fluid consumption, accumulator status and pressure; and run time sensors for sensing an operational usage of the pump and run time statistics. A pump shaft rotational speed may be sensed directly by a CPM apparatus, or indirectly using for example a proximity sensor and an indicator disc mounted to the shaft. Shaft power and shaft efficiency may be sensed indirectly by determining them based on other sensed quantities.

[00111] Further, one or more CPM apparatuses may also be associated with and monitor the process system, for example at or in relation to the pump. The process system CPM apparatus may include: sensors for sensing a suction pressure and a discharge pressure of the product from the pump, for use in calculating a differential pressure of the pump; sensors for sensing a flow rate of the product, particularly through the pump, such as a volumetric flow rate; sensors for sensing a temperature of the product; sensors for sensing a density ratio of the product; and sensors for sensing a gas volume fraction (GVF) of the product.

[00112] The CPM Apps 340 of the FMS 222 may compile and store monitored data from the CPM apparatus and provide analytics in the form of, for example, hydraulic pressure and flow, valve profiles, electrical power efficiency, network communication, software revision and configuration, pressure, temperature and humidity monitoring and advanced electronics state monitoring. The Pump App 342, for example, may provide pump analytics including vibration and proximity (e.g., speed, orbit plot, phase spectrums and harmonics), pump cartridge (e.g., dynamic pump plot, pressure, output power, efficiency), motor cartridge (e.g., dynamic torque plot, slip, efficiency and cooling performance), barrier fluid system (e.g., consumption, working accumulators, pressure, pressure and volume regulator) and power (e.g., usage, quality and insulation).

[00113] The SEMs 212a,b...n receive live sensor data from the various CPM apparatuses 240a,b...n, processes it and sends it to the FMS 212 as a data stream. The update interval of input sensing data in the data streams may be at a sample rate of at least 100 Hz, optionally at least 1000 Hz, optionally at least 5000 Hz, optionally at least 10 kHz, optionally 12 kHz.

[00114] Referring to Figures 5A and 5B, a subsea pump 500 is illustrated showing its key components and some components of the pump CPM apparatus for providing sensor data to the Pumps App 432 in a live stream of sensor readings. The subsea pump 500 comprises a shaft driven by a motor 510 which may be a variable speed drive. The shaft 505 is connected to a pump 520, which may be a centrifugal pump including one or more impellers, which creates a differential pressure as a difference between a suction pressure and a discharge pressure for pumping product in a flow line. Pump CPM sensor components for monitoring the running of the pump may be provided at locations around the drive shaft bearings at a Motor Non-Drive End (MNDE) 532, Motor Drive End (MDE) 534, Pump Drive End (PNDE) 536 and Pump Non-Drive End (PNDE) 538. The sensors shown in Figure 5A can be used to identify pump shaft vibration, orbit to help identify wear, and rotational speed.

[00115] The core input data for the algorithms and resulting calculations and views are the high-speed signals from the accelerometers radial and axial and x and y proximity probes placed around the bearings of the pump shaft at the Motor Non-Drive End (MNDE) 532,

Motor Drive End (MDE) 534, Pump Drive End (PNDE) 536 and Pump Non-Drive End (PNDE) 538. There is at least one accelerometer measuring radial vibration in mm/s^A2 on each of the four positions. As shown in Figure 5B, there are also at least two proximity probes x and y measuring the distance to the shaft in pm at each of the four positions. The proximity x and y probes are placed 90° from each other and serves as input to the Orbit plots, the figure below illustrates the sensor setup.

[00116] At MDE 534 and PDE 536 there are also axial vibration sensors as well as proximity probe measuring distance in the z direction. These sensors monitor the magnitude of vibrations on the mechanical seal between the motor and the pump module. On the MDE 534 the proximity z sensor measures the distance to a speed disc (not shown), the signal from this sensor is input to the Pump App 432 to calculate a rotation speed, a rotation direction, by algorithms that identify in the proximity z sensor data a series of marks on the of the shaft.

[00117] Thus pump CPM components shown in Figure 5 provide a live stream of sensor data to the Pump App 432 which includes algorithms to contemporaneously and continuously process the live stream of data on the data bus and provide an operator at workstation 230 with views and live updates for: pump shaft vibration frequency spectra; pump shaft rotational speed; bode plots; and orbit plots and orbit alignment. Further Pump CPM components may be provided to monitor other aspects of the pump performance for processing by the Pump App 432. The sensor data from the Pump CPM Apparatus and the processing thereof in accordance with various examples of the present disclosure will be described further below in relation to Figures 6, 7 and 8 in particular.

[00118] Figure 6 shows data flows from one or more CPM apparatuses 240a,b...n including a Pump CPM apparatus 612 monitoring the pump 500 and a Process CPM apparatus 614 monitoring the process system in relation to the pump 500.

[00119] Pump CPM apparatus 612 provides to the Pump App 342 a stream of sensor signals indicating the current of the pump motor 510, the motor frequency, the motor cooling temperature, and also z proximity probe data from the MDE 534 indicative of the rotation of the speed disc connected to drive shaft 505.

[00120] Process CPM apparatus 612 provides to the Pump App 342 a stream of sensor signals indicating: a sensed gas volume fraction of the pumped product; a sensed density ratio of the pumped product; a temperature of the flowing product; a volumetric flow rate of the flowing product; and a suction pressure and discharge pressure in the flow line before and after the pump 500.

[00121] Together, the Pump CPM apparatus 612 and Process CPM apparatus 614 produce CPM Sensor Data 610 used by the Pump App 342 to monitor the condition and performance of the pump seamlessly and in real time. The data flows from the CPM apparatuses 240a,b...n may be relayed as CPM Sensor Data 610 to the FMS Pump app 342 seamlessly and in real time, in examples as data published in a universal data format on a data bus as described above in relation to Figures 2, 3 and 4. The Pump App 342 may subscribe to the Pump CPM Sensor Data 610, which may be received at the pump app as one or more data streams on the data bus.

[00122] As will now be described, in accordance with some examples of the present disclosure the Pump App 342 receives this data and performs data-processing therein to produce a real-time pump plot, as shown in Figure 8. The processing of this data by the Pump App 342 for will now be described also with reference to Figure 7, which shows a process flow diagram for a method 700 for monitoring the operational performance of a subsea pump in accordance with some examples of the present disclosure.

[00123] In 710, the Pump App 342 continually receives an operating condition data stream comprising data representative of an operating condition of the pump derived from a subsea sensing system.

[00124] In 720, the Pump App 342 contemporaneously retrieves predicted pump performance data from the stored pump performance prediction model 436 based on a latterly received instant of the operating condition data stream data.

[00125] In 730, the Pump App 342 continually receives an actual pump performance data stream comprising data representative of the actual operational performance of the pump derived from the sensor system of subsea pump; and [00126] In 740, the Pump App 342 continually provides a comparison of synchronised latterly retrieved predicted pump performance data with a latterly received actual pump performance data. As will be described below in relation to Figures 6, 8A and 8B, this comparison may be provided on a pump plot.

[00127] After 740, the Pump App 342 loops around again in a continuous process to seamlessly receive the operating condition data stream and actual pump performance data stream and update the pump plots to give a live view of pump performance, effectively in real time.

[00128] The component processes of the method 700 will now be explained in further detail.

[00129] In 710, the Pump App 342 continually receives an operating condition data stream comprising data representative of an operating condition of the pump derived from a subsea sensing system. In examples, the received operating condition data stream may be received from Pump and Process CPM apparatus and may comprise data or be processed by the Pump App 342 to provide data representative of:

• a sensed gas volume fraction of the pumped product;

• and a sensed density ratio of the pumped product; and • a shaft speed driven by the motor.

[00130] As can be seen from Figure 6, the sensed gas volume fraction of the pumped product and the sensed density ratio of the pumped product are obtained from the Process CPM apparatus 612. The shaft speed driven by the motor is obtained by the Pump App 342 taking in data from the z proximity probe data from the MDE 534 of pump 500 received from the Pump CPM Apparatus 612 and processing it at a shaft speed module 622 to recover the rotation speed of the disc connected to drive shaft 505.

[00131] In 720, where the Pump App 342 contemporaneously retrieves predicted pump performance data from the stored pump performance prediction model 436, for fast recovery and retrieval, the pump performance prediction model 436 may store data as a Look-Up Table (LUT) in persistent storage 434. The pump performance prediction model 436 may store data representative of the pump performance and the operating conditions of the pump 500. Based on the latterly received instant of the operating condition data stream data, the data representative of the predicted pump performance is retrieved from the LUT by a “make pump curves” module 624 of the Pump App 342.

[00132] The pump performance prediction model 436 may store retrievable predicted pump performance data for given values in ranges of operating conditions of the pump 500. That is, pump performance data is stored for given values in tested ranges of: gas volume fractions and density ratios of a pumped product; and shaft speeds driven by the motor. That is, for a given gas volume fraction and density ratio of a pumped product, and for a given shaft speed of a pump driven by the motor, the pump performance prediction model 436 can provide data predicting the pump’s “as-new” performance (provided the given values are within the tested or modelled ranges). Thus, based on the latterly received instant of the operating condition data stream data (or windowed average thereof over an update interval), the Pump App 342 retrieves, based on the current (i.e. latterly received) operating conditions (product GVF, density ratio and shaft speed), data representative of the predicted performance of the pump in the “as-new” condition.

[00133] The pump performance prediction model data may be generated based on one or more of:

• a theoretically-derived standard model of the performance of a pump of the type being monitored - i.e. a theoretical model of the pump 500 (suitable theoretical basis for the pumps model may be derivable from Centrifugal Pumps, by Gulich, J.F., Springer (2010);

• a standard model of the performance of a pump of the type being monitored derived empirically through testing - i.e. an empirical model of the pump 500 - or theoretically or semi-empirically through testing and pump theory;

• a factory acceptance test of the actual subsea pump 500 being monitored (i.e. from testing performed to characterised the actual performance of the pump 500, for example in a factory acceptance test performed before the pump 500 is deployed).

[00134] In this way, the pump performance prediction model 436 provides data representative of the how the pump 500 is predicted to be performing for its current operational state (based on the latterly received instant of the operating condition data stream data), if it were effectively in an “as-new” condition, in accordance with a standard theoretical model for that type of pump, a standard empirical dataset for a test for that type of pump, and/or an empirical dataset from a test, such as a factory acceptance test, for the specific pump being monitored, conducted before the pump 500 was deployed.

[00135] Where the pump performance prediction model data is generated at least in part empirically, a test setup for performing characterising the pump performance and generating the test data, will be described below in relation to Figures 10 and 11. This data may be used alone to provide an empirical model, or to adapt a theoretical model for the pump to provide a semi-empirical model.

[00136] As can be seen from Figures 8A and 8B, the Pumps App 342 in examples may produce live pump plots comparing the actual operational versus the predicted operational pump performance, the pump plots optionally being provided to be displayed on workstation 230.

[00137] In examples, two the pump plots are produced. The first, shown in Figure 8A, plots the differential pressure created by the pump 500 (e.g. in bar) against the volumetric flow rate for the pump 500 (in cubic metres per hour). The second, shown in Figure 8B, plots the shaft power (in kW) of the pump 500 and the efficiency of the pump 500 (in percent, %) against the volumetric flow rate for the pump (again in cubic metres per hour).

[00138] To plot the predicted operational pump performance, the predicted pump performance data retrieved from the pump performance prediction model may be indicative of one or more of:

• the predicted variation of differential pressure created by the subsea pump under test;

• the predicted variation of the shaft power of the subsea pump under test;

• the predicted variation of the efficiency of the subsea pump under test;

[00139] The above predicted variations being at different volumetric pump flow rates for the sensed operating condition of the pump.

[00140] This performance data retrieved from the Pump App 342 from the pump performance prediction model 436 is plotted as predicted performance pump curves on the respective pump plots.

[00141] The predicted variation of differential pressure created by the subsea pump under test is plotted as a predicted pressure curve 812 the plot in Figure 8A. The predicted pressure curve 812 shows the predicted variation of the differential pressure created by the pump at different flow rates for the current pump running speed, GVF and product density ratio.

[00142] The predicted variation of the shaft power of the subsea pump under test is plotted as a predicted shaft power curve 822 the plot in Figure 8B. The predicted shaft power curve 822 shows the predicted variation of the shaft power created by the pump at different flow rates for the current pump running speed, GVF and product density ratio.

[00143] The predicted variation of the efficiency of the subsea pump under test is plotted as a predicted efficiency curve 824 the plot in Figure 8B. The predicted efficiency curve 824 shows the predicted variation of the efficiency created by the pump at different flow rates for the current pump running speed, GVF and product density ratio.

[00144] The pump performance prediction model 436 may also store predicted pump performance envelope data, which may define for the pump operational limits, such as acceptable operational limits within which the pump is intended to be operated. Operation of the pump outside the predicted pump performance envelope may comprise data representative of the predicted variation of differential pressure created by the subsea pump under test at different volumetric pump flow rates for the sensed operating condition of the pump and for at least one of:

• a maximum pump shaft speed;

• a minimum pump shaft speed;

• a maximum volumetric pump flow rate;

• a minimum volumetric pump flow rate.

[00145] The curves making up this operational pump performance envelope may be plotted on the pump plot as shown in Figure 8 as a window 814 which encloses acceptable pump performance.

[00146] A maximum shaft power for the current sensed operating condition of the pump may also be recovered and plotted as an envelope on the pump plot in Figure 8B.

[00147] In 730, the Pump App 342 continually receives an actual pump performance data stream comprising data representative of the actual operational performance of the pump derived from the sensor system of subsea pump. In examples, the received actual pump performance data stream may be received from Pump and Process CPM apparatus and may comprise data or be processed by the Pump App 342 to provide data representative of:

• volumetric pump flow rate;

• the actual operational shaft power;

• the actual operational efficiency of the pump; and • the actual operational differential pressure created by the pump.

[00148] Referring to Figure 6, the CPM data representative of: the sensed temperature of the flowing product; the volumetric flow rate of the flowing product; and the suction pressure and discharge pressure in the flow line before and after the pump 500; may be received from the Process CPM apparatus 614 and taken in at the Pump App 342 where it is passed to a pump performance module 626. The pump performance module 626 may calculate the actual efficiency of the pump and actual differential pressure of the pump using at least one of: temperature of the product; rate of flow of the product; suction pressure of the product; and discharge pressure of the product.

[00149] Referring again to Figure 6, the CPM data representative of: motor current; motor frequency; and motor cooling temperature; may be received from the Pump CPM apparatus 612 and taken in at the Pump App 342 where it is passed to a motor output power module 628. The motor output power module 628 may calculate the actual shaft power.

[00150] The actual differential pressure ofthe pump and the actual efficiency ofthe pump calculated by the pump performance module 626 and the actual shaft power calculated by the motor output power module 628 may represent the actual pump performance data stream comprising data representative ofthe actual operational performance ofthe pump.

[00151] The Pump App 342 may then provide a comparison of the actual pump performance data by plotting points representative of the actual differential pressure of the pump 816 on the pump plot in Figure 8A, and the actual efficiency of the pump 826 and the actual shaft power 828 on the pump plot in Figure 8B synchronised with the predicted pump performance curves for the operational condition pump and actual performance retrieved for effectively the same instant.

[00152] The Pump App 342 may continually update the predicted pump performance data by retrieving data from the stored pump performance prediction model at intervals as more recent instants of operating condition data stream data are received. The plotting of this synchronised actual and predicted pump performance data on pump plots is in this way performed continually, as the data is received, and is updated as further instants of data are received.

[00153] The Pump App 342, as it may continually receive the operating condition data stream and actual pump performance data stream and contemporaneously retrieve predicted pump performance data, may cause the continually provided comparison of predicted pump performance data with actual pump performance data to be effectively updated in real time as the data streams are received and as the pump is pumping product from the formation.

[00154] As indicated above, the normal update interval of input sensing data in the received operating condition data stream and actual pump performance data stream data may be at a sample rate of at least 100 Hz, optionally at least 1000 Hz, optionally at least 5000 Hz, optionally at least 10 kHz, optionally 12 kHz. It may occur that data loss occurs due to interruption of the sensors or a connection therewith, and so the data flow may be inadvertently temporarily interrupted. However, in normal operation, input sensing data is continually received at a high sampling rate.

[00155] In examples, while the input sensing data is received at a high sampling rate, the normal update interval for the predicted pump performance data and the actual pump performance data may be at most 1 minute, optionally at most 30 seconds, optionally at most 10 seconds, at most 5 seconds, at most 1 second. In this way, the pump plots are regularly updated by the Pump App 342 as data is received. The predicted pump performance data and the actual pump performance data may be generated periodically based on a windowed average of the input sensing data in the received operating condition data stream and actual pump performance data stream data.

[00156] As can be seen from Figure 8A, the actual differential pressure 816 achieved by the pump is less, or lower on the plot, than the differential pressure curve 812 predicted for pump by the “as-new” model at the current pump rotation speed (and GVF and density ratio), indicating that the pump is not creating as much differential pressure as expected. However, the actual differential pressure 816 is inside the operational envelope 814.

[00157] As can be seen from Figure 8B, the actual shaft power 828 of the pump is greater, or higher on the plot, than the shaft power curve 822 predicted for pump by the “as-new” model at the current pump rotation speed (and GVF and density ratio), indicating that the shaft power of the pump is greater than expected.

[00158] As can also be seen from Figure 8B, the actual efficiency 826 of the pump is less, or lower on the plot, than the efficiency curve 824 predicted for pump by the “as-new” model at the current pump rotation speed (and GVF and density ratio), indicating that the efficiency of the pump is less than expected.

[00159] From the efficiency curve 824 predicted for pump by the “as-new” model at the current pump rotation speed, the Pump App 342 may determine a most efficient operating condition for the pump. The most efficient operating condition for the pump may be determined by the Pump App 342 as being the volumetric flow rate 830 at which the highest efficiency is achieved for the current running speed. That is the volumetric flow rate 830 at which the predicted pump efficiency peaks at a maximum. The Pump App 342 may provide to a user interface of the apparatus the determined most efficient operating condition for the pump, as indicated by the marker 830 on the pump plot in Figure 8A. In examples, the Pump App 342, or the FMS 222 or MCS 220 may generate, based on the determined most efficient operating condition for the pump, control data usable to adjust the operational parameters of the pump. For example, the MCS 220 could automatically or semi-automatically send a control signal to the VSD 270 to adjust the speed of the pump so as to change the volumetric flow rate of the product towards the volumetric flow rate at which the pump efficiency is predicted to be at a maximum.

[00160] To determine and record whether the pump 500 is being operated in such a way so as to, for example, cause excessive wear on the pump 500, or simply to measure its use in certain operational states whether damaging or otherwise, in examples, the Pump App 342 carry out a process 900 which will now be described with reference to Figure 9.

[00161] In 910, the Pump App 342 may continually receive an operating condition data stream comprising data representative of an operating condition of the pump derived from a subsea sensing system.

[00162] In 920, the Pump App 342 may contemporaneously retrieve predicted pump performance envelope data from the stored pump performance prediction model 436 based on a latterly received instant of the operating condition data stream data. The predicted pump performance envelope data may define an acceptable operational envelope for the current operating conditions of the pump 500.

[00163] In 930, the Pump App 342 may continually receive an actual pump performance data stream comprising data representative of the actual operational performance of the pump derived from the sensor system of subsea pump.

[00164] Then, in 940, the Pump App 342 may compare the received operating condition data or the received actual pump performance data or both with one or more pump operational state alert criteria (such as the retrieved pump performance envelope data).

[00165] In 950, the Pump App 342 may generate pump state event record data when one or more of the pump operational state alert criteria are satisfied. The pump state event record data may be a increment of a count of the frequency or duration of occurrence of pump operations triggering operational state alert criteria.

[00166] the Pump App 342 may store or update a pump state event record data in memory, such as persistent storage 424, to provide a record of the pump’s state experience during operation against alert criteria.

[00167] In examples, the pump operational state alert criteria may include one or more of: the number of successful starts; the number of failed starts; the number of stops; the number of emergency stops; the operation of the pump within a critical or resonant frequency region; the operation of the pump outside operational limits for the pump (such as the retrieved pump performance envelope data); the operation of the motor outside operational limits for the motor; and the operation of the pump in a high vibration state.

[00168] In examples, the Pump App 342 may generate pump state event record data to monitor the time during or frequency with which the actual pump performance data is outside synchronised predicted pump performance envelope data defining the operational limits for the instant performance of the pump.

[00169] In examples, the Pump App 342 may generate pump state event record data to count, based on the shaft speed data, the number of successful starts, failed starts, stops and emergency stops.

[00170] In examples, the Pump App 342 may generate pump state event record data to monitor, based on the shaft speed data, the time during or frequency with which the shaft speed corresponds to a resonant frequency of the pump.

[00171] In examples, the Pump App 342 may generate pump state event record data to monitor based on the motor operation data the time during or frequency with which the motor operates outside operational limits defined for the motor.

[00172] In examples, the Pump App 342 may generate pump state event record data to monitor based on pump vibration data the time during or frequency with which the pump vibrates in a high vibration state defined for the pump.

[00173] Referring now to Figures 10 and 11, an example a testing apparatus and method of operation thereof for generating an empirically or semi-empirically derived pump performance prediction model in accordance with some examples of the present disclosure will now be described.

[00174] In examples, the testing apparatus 1000 may comprise a flow line 1005 circulating a product between a separator 1010 and pump 1020. The pump 1020 being modelled may be the actual pump 500 to be deployed in operation subsea, or it may be a pump of the same type as the actual pump 500.

[00175] The test product flowing in flow line 1005 may have a gas phase and a liquid phase and may be actual petrochemical product or a fluid simulation thereof, such as a water/air mix.

[00176] The separator 1010 may, in use, adjustably separate out the oil and gas components of a test product flowing in flow line 1005 to adjust the gas volume fraction and/or density ratio of the test product.

[00177] The pump 1020 may comprise, as in Figure 5, a motor driving a shaft supporting an impeller for creating a pressure differential across the pump to pump the product around a test flow line 1005 and through the separator 1010 [00178] The testing apparatus 1000 may also have a GVF/Density sensing system 1030 coupled to the separator 1010 and flow line 1005 to, in use, measure the gas volume fraction of the product and measure the density ratio of the product.

[00179] The testing apparatus 1000 may also have a pump sensing system 1050 coupled to the pump 1020 to, in use, measure the pump shaft speed gas, volumetric pump flow rate, actual operational shaft power volume fraction of the the actual operational efficiency of the pump, and the actual operational differential pressure created by the pump. Similar apparatus for determining these values may be used as in the operational setup described above.

[00180] The testing apparatus 1000 may also have a controller, such as one or more processors acting under control of pump modelling software, configured to provide a pump modelling engine 1050. The modelling engine 1050 may be operatively coupled to each of the separator 1010 and motor 1020 to control the separator and the pump, and also to the GVF/density sensing system 1030 and pump sensor system 1040.

[00181] With reference to Figure 11, a method 1100 will now be described for operating the testing apparatus 1000 for generating an empirically or semi-empirically derived pump performance prediction model usable in the Pump App 342 as pump performance prediction model 436.

[00182] In 1110, the modelling engine 1050 may, in use, control at least the separator 1010 and the pump 1020 to vary one or more operational condition parameters for the pump 1020, including:

• the speed of the shaft as driven by the motor;

• the gas volume fraction of the product; or • the density ratio of the product.

[00183] The shaft speed, GVF and density ratio may be varied over operational ranges for the pump 1020.

[00184] In 1120, the modelling engine 1050 may receive from the GVF/density sensing system 1030 data representative of a measured gas volume fraction and density ratio of the product.

[00185] In 1130, the modelling engine 1050 may receive from the pump sensor system 1040 data representative of: a measure of the speed of the motor shaft; receive data representative of a measure of the volumetric pump flow rate, the actual operational shaft power, the actual operational efficiency of the pump, and the actual operational differential pressure created by the pump.

[00186] In 1140, the modelling engine 1050 may generate a pump performance prediction model data based on the received pump performance data and the synchronised test operational conditions. That is, data is generated for the model indicative of the pump performance data representing the: current volumetric pump flow rate; the actual operational shaft power; the actual operational efficiency of the pump; and the actual operational differential pressure created by the pump; and this is stored in the model and related to data representative of the current operational conditions of the test system 1000, including data representative of: the measured gas volume fraction and density ratio of the product; and the measured of the speed of the motor shaft. The modelling engine may store the generated pump performance prediction model data in a lookup table (LUT).

[00187] Once the pump performance has been characterised for the current operational conditions, the modelling engine 1050 may control at least the separator 1010 and the pump 1020 to further vary one or more operational condition parameters for the pump 1020 until the model has been populated across the operational range of the pump 1020.

[00188] The modelling engine 1050 may be further configured to generate the pump performance prediction model data based also on a theoretically-derived standard model of the performance of a pump of the type being monitored.

[00189] Throughout the description and claims of this specification, the words “comprise” and “contain” and variations of them mean “including but not limited to”, and they are not intended to (and do not) exclude other moieties, additives, components, integers or steps. Throughout the description and claims of this specification, the singular encompasses the plural unless the context otherwise requires. In particular, where the indefinite article is used, the specification is to be understood as contemplating plurality as well as singularity, unless the context requires otherwise.

[00190] Features, integers, characteristics, compounds, chemical moieties or groups described in conjunction with a particular aspect, embodiment or example of the invention are to be understood to be applicable to any other aspect, embodiment or example described herein unless incompatible therewith. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and/or all of the steps of any method or process so disclosed, may be combined in any combination, except combinations where at least some of such features and/or steps are mutually exclusive. The invention is not restricted to the details of any foregoing embodiments. The invention extends to any novel one, or any novel combination, of the features disclosed in this specification (including any accompanying claims, abstract and drawings), or to any novel one, or any novel combination, of the steps of any method or process so disclosed.

Claims

1. Apparatus for monitoring the operational performance of a subsea pump for pumping product from a formation, the apparatus comprising:

machine readable storage comprising a pump performance prediction model storing data representative of the pump performance and the operating conditions of the pump; and a controller configured to, in operational use:

continually receive an operating condition data stream comprising data representative of an operating condition of the pump derived from a subsea sensing system;

contemporaneously retrieve predicted pump performance data from the stored pump performance prediction model based on a latterly received instant of the operating condition data stream data;

continually receive an actual pump performance data stream comprising data representative of the actual operational performance of the pump derived from the sensor system of subsea pump; and continually provide a comparison of synchronised latterly retrieved predicted pump performance data with a latterly received actual pump performance data.

2. Apparatus as claimed in claim 1, wherein the pump performance prediction model data is generated based on one or more of:

a theoretically-derived standard model of the performance of a pump of the type being monitored;

a standard model of the performance of a pump of the type being monitored derived empirically through testing or theoretically or semi-empirically through testing and pump theory;

a factory acceptance test of the actual subsea pump being monitored.

3. Apparatus as claimed in claim 1 or 2, wherein the controller is further configured to continually update the predicted pump performance data by retrieving data from the stored pump performance prediction model at intervals as more recent instants of operating condition data stream data are received.

4. Apparatus as claimed in claim 1, 2 or 3, wherein the controller being configured to continually receive the operating condition data stream and actual pump performance data stream and contemporaneously retrieve predicted pump performance data causes the continually provided comparison of predicted pump performance data with actual pump performance data to be effectively updated in real time as the data streams are received and as the pump is pumping product from the formation.

5. Apparatus as claimed in any preceding claim, further comprising the controller being configured to provide the comparison of the synchronised predicted pump performance data with the actual pump performance data as graph plots for rendering on a display.

6. Apparatus as claimed in any preceding claim, wherein the normal update interval of input sensing data in the received operating condition data stream and actual pump performance data stream data is at a sample rate of at least 100 Hz, optionally at least 1000 Hz, optionally at least 5000 Hz, optionally at least 10 kHz, optionally 12 kHz.

7. Apparatus as claimed in any preceding claim, wherein the normal update interval for the predicted pump performance data and the actual pump performance data is at most 1 minute, optionally at most 30 seconds, optionally at most 10 seconds, at most 5 seconds, at most 1 second, and optionally wherein the predicted pump performance data and the actual pump performance data is generated periodically based on a windowed average of the input sensing data in the received operating condition data stream and actual pump performance data stream data.

8. Apparatus as claimed in any preceding claim, wherein the memory stores the pump performance prediction model data generated from a factory acceptance test of the subsea pump as a Lookup Table (LUT), usable to retrieve data representative of the pump performance based on data representative of an operating condition of the pump.

9. Apparatus as claimed in any preceding claim, wherein the pump has a motor driving a shaft supporting an impeller for creating a pressure differential across the pump.

10. Apparatus as claimed in any preceding claim, wherein the pump performance prediction model stores retrievable predicted pump performance data for given values in tested ranges of: gas volume fractions and density ratios of a pumped product; and shaft speeds driven by the motor.

11. Apparatus as claimed in claim 9 or 10, wherein the predicted pump performance data retrieved from the pump performance prediction model is indicative of one or more of:

the predicted variation of differential pressure created by the subsea pump under test;

the predicted variation of the shaft power of the subsea pump under test;

the predicted variation of the efficiency of the subsea pump under test;

the above predicted variations being at different volumetric pump flow rates for the sensed operating condition of the pump.

12. Apparatus as claimed in claim 9, 10 or 11, wherein the received operating condition data stream comprises data representative of a sensed gas volume fraction and a density ratio of the pumped product, and a shaft speed driven by the motor.

13. Apparatus as claimed in any of claims 9 to 12, wherein the received actual pump performance data stream comprises data representative of volumetric pump flow rate, the actual operational shaft power, the actual operational efficiency of the pump, and the actual operational differential pressure created by the pump.

14. Apparatus as claimed in claim 13, wherein the controller is further configured to calculate the actual efficiency of the pump and actual differential pressure of the pump using at least one of:

temperature of the product; rate of flow of the product; suction pressure of the product; and discharge pressure of the product;

the above being as sensed by the subsea sensing system or the sensor system of subsea pump and being received by the controller in a data stream.

15. Apparatus as claimed in claims 13 or 14, wherein the actual shaft power is calculated using at least one of:

motor current;

motor frequency; and motor cooling temperature;

the above being as sensed by the sensor system of subsea pump and being received by the controller in a data stream.

16. Apparatus as claimed in any of claims 9 to 15, the controller being further configured to:

contemporaneously retrieve predicted pump performance envelope data from the stored pump performance prediction model based on a latterly received instant of the operating condition data stream data;

wherein the predicted pump performance envelope data comprises data representative of the predicted variation of differential pressure created by the subsea pump under test at different volumetric pump flow rates for the sensed operating condition of the pump and for at least one of:

a maximum pump shaft speed; a minimum pump shaft speed; a maximum volumetric pump flow rate; a minimum volumetric pump flow rate.

17. Apparatus as claimed in claim 16, the controller being further configured to provide a comparison of the synchronised predicted pump performance envelope data with the actual pump performance data; said controller optionally being further configured to provide the of the synchronised predicted pump performance envelope data with the actual pump performance data as graph plots for rendering on a display.

18. Apparatus as claimed in any preceding claim, wherein the controller is further configured to determine, from the comparison of the actual pump performance data and the predicted pump performance data, a most efficient operating condition for the pump.

19. Apparatus as claimed in claim 18, wherein the controller is further configured to determine the most efficient operating condition for the pump as being the volumetric flow rate at which the highest efficiency is achieved for the current running speed.

20. Apparatus as claimed in claim 18 or 19, wherein the controller is further configured to provide to a user interface of the apparatus the determined most efficient operating condition for the pump, and/or wherein the controller is further configured to generate, based on the determined most efficient operating condition for the pump, control data usable to adjust the operational parameters of the pump.

21. Apparatus as claimed in any preceding claim, further comprising the pump.

22. Apparatus as claimed in any preceding claim, wherein the controller is provided by one or more processors operating under program control to together or individually configure the controller as claimed, wherein one or more or all of the processors are located subsea or one or all of the configured functions of the controller are implemented by one or more processors located subsea, with the remainder or some or all of the processors or functions of the controller being implemented at one or more locations: topside; onshore; in the cloud.

23. Apparatus as claimed in any preceding claim, wherein the one or both of the operating condition data stream and the actual pump performance data stream are received from one or more subsea electronics modules (SEMs)

24. Apparatus as claimed in any preceding claim, wherein the controller is further configured to compare the received operating condition data or the received actual pump performance data or both with one or more pump operational state alert criteria, and to generate pump state event record data when one or more of the pump operational state alert criteria are satisfied.

25. Apparatus as claimed in claim 24, wherein the controller is further configured to store or update a pump state event record data in memory to provide a record of the pump’s state experience during operation against alert criteria.

26. Apparatus as claimed in claim 24 or 25, wherein the pump operational state alert criteria include one or more of:

the number of successful starts;

the number of failed starts;

the number of stops;

the number of emergency stops;

the operation of the pump within a critical or resonant frequency region; the operation of the pump outside operational limits for the pump; the operation of the motor outside operational limits for the motor; and the operation of the pump in a high vibration state.

27. Apparatus as claimed in claim 26, wherein the received operating condition data stream comprises data representative of a shaft speed driven by the motor, and wherein the controller is further configured to count, based on the shaft speed data, the number of successful starts, failed starts, stops and emergency stops.

28. Apparatus as claimed in claim 26 or 27, wherein the received operating condition data stream comprises data representative of a shaft speed driven by the motor, and wherein the controller is further configured to monitor, based on the shaft speed data, the time during or frequency with which the shaft speed corresponds to a resonant frequency of the pump.

29. Apparatus as claimed in claim 26, 27 or 28, wherein the controller is further configured to retrieve predicted pump performance envelope data from the stored pump performance prediction model based on received instants of the operating condition data stream data, the predicted pump performance envelope data defining the operational limits for the instant performance of the pump; wherein the controller is further configured to monitor the time during or frequency with which the actual pump performance data is outside the synchronised predicted pump performance envelope data defining the operational limits for the instant performance of the pump.

30. Apparatus as claimed in claim 29, wherein the predicted pump performance envelope data comprises data representative of the predicted variation of differential pressure created by the subsea pump under test at different volumetric pump flow rates for the sensed operating condition of the pump and for at least one of:

a maximum pump shaft speed;

a minimum pump shaft speed; a maximum volumetric pump flow rate;

a minimum volumetric pump flow rate.

31. Apparatus as claimed in any of claims 26 to 30, wherein the controller is further configured to receive, in a data stream from a sensor system of the subsea pump, motor operation data including at least one of: a motor current; a motor voltage; a motor frequency; and a motor cooling temperature; wherein the controller is configured to monitor based on the motor operation data the time during or frequency with which the motor operates outside operational limits defined for the motor.

32. Apparatus as claimed in any of claims 26 to 31, wherein the controller is further configured to receive, in a data stream from a sensor system of the subsea pump, pump vibration data including at least one of: pump shaft proximity at a drive end of the pump and/or the motor; pump shaft proximity at a non-drive end of the pump and/or the motor; pump shaft vibration at a drive end of the pump and/or the motor; pump shaft vibration at a non-drive end of the pump and/or the motor; wherein the controller is configured to monitor based on the pump vibration data the time during or frequency with which the pump vibrates in a high vibration state defined for the pump.

33. Apparatus for generating an empirically or semi-empirically derived pump performance prediction model comprising stored machine readable data generated based on a factory acceptance test of a subsea pump to be monitored in operation or testing of a standard subsea pump of a type to be monitored, said data being representative of the pump performance and the operating conditions of the pump; the apparatus comprising:

a separator to, in use, separate the components of a product to adjust the gas volume fraction and/or density ratio of the product;

a motor driving a shaft supporting an impeller for creating a pressure differential across the pump to pump the product around a test flow line and through the separator;

a sensing system coupled to the separator to, in use;measure the gas volume fraction of the product; measure the density ratio of the product a controller operatively coupled to each of the separator, motor and sensing system and configured to, in use:

vary one or more of:

the speed of the shaft as driven by the motor; the gas volume fraction of the product; or the density ratio of the product;

the controller being further configured to:

receive from the sensor system the data representative of a measured gas volume fraction and density ratio of the product;

receive data representative of a measure of the speed of the motor shaft;

receive data representative of a measure of the volumetric pump flow rate, the actual operational shaft power, the actual operational efficiency of the pump, and the actual operational differential pressure created by the pump;

and to generate a pump performance prediction model data based on the received data.

34. Apparatus as claimed in claim 33, wherein the controller is further configured to store the generated pump performance prediction model data in a lookup table (LUT).

35. Apparatus as claimed in claim 33 or 34, wherein the controller is further configured to generate the pump performance prediction model data based also on a theoreticallyderived standard model of the performance of a pump of the type being monitored.

36. Apparatus for monitoring the operational performance of a subsea pump for pumping product from a formation against pump operational state alert criteria, the apparatus comprising a controller configured to, in operational use:

continually receive an actual pump performance data stream comprising data representative of the actual operational performance of the pump derived from the sensor system of subsea pump;

compare the received operating condition data or the received actual pump performance data or both with one or more pump operational state alert criteria, and to generate pump state event record data when one or more of the pump operational state alert criteria are satisfied.

37. Apparatus as claimed in claim 36, wherein the controller is configured to continually receive the operating condition data stream and actual pump performance data stream in real time as the data streams are received and as the pump is pumping product from the formation.

38. Apparatus as claimed in claim 36 or 37, wherein the normal update interval of input sensing data in the received operating condition data stream and actual pump performance data stream data is at a sample rate of at least 100 Hz, optionally at least 1000 Hz, optionally at least 5000 Hz, optionally at least 10 kHz, optionally 12 kHz.

39. Apparatus as claimed in claim 36, 37 or 38, wherein the controller is further configured to store or update a pump state event record data in memory to provide a record of the pump’s state experience during operation against alert criteria.

40. Apparatus as claimed in any of claims 36 to 39, wherein the pump operational state alert criteria include one or more of:

the number of successful starts;

the number of failed starts;

the number of stops;

the number of emergency stops;

41. Apparatus as claimed in claim 40, wherein the received operating condition data stream comprises data representative of a shaft speed driven by the motor, and wherein the controller is further configured to count, based on the shaft speed data, the number of successful starts, failed starts, stops and emergency stops.

42. Apparatus as claimed in claim 40 or 41, wherein the received operating condition data stream comprises data representative of a shaft speed driven by the motor, and wherein the controller is further configured to monitor, based on the shaft speed data, the time during or frequency with which the shaft speed corresponds to a resonant frequency of the pump.

43. Apparatus as claimed in claim 40, 41 or 42, further comprising machine readable storage comprising a pump performance prediction model storing data representative of the pump performance and the operating conditions of the pump; wherein the controller is further configured to retrieve predicted pump performance envelope data from a pump performance prediction model based on received instants of the operating condition data stream data, the predicted pump performance envelope data defining the operational limits for the instant performance of the pump; wherein the controller is further configured to monitor the time during or frequency with which the actual pump performance data is outside the synchronised predicted pump performance envelope data defining the operational limits for the instant performance of the pump.

44. Apparatus as claimed in claim 43, wherein the predicted pump performance envelope data comprises data representative of the predicted variation of differential pressure created by the subsea pump under test at different volumetric pump flow rates for the sensed operating condition of the pump and for at least one of:

a maximum pump shaft speed; a minimum pump shaft speed; a maximum volumetric pump flow rate;

a minimum volumetric pump flow rate.

45. Apparatus as claimed in any of claims 40 to 44, wherein the controller is further configured to receive, in a data stream from a sensor system of the subsea pump, motor operation data including at least one of: a motor current; a motor voltage; a motor frequency; and a motor cooling temperature; wherein the controller is configured to monitor based on the motor operation data the time during or frequency with which the motor operates outside operational limits defined for the motor.

46. Apparatus as claimed in any of claims 40 to 45, wherein the controller is further configured to receive, in a data stream from a sensor system of the subsea pump, pump vibration data including at least one of: pump shaft proximity at a drive end of the pump and/or the motor; pump shaft proximity at a non-drive end of the pump and/or the motor; pump shaft vibration at a drive end of the pump and/or the motor; pump shaft vibration at a non-drive end of the pump and/or the motor; wherein the controller is configured to monitor based on the pump vibration data the time during or frequency with which the pump vibrates in a high vibration state defined for the pump.

47. Method of monitoring the operational performance of a subsea pump for pumping product from a formation, comprising:

continually receiving, at a controller, an operating condition data stream comprising data representative of an operating condition of the pump derived from a subsea sensing system;

contemporaneously retrieving, by the controller and based on a latterly received instant of the operating condition data stream data, predicted pump performance data from an pump performance prediction model storing data representative of the pump performance and the operating conditions of the pump;

continually receiving, at the controller, an actual pump performance data stream comprising data representative of the actual operational performance of the pump derived from the sensor system of subsea pump; and continually providing, by the controller, a comparison of synchronised latterly retrieved predicted pump performance data with a latterly received actual pump performance data.

48. Method of generating an empirically or semi-empirically derived pump performance prediction model comprising stored machine readable data generated based on a factory acceptance test of a subsea pump to be monitored in operation or testing of a standard subsea pump of a type to be monitored, said data being representative of the pump performance and the operating conditions of the pump; the method comprising:

separating, using a separator, the components of a product to adjust the gas volume fraction and/or density ratio of the product;

driving a shaft of a motor, the shaft supporting an impeller for creating a pressure differential across the pump to pump the product around a test flow line and through the separator;

measuring the gas volume fraction of the product;

measuring the density ratio of the product varying one or more of:

receiving, at a controller, data representative of a measured gas volume fraction and density ratio of the product;

receiving, at the controller, data representative of a measure of the speed of the motor shaft;

receiving, at the controller, data representative of a measure of the volumetric pump flow rate, the actual operational shaft power, the actual operational efficiency of the pump, and the actual operational differential pressure created by the pump;

and generating, by the controller, pump performance prediction model data based on the received data.

49. Method of monitoring the operational performance of a subsea pump for pumping product from a formation against pump operational state alert criteria, the method comprising:

continually receiving, at the controller, an actual pump performance data stream comprising data representative of the actual operational performance of the pump derived from the sensor system of subsea pump;

comparing, by the controller, the received operating condition data or the received actual pump performance data or both with one or more pump operational state alert criteria, and to generate pump state event record data when one or more of the pump operational state alert criteria are satisfied.

50. Computer readable medium comprising instructions which when executed by one or more processors, cause the processor or processors together to provide a controller for monitoring the operational performance of a subsea pump for pumping product from a formation, the controller being configured to:

contemporaneously retrieve predicted pump performance data from a stored pump performance prediction model based on a latterly received instant of the operating condition data stream data, the pump performance prediction model storing on machine readable storage data representative of the pump performance and the operating conditions of the pump;

51. Computer readable medium comprising instructions which when executed by one or more processors configure the processor or processors together to provide a controller for generating an empirically or semi-empirically derived pump performance prediction model comprising stored machine readable data generated based on a factory acceptance test of a subsea pump to be monitored in operation or testing of a subsea pump of a type to be monitored, said data being representative of the pump performance and the operating conditions of the pump; the controller being configured to:

control a separator to, in use, separate the components of a product to adjust the gas volume fraction and/or density ratio of the product;

control a motor driving a shaft supporting an impeller for creating a pressure differential across the pump to pump the product around a test flow line and through the separator;

control a sensing system coupled to the separator to, in use; measure the gas volume fraction of the product; measure the density ratio of the product;

vary one or more of:

the speed of the shaft as driven by the motor;

the gas volume fraction of the product; or the density ratio of the product;

the controller being further configured to:

receive data representative of a measure of the speed of the motor shaft;

52. Computer readable medium comprising instructions which when executed by one or more processors configure the processor or processors together to provide a controller for monitoring the operational performance of a subsea pump for pumping product from a formation against pump operational state alert criteria, the controller being configured to:

53. Apparatus for monitoring the operational performance of a subsea pump for pumping product from a formation, substantially as hereinbefore described, with reference to the accompanying drawings.

54. Method for monitoring the operational performance of a subsea pump for pumping product from a formation, substantially as hereinbefore described, with reference to the accompanying drawings.

55. Computer readable medium comprising instructions which when executed by one or more processors configure the processor or processors together to provide apparatus for monitoring the operational performance of a subsea pump for pumping product from a formation, substantially as hereinbefore described, with reference to the accompanying drawings.

56. Apparatus for generating an empirically or semi-empirically derived pump performance prediction model substantially as hereinbefore described, with reference to the accompanying drawings.

57. Method of generating an empirically or semi-empirically derived pump performance prediction model substantially as hereinbefore described, with reference to the accompanying drawings.

58. Computer readable medium comprising instructions which when executed by one or more processors configure the processor or processors together to generate an empirically or semi-empirically derived pump performance prediction model, substantially as hereinbefore described, with reference to the accompanying drawings.

59. Apparatus for monitoring the operational performance of a subsea pump for pumping product from a formation against pump operational state alert criteria

5 substantially as hereinbefore described, with reference to the accompanying drawings.

60. Method of monitoring the operational performance of a subsea pump for pumping product from a formation against pump operational state alert criteria substantially as hereinbefore described, with reference to the accompanying drawings.

61. Computer readable medium comprising instructions which when executed by one 10 or more processors configure the processor or processors together to monitor the operational performance of a subsea pump for pumping product from a formation against pump operational state alert criteria, substantially as hereinbefore described, with reference to the accompanying drawings.

Intellectual

Property

Office

Application No: GB 1614613.6 Examiner: Mr Peter Middleton