WO2021059224A1

WO2021059224A1 - Floating hydrocarbon production plant and system

Info

Publication number: WO2021059224A1
Application number: PCT/IB2020/058993
Authority: WO
Inventors: Jean-Damien ROLLIER
Original assignee: Single Buoy Moorings Inc.
Priority date: 2019-09-25
Filing date: 2020-09-25
Publication date: 2021-04-01

Abstract

Floating hydrocarbon production plant (100), comprising hydrocarbon processing equipment (101 to 116), a sensor (101a-d to 116a-d) for measuring a value of a process parameter of the hydrocarbon processing equipment, and a plant server system (120) for operating the floating hydrocarbon production plant offshore, comprising a first data interface server (130) configured to collect data generated by the sensor and to stream the data to a data historian server (140), the data historian server configured to store the data, to provide access to the data to an operator (170) of the plant, and to stream the data to an analytics server (150), and the analytics server configured to structure the data thereon and to perform analytics on the data, wherein the plant server system is configured to convert the data into actionable information about the process parameter on the basis of which the operator is enabled to assess and/or to control a processing performance of the hydrocarbon processing equipment.

Description

FLOATING HYDROCARBON PRODUCTION PLANT AND SYSTEM

The invention relates to a floating hydrocarbon production plant and a system comprising the same. In particular, the invention relates to a floating hydrocarbon production plant, comprising hydrocarbon processing equipment and a sensor for measuring a process parameter of the hydrocarbon processing equipment.

Floating hydrocarbon production plants, also known as floating production, storage and offloading facilities (FPSOs), are typically floating vessels used by the offshore oil and gas industry for the production and processing of hydrocarbons, such as oil and gas. Such vessels are specifically designed to receive hydrocarbons from a nearby plant or from a subsea template, to process the hydrocarbons on board, and to store the hydrocarbons until the hydrocarbons can be offloaded onto a tanker or transported towards an onshore facility by means of a pipeline.

An advantage of FSPOs to fixed oil platforms are e.g. that they can be developed faster than fixed platforms and thereby can provide earlier cash flows. In addition, they can be moved to other platforms and as a result, they retain value and costs can be spread over several fields due to their re-deploy ability. Moreover, FPSOs are also efficient for smaller fields in which there is a high possibility to be depleted quickly. As a result, oil and gas companies can avoid the cost of installing permanent pipelines and the decommissioning process can be made easier and less costly in comparison with the traditional oil platforms.

However, in spite of their higher cost efficiency over fixed platforms, there is still a lot to gain in terms of profitability. Oil companies could lose significant production due to unplanned downtime, causing a significant loss of profits. Due to the asset-intensive nature of the industry, any small improvement in FPSO utilization can result in a large gain in revenue and cash flow. Therefore, keeping FPSOs running as long and as optimally efficient as possible with minimum failure is of crucial importance to improving profitability and maximizing returns on projects.

An object of the present invention, next to other objects, is to improve operational efficiency of floating hydrocarbon production plants.

To meet this object, next to other objects, a floating hydrocarbon production plant according to claim 1 is provided. Specifically, a floating hydrocarbon production plant is provided, comprising hydrocarbon processing equipment, a sensor for measuring a value of a process parameter of the hydrocarbon processing equipment, and a plant server system for operating the floating hydrocarbon production plant offshore, comprising a first data interface server configured to collect data generated by the sensor and to stream the data to a data historian server, and the data historian server configured to store the data, to provide access to the data to an operator of the plant, and to stream the data to an analytics server.

Whereas the data interface server allows collecting the sensor data, the data historian server allows recording and retrieving production and process data by time. It preferably stores the information in a time-series database that can efficiently store data with minimal disk space and fast retrieval. Moreover, non-time-series information can be integrated in the data historian to provide context, such as processing equipment info and aggregate data. One of the important advantages of the data historian server is its ability to correlate data over time. The data historian server may provide operational data that is well organized and easily accessible, which enables the operator to make informed and fast decisions to improve productivity, quality and efficiency of the floating hydrocarbon production plant.

Preferably, the plant server system further comprises the analytics server configured to structure the data thereon and to perform analytics on the data. The analytics server allows carrying out analytics on the raw data, the pre-processed data and/or on the aggregated data, which may be descriptive, diagnostic or predictive. The analytics server thus allows describing the data in a way that it provides actionable information to the operator of the plant, to find a trend in the data over time or detect anomalies in the data, or to predict a future behavior of the processing equipment’s performance.

It is noted that at least one of the data collection, data storage and data analytics (i.e. analysis) may preferably comprise manual and/or automatic readout of the sensor of the plant.

Preferably, the plant server further comprises a data preparation server configured to pre-process the data and to aggregate the data. The preparation server allows pre-processing the data into pre- processed data using known pre-processing functions such as noise reduction, interpolation, normalization, etc. The analytics server further allows aggregating the raw data and/or pre- processed data into aggregated data, wherein the aggregating may comprise any aggregation function such as a sum, average, median, count, minimum, maximum, other aggregation functions, or any combination thereof. Optionally, the preparation server further allows transforming the data using transform functions, such as the Fourier transform or the Wavelet transform and other such functions. The preparation server thus allows processing the raw data in a way that improves the reliability of the data while simultaneously reducing the amount of data. The plant server system preferably allows converting the data into actionable information about the process parameter on the basis of which the operator is enabled to assess and/or to control a processing performance of the hydrocarbon processing equipment. The plant server system, preferably comprising an interface server, a data historian server, and an analytics server on the floating hydrocarbon production plant enables the operator to access raw and/or aggregated data relating to the production performance of the plant, to monitor different processes and to analyze and predict, using predictive models, the production performance behavior of the plant, so that the plant can be operated optimally when the plant is located offshore.

Specifically, the plant server system on the floating plant, i.e. a local solution and environment when the plant is offshore, makes the plant less dependent on a connection with a satellite to communicate with an operating center, which is typically located onshore. Hence, this allows for running and optimizing the plant’ s production performance even when a satellite connection with an onshore facility is down, in particular to maximize production uptime, to optimize production efficiency, and to maximize the plant’s operational lifetime, while at the same time preventing the stringent operational safety requirements from being compromised. Specifically, the combined plant server system provides the offshore operator a data entry solution to consolidate operational logs and reporting, and to reduce the operator’s time to report by collecting all data already electronically available and to allow for integrated information review and validation processes. Moreover, it allows the offshore operator to use digital solutions with enhanced experience, i.e. providing notifications, analytics and predictive models.

In a preferred embodiment, the plant server system further comprises a communication server configured to communicate with the (onshore) operating center and configured to stream the pre- processed data, the aggregated data, the analytics data or any combination thereof to the communication system. Preferably the communication server comprises a data buffer configured to buffer data to be send in case of a temporarily connection loss. By streaming the pre-processed data, the aggregated data, and/or the analytics data, the communication server allows to reduce the amount of streamed data compared to when raw data is streamed.

In a preferred embodiment, the plant server system further comprises a web server configured to consult the data through a web interface, wherein the web server comprises a local and/or a globally connected network of computers, such as an intranet and/or the internet, respectively. An advantage thereof is that users of the server system, e.g. operators of the plant, can connect locally, e.g. through their personal computer’s web browser, to connect to the server system. This allows operators to connect to the server system from anywhere in the world.

In a preferred embodiment, the data historian server is configured to visualize the data offshore, and/or the analytics server is configured to visualize an outcome of the analytics performed offshore. In this way, and operator of the plant can assess the performance of the hydrocarbon processing equipment or of the plant as a whole as well as processing structures or sub-components thereof and to take action if needed, for instance when the operator recognizes a need for calibration, repair or replacements of for instance the equipment or a sub-components of the process and structure of the plant. Preferably, the data historian server is configured to visualize the data in a real-time manner, and/or the analytics server is configured to perform the analytics and/or visualize the outcome in a real-time manner. This allows for accurate monitoring and quick assessment and/or decision-making in relation to the processing performance of the processing equipment.

In a preferred embodiment, the plant server system further comprises a second data interface server configured to collect the data and stream the data to the data historian server and whose architecture and functionality is identical to that of the first data interface server. This second data interface server can be used as a failover when the other data interface server is not available due to for instance malfunction or breakdown. In this way, data losses are minimized thanks to the offshore local data historian server and redundant data interface servers. Preferably, the first data interface server and/or the second data interface server are configured to consolidate, organize and contextualize the data.

In a preferred embodiment, the plant server system is configured to analyze data representing the process parameter of the hydrocarbon processing equipment so as to predict a behavior of the processing performance of the hydrocarbon processing equipment, wherein real-time data generated by the sensor representing the process parameter are compared to historical data representing the process parameter. Therefore, operational settings of the processing equipment or maintenance, repair or replacement activities can be scheduled and prepared well in advance of an eventual downtime-causing flaw in the production process or a malfunction or breakdown of the processing equipment, so that any downtime can be minimized.

In a preferred embodiment, the plant server system is configured to provide a performance parameter value being indicative of a production performance of the floating hydrocarbon production plant by being configured to: obtain plant data from the plant, wherein the plant data comprises data generated by the sensor; obtain a trained predictive model arranged for predicting or classifying the performance parameter value; and provide, on the basis of the trained predictive model and the plant data, the performance parameter value for the plant.

Using a predictive model, a performance parameter value being indicative of a production performance of the plant can be efficiently determined. It is hereby noted that the term predictive model may comprise a predictive model such as a regression model, but may also comprise a classification model or a clustering model.

Obtaining a reliable (i.e. reliably trained) predictive model is typically challenging. The plant server system of the current disclosure is however preferably part of a system of plant server systems implemented in a fleet of floating hydrocarbon production plants having a substantially similar configuration. Therefore, the plant server system is preferably configured to train a predictive model on the basis of data originating from one plant and to use this model in another plant. Additionally or alternatively, the model is used in the one plant from which the data originates. Preferably, the data originating from the one plant is combined with the data from the other plant in training the predictive model. Therefore, the plant server system is preferably configured to obtain the trained predictive model by being configured to: obtain plant training data from another floating hydrocarbon production plant, wherein the plant training data comprises data generated by a sensor for measuring a process parameter value of hydrocarbon processing equipment of the other plant; provide a predictive model; and train the predictive model using the plant training data for obtaining the trained predictive model.

In a preferred embodiment, the plant server system is further configured to: combine the plant training data with the plant data to obtain a set of combined plant data; and train the predictive model using the set of combined plant data for obtaining the trained predictive model.

An accurate predictive model is obtained when the model is trained using production performance data. Therefore, the plant server system is preferably configured to obtain production performance data, wherein the production performance data contains data representing a value of at least one operating parameter which is indicative of a production status of the other plant, and to train the predictive model on the basis of the production performance data. Based on the production performance data, the predictive model can be trained efficiently. Preferably, the production performance data is related to the performance parameter value to be determined. More preferably, the product performance data and the performance parameter value are related to the same production performance parameter.

It is also possible that an operator on the plant identifies an event, for instance during an inspection of the equipment or by inspecting the plant data. This may even be done remotely. An event may for instance include a malfunction or otherwise suboptimal working of equipment. Such an event may also be detected using an algorithm on the basis of plant data, for instance when a certain value reaches a predetermined threshold. An event indicator being indicative of such an event is then generated. This event indicator is preferably included as production performance data and as such included in the plant data, to be used as training data. Preferably, as with all data, the event indicator has an associated time stamp, allowing the event indicator to be correlated in time with other data for training purposes.

The occurrence or identification of an event is valuable information for training purposes of the predictive model. It is thus preferred that the predictive model is retrained based on the additionally available plant training data, including the event indicator. The plant server system is therefore preferably configured to:

- receive an event indicator associated with the other plant;

- in reaction to the received event indicator, obtain the plant training data from the other plant including the received event indicator as production performance data;

- repeat the step of training the predictive model using the plant training data including the received event indicator; and

- provide the trained predictive model to the plant.

In a preferred embodiment, the plant server system is further configured to:

- combine the plant training data with the plant data to obtain a set of combined plant data including the received event indicator; and

- repeat the step of training the predictive model using the set of combined plant data including the received event indicator. The occurrence or identification of an event in one plant may thus be used to improve the model to be used in another plant. As mentioned above, the model may also, or exclusively, be used in the plant wherein the event occurred. Retraining of the model after an event improves the predicting capability.

In a preferred embodiment, the plant server system is configured to factor out an external factor that influences a value of the data generated by the sensor of the floating hydrocarbon production plant representing the process parameter of the hydrocarbon processing equipment of the floating hydrocarbon production plant and/or the data generated by the sensor of the other floating hydrocarbon production plant representing the process parameter of the hydrocarbon processing equipment of the other floating hydrocarbon production plant, such that normalized data are obtained which are not associated with the external factor. In this way, the plant server system may run, build or train a predictive model on the basis of the data from multiple floating plants leading to the occurrence of a downtime -causing event. Therefore, since more data can be used for predicting the event, the predictive model becomes more accurate and therewith assessment and decisions as regards the operational performance of the plants can be made more reliably. Preferably, the external factor comprises at least one of the design of the floating hydrocarbon production plant and environmental influences, wherein the environmental influences comprise at least one of weather conditions and marine conditions.

In a preferred embodiment, the normalized data are provided as input to a predictive model configured to predict a future value of the data representing the process parameter, being indicative of a production performance of the floating hydrocarbon production plant. Preferably, the future value is compared to a predetermined critical value, wherein a performance maintaining operation or performance improving operation is carried out or planned, if the future value exceeds or falls below the predetermined critical value.

According to a second aspect, there is provided a system for operating a floating hydrocarbon production plant, comprising a floating hydrocarbon production plant according to any one of the above -described preferred embodiments, and in an onshore facility, an onshore server system comprising at least a data interface server configured to collect data generated by the plant server system.

Preferably, the onshore server system has a system architecture which is similar to the system architecture of the plant server system as defined in any one of the above-described preferred embodiments. The term similar is meant to comprise at least one of structurally identical, functionally identical, structurally similar and functionally similar. More preferably, the onshore server system has a system architecture which is the same as the system architecture of the plant server system as defined in any one of the above-described preferred embodiments.

The plant server system and the onshore server system are further configured to communicate with each other, preferably via a satellite. The central onshore server system receives the data from the floating hydrocarbon production plant and may perform analytics and build analytics models, which are then transferred to the floating plant for local use offshore. In this way, the offshore floating plant can be provided with intelligent analytics, so that the plant’s data can be analysed more thoroughly offshore, so that the operator on the plant is provided with more accurate and reliable information upon which he can assess the processing equipment’s processing performance and/or the production performance of the plant as a whole and take action, such as adjusting operational parameters of the processing equipment or planning maintenance, repair or replacement activities (predictive maintenance), well in advance of an otherwise unplanned event, which would cause serious downtime of the floating plant.

Preferably, a data storage capacity, a data processing power and/or a computing power of the onshore server system is larger than that of the plant server system of the floating hydrocarbon production plant. This allows for e.g. performing analytics on the basis of larger amounts of data, for carrying out more complex calculations, and for running and creating more complex models, such as predictive models.

In a preferred embodiment, the system further comprises another floating hydrocarbon production plant, comprising hydrocarbon processing equipment which is at least similar to the hydrocarbon processing equipment of the floating hydrocarbon production plant according to any one of the above -described preferred embodiments, a sensor for measuring a process parameter of the hydrocarbon processing equipment of the other floating hydrocarbon production plant, and a plant server system as defined in any one of the above -described preferred embodiments of the floating hydrocarbon production plants, which is configured to communicate with the onshore server system via a satellite. As an advantage, the central onshore server system may use data of the other floating hydrocarbon production plant, such that the occurrence of a downtime -causing event in one of the floating plants can be used to predict the occurrence of such an event in the other one of the floating plants. Specifically, the onshore server system may run, build or train a predictive model (also known as Artificial Intelligence Agent or AI Agent) on the basis of the data from one of the floating plants leading to the downtime-causing event. This predictive model is then used on the other floating plant for predicting this event on the basis of real-time data measurements on the other floating plant. Specific predictive models may the trained for specific subsystems of the floating plants. The onshore server system may receive the data the two floating plants and trains one or more predictive models, which, when trained, is/are subsequently transferred to the floating plants, in particular to the respective analytics servers, for local use offshore.

Preferably, the system architecture of the plant server system of the other floating hydrocarbon production plant is similar to the system architecture of the onshore server system and the system architecture of the plant server system of the plant. The term similar is meant to comprise at least one of structurally identical, functionally identical, structurally similar and functionally similar. More preferably, the system architecture of the plant server system of the other floating hydrocarbon production plant is the same as the system architecture of the onshore server system and the system architecture of the plant server system of the plant.

Preferably, the onshore server system is configured to analyze data representing the process parameter of the hydrocarbon processing equipment of the other floating hydrocarbon production plant so as to predict a behavior of the processing performance of the hydrocarbon processing equipment of the floating hydrocarbon production plant, wherein real-time data generated by the sensor of the floating hydrocarbon production plant representing the process parameter of the hydrocarbon processing equipment of the floating hydrocarbon production plant are compared to historical data generated by the sensor of the other floating hydrocarbon production plant representing the process parameter of the hydrocarbon processing equipment of the other floating hydrocarbon production plant.

In a preferred embodiment, the onshore server system is configured to provide a performance parameter value being indicative of a production performance of the floating hydrocarbon production plant by being configured to: obtain plant data from the plant, wherein the plant data comprises data generated by the sensor; obtain a trained predictive model arranged for predicting or classifying the performance parameter value; and provide, on the basis of the trained predictive model and the plant data, the performance parameter value for the plant.

As discussed above, obtaining a reliable (i.e. reliably trained) predictive model is typically challenging. The onshore server system of the current disclosure is however preferably part of a system comprising the onshore server system and a plurality of plant server systems implemented in a fleet of floating hydrocarbon production plants having a substantially similar configuration. Therefore, the onshore server system is preferably configured to train a predictive model on the basis of data originating from one plant and to use this model in another plant. Preferably, the data originating from the one plant is combined with the data from the other plant in training the predictive model. Therefore, the onshore server system is preferably configured to obtain the trained predictive model by being configured to: obtain plant training data from the other floating hydrocarbon production plant, wherein the plant training data comprises data generated by a sensor for measuring a process parameter value of hydrocarbon processing equipment of the other plant; provide a predictive model; and train the predictive model using the plant training data for obtaining the trained predictive model.

In a preferred embodiment, the onshore server system is further configured to: combine the plant training data with the plant data to obtain a set of combined plant data; and train the predictive model using the set of combined plant data for obtaining the trained predictive model.

As discussed above, an accurate predictive model is obtained when the model is trained using production performance data. Therefore, the onshore server system is preferably configured to obtain production performance data, wherein the production performance data contains data representing a value of at least one operating parameter which is indicative of a production status of the other plant, and to train the predictive model on the basis of the production performance data. Based on the production performance data, the predictive model can be trained efficiently. Preferably, the production performance data is related to the performance parameter value to be determined. More preferably, the product performance data and the performance parameter value are related to the same production performance parameter.

The occurrence or identification of an event is valuable information for training purposes of the predictive model. It is thus preferred that the predictive model is retrained based on the additionally available plant training data, including the event indicator. The onshore server system is therefore preferably configured to:

- receive an event indicator associated with the other plant;

- repeat the step of training the predictive model using plant training data including the received event indicator; and

- provide the trained predictive model to the plant.

In a preferred embodiment, the onshore server system is further configured to:

- repeat the step of training the predictive model using the set of combined plant data including the received event indicator.

The occurrence or identification of an event in one plant may thus be used to improve the model to be used in another and/or the same plant.

In a preferred embodiment, the other plant comprises a plurality of plants. Accordingly, the step of obtaining plant training data comprises obtaining plant data from a plurality of plants, wherein the step of training comprises training the predictive model on the basis of the plant training data obtained from the plurality of plants. Increasing the amount of relevant data available for training improves the quality of the predictive model. In other words, training on the basis of more data obtains a more reliable model. Preferably, the system architecture of the plant server systems of each of the plurality of plants is similar, more preferably the same as the system architecture of the onshore server system and the system architecture of the plant server system of the plant. The term similar is meant to comprise at least one of structurally identical, functionally identical, structurally similar and functionally similar. The similarity, preferably sameness, of the system architectures allows to build and/or train the predictive at one plant or at the onshore facility and to deploy the built and/or trainen model at one of the other plants, and vice versa. In other words, the fact that the server system architectures of the onshore server system and each of plant server systems are similar, preferably identical, allows for applying powerful analytical results of complex predictive models built and/or trained in the onshore facility (or in one of the plants) in the analytics to be carried out by the analytics servers of the plant server systems of each of the plurality of plants.

In a preferred embodiment, the onshore server system is configured to factor out an external factor that influences a value of the data representing a parameter related to the processing performance of the hydrocarbon processing equipment of the floating hydrocarbon production plant and/or the data representing the parameter related to processing performance of the hydrocarbon processing equipment of the other floating hydrocarbon production plant, such that normalized data (i.e. calibrated data) are obtained which are not associated with the external factor. In this way, the onshore server system may run, build or train a predictive model on the basis of the data from multiple floating plants leading to the occurrence of a downtime-causing event. Therefore, since more data can be used for predicting the event, the predictive model becomes more accurate and therewith assessment and decisions as regards the operational performance of the plants can be made more reliably. Preferably, the external factor comprises at least one of the design of the floating hydrocarbon production plant and environmental influences, wherein the environmental influences comprise at least one of weather conditions and marine conditions.

In a preferred embodiment, the floating hydrocarbon production plant and the other floating hydrocarbon production plant, at least hydrocarbon processing equipment subsystems of the plant and the other plant are preferably substantially the same, at least to such an extent that the respective data obtained from said subsystems are comparable for analysis, whether or not after normalization of the data, i.e. after having factored out external factors that influence the data values by the plant server system or onshore server system. Even data from a non -similar subsystem of the other plant may preferably be used as a basis for providing the predictive model for a subsystem of the plant, or vice versa. A plant may contain several substantially the same subsystems. Preferably, data of more than one subsystem may be combined to improve the training of the model. In a preferred embodiment, the predictive model as described above in relation to both the plant server system and the onshore server system comprises at least one of a neural network, a random forest, a k-nearest neighbor classifier, a logistic regression model, a k-means clustering model, a principal component analysis or a support vector machine. It is noted that the term predictive model may also comprise a classification model or a clustering model. The predictive model may be combined with any suitable distance or (dis)similarity measure, such as Euclidean distance, Minkowski distance, Jaccard dissimilarity measure, dynamic time warping, etc.

It will be appreciated by a person skilled in the art of predictive modelling that selecting the type of predictive model and, if applicable, the corresponding distance or (dis)similarity measure is based on the type of data that is used with the model and the type of predictive parameter the model is intended to find. For example, when the predictive parameter is indicative for a certain type of behavior of the hydrocarbon processing equipment, a neural network or a k-nearest neighbor classifier might be selected. On the other hand, when the predictive parameter is, for example, indicative for an abnormal behavior of the hydrocarbon processing equipment, a clustering model such as k-means might be selected. When the predictive parameter is indicative for future behavior of the hydrocarbon processing equipment or indicative for future failure of the equipment, a neural network or a logistic regression model may be selected. It is noted that the examples given above are not exhaustive and many other selections are possible.

The selection of the predictive model may further comprise selecting predetermined hyper parameters, wherein the predetermined hyper parameters comprise the parameters defining the settings of the predictive model. It will be appreciated by a person skilled in the art of predictive modelling that the hyper parameters will be dependent on the type of predictive model, the type of data, the type of the predictive parameter the model is intended to find, and other conditions. Examples of hyper parameters are a number of neighbors (k) in a k-nearest neighbor classifier, a number of layers and nodes/hidden units in a neural network (and the connection between the nodes), a number of support vectors in a support vector machine, a number of clusters in a k- means clustering model, etc. Other examples of hyper parameters are a learning rate, a training batch size, and a number of training epochs.

In a preferred embodiment, the hydrocarbon processing equipment comprises at least one of: dynamic equipment, comprising at least one of a compressor, a pump and a gas turbine; and static equipment, comprising at least one of a pipe, a floating hydrocarbon production plant hull, a pressure vessel, a separator and a swivel. In a preferred embodiment, at least one of the floating hydrocarbon production plant and/or the other floating hydrocarbon production plant are floating production, storage and offloading facilities (“FPSOs”).

Further advantages, features and details of the floating hydrocarbon production plant and system are elucidated on the basis of the following description of preferred embodiments thereof with reference to the accompanying drawings, in which: figure 1 shows a top view of a preferred embodiment of the floating hydrocarbon production plant; figure 2 shows a close-up view of a preferred embodiment of the plant server system of the plant of figure 1 ; figure 3 shows a schematic view of a preferred embodiment of the system; figure 4 shows a schematic view of a processing structure.

Figure 1 shows a schematic top view of a floating hydrocarbon production plant or FPSO 100, comprising several processing structures 101 to 116 which consist of several sub-components, also referred to as processing components, which are pieces of hydrocarbon processing equipment. A plurality of sensors lOla-d and possibly at least one actuator 101k (see also figure 4) is provided in association with the processing structure 101 or sub-components, i.e. hydrocarbon processing equipment. Similarly, sensors 101 a-d to 116 a-d are arranged to measure parameters relating to the performance of the sensor’s respective structure/sub-components/equipment piece 101 to 116.

As examples of processing structures, the floating hydrocarbon production plant comprises a filter structure 101 (see also figure 4), a sea water treatment structure 102, multiple oil processing structures 103, multiple gas treatment structures 104, an injection gas compression structure 105, a flare knockout drum structure 106, a vent stack structure 107, flare stack structure 108, a main gas compression structure 109, a H₂S removal structure 110, a C0₂ membrane structure 111, a C0₂ gas compression structure 112, a main gas compression structure 113, a laydown area/chemical injection structure 114, multiple power generation structures 115, and an oil metering offloading skid structure 116. Each of these structures 101 to 116 are associated with sensors lOla-d to 116a- d which measure parameters relating to the performance of these structures and/or the performance of their sub-components, which may be pieces of hydrocarbon processing equipment.

For instance, a sensor 101a associated with a filter structure 101 measures a pressure and temperature of the inflow, while a sensor lOld may measure the outflow. Additionally, a sensor associated with an H₂S scavenger vessel of the H₂S removal structure 110 measures an H₂S scavenger vessel’ s performance parameter, such as an H₂S concentration after H₂S treatment by the scavenger.

The measurements of these sensors result in data which can be analyzed in order to assess the production performance of the plant 100, processing structure 101 to 116 or sub-component. Thereto, as shown in Figure 2, the floating hydrocarbon production plant 100 comprises a plant server system 120, substantially comprising four servers, viz. a data interface server 130, a data historian server 140, an analytics server 150 and a web server 160. Specifically, the data is collected by the data interface server 130 which is configured to collect the data and stream the data to the data historian server 140. Although not shown in the figures, the server system 120 ideally comprises a second data interface server which is used as a failover when the other data interface server 130 is not available due to for instance malfunction or breakdown.

The data historian server 140 allows recording and retrieving production and process data by time. It stores the information in a time-series database that can efficiently store data with minimal disk space and fast retrieval. Such time-series information is displayed in a trend or as tabular data over a time range, for example over the last day, the last week or the last year. It records data over time from one or more locations for an operator 170 to analyze. Analog readings such as temperature, pressure, flow rate or rotational speed as well as digital readings, such as discrete positions of valves and limit switches and outputs of discrete level sensors, can be recorded in the data historian server 140. Moreover, non-time-series information can be integrated in the data historian server 140 to provide greater context, e.g. processing equipment info, such as equipment IDs and material IDs, quality info, such as process and equipment limits, and aggregate data such as average, standard deviation and moving average. Other data related to the production performance of the plant 100 can be extracted from other integrated database sources and integrated/stored into the date historian server 140.

One of the important advantages of the data historian server 140 is its ability to correlate data over time, which can include for instance day shifts versus night shifts, one operator group versus another, one continuous production run versus another and/or one season versus another. The data historian server 140 can be applied independently on one or more FPSOs 100. Importantly, the data historian server 140 is especially valuable when applied across multiple FPSOs 100 within a fleet 10 of FPSOs and/or one or more FPSOs within the fleet 10 and an onshore facility 500. Using the data historian server 140 allows for instance to discover a production problem’s root cause and/or to discover that two similar pieces of equipment or two similar floating hydrocarbon production plants 100, processing structures 101 to 116 or sub-components thereof produce significantly different results over time. Such information can be used for instance to detect a problem with the pieces of equipment, floating plants, processing structures or sub-components thereof as well as to factor out or normalize factors which are specific and unique for each of the individual equipment pieces, plants, processing structures or sub-components thereof, such that normalized data (or calibrated data) can be compared one to one and a in order to detect anomalies in the data trends.

Additionally, the data historian server 140 allows for visualization of the data, such that an operator 170 can assess the performance of equipment or the plant 100 as a whole as well as processing structures 101 to 116 or sub-components thereof and to take action if needed, for instance when the operator 170 recognizes the need for calibration, repair or replacement of for instance the equipment or a sub-component of the processing structure 101 to 116 of the plant 100. Specifically, the data historian server 140 allows to monitor and instruments to keep it operational and, as discussed, to recognize the need for calibration, repair, replacement. Moreover, it enables to monitor process, a set of sub-components of a process and structure or a set of pieces of equipment in a processing structure/unit 101 to 116, to get it to operate within a set of process specifications. Moreover, the data historian server 140 allows to monitor production within a processing unit to maintain product quality within the process capability, to monitor the entire production sequence for maximum operational efficiency and flow, to monitor aspects of an FPSO 100 to optimize resource demand and consumption, to monitor multiple FPSOs 100 to strive for consistency and continuous improvement, to allow operational real-time data to integrate with business and financial systems, and/or to provide input for design experiments, prototype production systems, and continuous improvement projects.

In short, the data historian server 140 of the plant server system of the floating hydrocarbon production plant 100 provides operational data that is well organized and easily accessible, which enables an operator 170 to make informed and fast decisions to improve productivity, quality and efficiency of the FPSO 100 or fleet 10 of FPSOs 100, in particular to maximize production uptime, to optimize production efficiency, i.e. to optimize production performance of the FPSO or FPSOs 100, to maximize the FPSO’s or FPSOs’ operational lifetime, while at the same time preventing the stringent operational safety requirements from being compromised. Thus, the data historian server 140 allows to convert the data generated by each of the plurality of sensors 101 a-d to 116a-d into actionable information upon which an operator 170 of the plant 100 can make decisions in order to optimize production in terms of uptime maximization, operational efficiency optimization, while extending or maintaining processing equipment lifetime and not compromising the safety conditions on the plant 100.

Specifically, an analytics server 150 is provided to carry out’s descriptive analytics, i.e. describing the data in a way that it provides actionable information to the operator 170 of the plant 100. Moreover, the analytics server 150 enables to perform diagnostic analytics, i.e. to carry out analysis on the data, to find a trend in the data over time or to detect anomalies in the data, so that the operator 170 is assisted or instructed in its decision-making process as to operating the processing equipment, which may include adjusting equipment settings, scheduling maintenance of equipment, or shutting down equipment. Moreover, the analytics server 150 enables to create and run models on the basis of which the analysis of the data is performed, wherein results of the model-based analysis are used for assisting the operator 170 in its decision-making process. Such models may be either formula-based or data-driven, or may be a combination of both. Moreover, the analytics server 150 enables to perform predictive analytics, i.e. to run, create and/or train a predictive model which allows to carry out a predictive analysis on the data, i.e. to analyze current and historical data and/or facts on the basis of which a prediction as regards to future processing performance behavior of the processing equipment can be made which can be used to avoid any unplanned incidents which would cause unscheduled downtime.

Predictive analytics thus utilizes techniques such as machine learning and data mining to predict how the processing equipment’s performance might behave in the future. Importantly the analytics server 150 is configured to carry out prescriptive analytics, i.e. to offer a recommendation to the operator 170 of the floating hydrocarbon production plant 100 on the basis of a predicted outcome. It recommends the operator 170 actions based on historical data, external data sources and/or machine learning algorithms. The analytics server 150 allows optimizing process settings of each of the processing structures 101 to 116 and/or it sub-components, such as processing rates of each of the structures and/or sub-components, so that an average processing performance can be optimized over the lifetime of the FPSO 100.

Moreover, data of each of the individual sensors of equipment different individual structures 101 to 116, specifically of a complex set of structures, can be combined in order to gain a deeper insight into the processing performance of the plant 100, in particular into the influence of the processing performance of a first processing structure onto another processing structure associated with the first processing structure, so that process settings, such as processing rates of each of the processing structures, can be optimized. The plant server system 120 consisting of the data interface server 130, the data historian server 140, and the analytics server 150 on the floating hydrocarbon production plant 100 enables the operator 170 to access data relating to the production performance of the plant 100, to monitor different processes and to analyze and predict, using predictive models, the production performance behavior of the plant 100, so that the plant 100 can be operated optimally when the plant 100 is located offshore. This allows for running and optimizing the plant’s production performance even when a connection via a satellite 600 with an onshore facility 500 is off-line.

A web server 160 is provided to which users of the server system 120, e.g. operators 170 of the plant, can connect locally, e.g. through their personal computer’s web browser, to connect to the server system 120. This allows operators 170 to connect to the server system 120 from anywhere in the world.

Figure 3 shows a system 1 comprising a fleet 10 of four floating hydrocarbon production plants 100; 200; 300; 400, each comprising a plant server system 120; 220; 320; 420, comprising a data interface server 130; 230; 330; 430, a data historian server 140; 240; 340; 440, an analytics server 150; 250; 350; 450 and a web server 160; 260; 360; 460, and an onshore facility 500, which comprises an onshore server system 520, comprising a data interface server 530, a data historian server 540, an analytics server 550 and a web server 560, wherein the server systems 120; 220; 320; 420 of each of the floating hydrocarbon production plants 100; 200; 300; 400 are configured to collect, store and analyze data generated by the plurality of sensors lOla-d to 416a-d of each respective one of the fleet 10 of four plants 100; 200; 300; 400, and wherein the servers of the onshore facility 500 are configured to collect, store and analyze the data generated by the plurality of sensors of all four floating hydrocarbon production plants 100; 200; 300; 400. Data communication between the onshore server system 520 and each of the plant server systems 120; 220; 320; 420 of each of the floating plants 100; 200; 300; 400 occurs through a satellite 600.

The central onshore server system 520 receives the data from the floating hydrocarbon production plants 100; 200; 300; 400 and allows performing analytics and building analytics models, which are then transferred to the floating plant, in particular the analytic servers thereof, for local use offshore. In this way, the offshore floating plants 100; 200; 300; 400 can be provided with intelligent analytics, so that each plant’s data can be analysed more thoroughly offshore, so that the operator 170; 270; 370; 470 is provided with more accurate and reliable information upon which he can assess the processing equipment’s processing performance and/or the production performance of the plant 100; 200; 300; 400 as a whole and take action, such as adjusting operational parameters of the processing equipment or planning maintenance, repair or replacement activities (predictive maintenance), well in advance of an otherwise unplanned event, which would cause serious downtime of the floating plant.

The onshore server system 520 of the onshore facility 500 has a data storage capacity, a data processing power and or a computing power which is larger than that of the plant server systems 120; 220; 320; 420 of each of the floating hydrocarbon production plants 100; 200; 300; 400. Specifically, due to this higher data storage capacity, the data historian server 540 of the onshore server system 520 is configured to store more data than each of the data historian servers 140; 240; 340; 440 of the plant server systems 120; 220; 320; 420 of each of the floating hydrocarbon production plants 100; 200; 300; 400. Due to its higher data processing power and computing power, the analytics server 550 of the onshore server system 520 enables processing more data and performing more complex analytics on the data than each of the analytics servers 150; 250; 350; 450 of each respective plants 100; 200; 300; 400 do.

The architecture of the onshore server system 520 of the onshore facility 500 is similar to the architecture of the plant server systems 120; 220; 320; 420. This enables to deploy the analytical results of the predictive models built using the analytics server 550 of the onshore server system 520 of the onshore facility 500 into the decision-making process on the floating hydrocarbon production plants 100; 200; 300; 400 offshore to get results, reports and outputs by automating the decisions based on the modeling. In other words, the fact that the server system architectures of the onshore server system 520 and each of plant server systems 120; 220; 320; 420 are identical allows for applying powerful analytical results of complex predictive models built in the onshore facility 500 in the analytics to be carried out by the analytics servers 150; 250; 350; 450 of each of the plant server systems 120; 220; 320; 420.

The combination of the fact that each plant server system 120; 220; 320; 420 has its own data historian server 140; 240; 340; 440 and analytics server 150; 250; 350; 450 and the fact that the higher capacity and more powerful onshore server system 520 and the plant server systems 120; 220; 320; 420 have the same architecture thus allows access the data, to analyze the data and to predict a future behavior of the production performance of each respective floating hydrocarbon production plant 100; 200; 300; 400, when each respective plant 100; 200; 300; 400 is located offshore and when a connection through the satellite 600 between the plants 100; 200; 300; 400 and the onshore facility 500 is absent.

Moreover, the fleet 10 as shown in figure 3 allows for using data of different similar floating hydrocarbon production plants 100; 200; 300; 400 in building the analytics models using the onshore server system 520. This allow for faster and more accurate model building and thus more reliable analytical results which can be incorporated into the everyday decision-making on the different production plants 100; 200; 300; 400 of the fleet 10. Specifically, external factors relating to the specific differences between the plants 100; 200; 300; 400 within the fleet 10, such as differences in the design of the processing structures 101 to 116 as well as environmental differences, such as marine and/or weather conditions, can be factored out or normalized. Such normalized data are eminently suitable for fast building of accurate and thus reliable analytics models for predicting a future behavior of the production performance of a specific plant 100; 200; 300; 400.

The present invention thereby allows to improve performance by collecting and analyzing data, to reduce maintenance costs by doing maintenance only when required and identifying and solving quickly issues and downtime by monitoring the performance of the floating hydrocarbon production plant 100 locally and remotely, i.e. offshore and onshore, and by operating the plant 100 on the basis of reliable predictions using predictive modeling, thereby avoiding unplanned shutdown and trips.

A person of skill in the art would readily recognize that functions of various above-described systems, in particular servers and server systems, can be performed by programmed computers. Herein, some embodiments are also intended to cover program storage devices, e.g., digital data storage media, which are machine or computer readable and encode machine-executable or computer-executable programs of instructions, wherein the instructions perform some or all of the steps of the above -described methods. The program storage devices may be, e.g., digital memories, magnetic storage media such as a magnetic disks and magnetic tapes, hard drives, or optically readable digital data storage media. The embodiments are also intended to cover computers programmed to perform the steps of the above -described methods.

The functions of the various elements shown in the figures, including any functional blocks labelled as “server systems”, “servers”, “units”, “processors” or “modules”, may be provided through the use of dedicated hardware as well as hardware capable of executing software such as firmware in association with appropriate software. When provided by a processor, the functions may be provided by a single dedicated processor, by a single shared processor, or by a plurality of individual processors, some of which may be shared. Moreover, explicit use of the term “server system”, “server”, “unit”, “processor” or “controller” should not be construed to refer exclusively to hardware capable of executing software, and may implicitly include, without limitation, digital signal processor (DSP) hardware, network processor, application specific integrated circuit (ASIC), field programmable gate array (FPGA), read only memory (ROM) for storing software, random access memory (RAM), and non volatile storage. Other hardware, conventional and/or custom, may also be included. Similarly, any switches shown in the FIGS are conceptual only. Their function may be carried out through the operation of program logic, through dedicated logic, through the interaction of program control and dedicated logic, or even manually, the particular technique being selectable by the implementer as more specifically understood from the context.

It should be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative circuitry embodying the principles of the invention. Similarly, it will be appreciated that any flow charts, flow diagrams, state transition diagrams, pseudo code, and the like represent various processes which may be substantially represented in computer readable medium and so executed by a computer or processor, whether or not such computer or processor is explicitly shown. Specifically, the terms “server system” and “server” used in the description above, may refer to a service and/or functionality, i.e. a process, rather than a physical server system or server, notwithstanding the fact that the process may be run on a dedicated server system or server which thereby provides the service and/or functionality. The present invention is not limited to the above described preferred embodiment; the rights are defined by the claims, within the scope of which many modifications can be envisaged.

Claims

1. Floating hydrocarbon production plant (100), comprising: hydrocarbon processing equipment (101 to 116); a sensor (lOla-d to 116a-d) for measuring a value of a process parameter of the hydrocarbon processing equipment; and a plant server system (120) for operating the floating hydrocarbon production plant offshore, comprising: a first data interface server (130) configured to collect data generated by the sensor and to stream the data to a data historian server (140); the data historian server configured to store the data, to provide access to the data to an operator (170) of the plant, and to stream the data to an analytics server (150); and the analytics server configured to structure the data thereon and to perform analytics on the data, wherein the plant server system is configured to convert the data into actionable information about the process parameter on the basis of which the operator is enabled to assess and/or to control a processing performance of the hydrocarbon processing equipment.

2. Floating hydrocarbon production plant according to claim 1 , wherein the plant server system further comprises a web server (160) configured to consult the data through a web interface, wherein the web server comprises a local and/or a globally connected network of computers.

3. Floating hydrocarbon production plant according to claim 1 or 2, wherein the data historian server is configured to visualize the data offshore; and/or the analytics server is configured to visualize an outcome of the analytics performed offshore.

4. Floating hydrocarbon production plant according to claim 3, wherein the data historian server is configured to visualize the data in a real-time manner; and/or the analytics server is configured to perform the analytics and/or visualize the outcome in a real-time manner.

5. Floating hydrocarbon production plant according to any one of claims 1 to 4, wherein the plant server system further comprises a second data interface server configured to collect the data and stream the data to the data historian server and whose architecture and functionality is identical to that of the first data interface server.

6. Floating hydrocarbon production plant according to any one of claims 1 to 5, wherein the first data interface server and/or the second data interface server are configured to consolidate, organize and contextualize the data.

7. Floating hydrocarbon production plant according to any one of claims 1 to 6, wherein the plant server system is configured to analyze data representing the process parameter of the hydrocarbon processing equipment so as to predict a behavior of the processing performance of the hydrocarbon processing equipment, wherein real-time data generated by the sensor representing the process parameter are compared to historical data representing the process parameter.

8. Floating hydrocarbon production plant according to claim 7, wherein the plant server system is configured to provide a performance parameter value being indicative of a production performance of the floating hydrocarbon production plant by being configured to: obtain plant data from the plant, wherein the plant data comprises data generated by the sensor; obtain a trained predictive model arranged for predicting or classifying the performance parameter value; and provide, on the basis of the trained predictive model and the plant data, the performance parameter value for the plant.

9. Floating hydrocarbon production plant according to claim 8, wherein the plant server system is configure to obtain the trained predictive model by being configured to: obtain plant training data from another floating hydrocarbon production plant, wherein the plant training data comprises data generated by a sensor for measuring a process parameter value of hydrocarbon processing equipment of the other plant; provide a predictive model; and train the predictive model using the plant training data for obtaining the trained predictive model.

10. Floating hydrocarbon production plant according to claim 9, wherein the plant server system is further configured to: combine the plant training data with the plant data to obtain a set of combined plant data; and train the predictive model using the set of combined plant data for obtaining the trained predictive model.

11. Floating hydrocarbon production plant according to any of claims 7 to 10, wherein the plant server system is configured to factor out an external factor that influences a value of the data generated by the sensor of the floating hydrocarbon production plant representing the process parameter of the hydrocarbon processing equipment of the floating hydrocarbon production plant (100) and/or the data generated by the sensor of the other floating hydrocarbon production plant representing the process parameter of the hydrocarbon processing equipment of the other floating hydrocarbon production plant (200; 300; 400), such that normalized data are obtained which are not associated with the external factor.

12. System (1) for operating an floating hydrocarbon production plant (100), comprising a floating hydrocarbon production plant according to any one of claims 1 to 7; and in an onshore facility (500), an onshore server system (520), comprising at least a data interface server configured to collect data generated by the plant server system.

13. System according to claim 12, wherein the onshore server system has a system architecture which is similar to a system architecture of the plant server system as defined in any one of claims 1 to 7, wherein the plant server system (120) and the onshore server system are further configured to communicate with each other via a satellite (600).

14. System according to claim 12 or 13, wherein a data storage capacity, a data processing power and/or a computing power of the onshore server system is larger than that of the plant server system of the floating hydrocarbon production plant.

15. System according to any one of claims 12 to 14, further comprising another floating hydrocarbon production plant (200; 300; 400), comprising hydrocarbon processing equipment which is at least similar to the hydrocarbon processing equipment (101 to 116) of the floating hydrocarbon production plant (100) according to any one of claims 1 to 7; a sensor (lOla-d to 116a-d) for measuring a process parameter of the hydrocarbon processing equipment of the other floating hydrocarbon production plant; and a plant server system (220; 320; 420) as defined in any one of claims 1 to 9, which is configured to communicate with the onshore server system via a satellite.

16. System according to claim 15, wherein the onshore server system is configured to analyze data representing the process parameter of the hydrocarbon processing equipment of the other floating hydrocarbon production plant (200; 300; 400) so as to predict a behavior of the processing performance of the hydrocarbon processing equipment of the floating hydrocarbon production plant (100), wherein real-time data generated by the sensor of the floating hydrocarbon production plant representing the process parameter of the hydrocarbon processing equipment of the floating hydrocarbon production plant (100) are compared to historical data generated by the sensor of the other floating hydrocarbon production plant representing the process parameter of the hydrocarbon processing equipment of the other floating hydrocarbon production plant (200; 300; 400).

17. System according to claim 16, wherein the onshore server system is configured to provide a performance parameter value being indicative of a production performance of the floating hydrocarbon production plant by being configured to: obtain plant data from the plant, wherein the plant data comprises data generated by the sensor; obtain a trained predictive model arranged for predicting or classifying the performance parameter value; and provide, on the basis of the trained predictive model and the plant data, the performance parameter value for the plant.

18. System according to claim 17, wherein the onshore server system is configured to obtain the trained predictive model by being configured to: obtain plant training data from the other floating hydrocarbon production plant, wherein the plant training data comprises data generated by a sensor for measuring a process parameter value of hydrocarbon processing equipment of the other plant; provide a predictive model; and train the predictive model using the plant training data for obtaining the trained predictive model.

19. System according to claim 18, wherein the onshore server system is further configured to: combine the plant training data with the plant data to obtain a set of combined plant data; and train the predictive model using the set of combined plant data for obtaining the trained predictive model.

20. System according to any one of claims claim 16 to 19, wherein the onshore server system is configured to factor out an external factor that influences a value of the data generated by the sensor of the floating hydrocarbon production plant representing the process parameter of the hydrocarbon processing equipment of the floating hydrocarbon production plant (100) and/or the data generated by the sensor of the other floating hydrocarbon production plant representing the process parameter of the hydrocarbon processing equipment of the other floating hydrocarbon production plant (200; 300; 400), such that normalized data are obtained which are not associated with the external factor.

21. System according to claim 20, wherein the external factor comprises at least one of the design of the floating hydrocarbon production plant and environmental influences, wherein the environmental influences comprise at least one of weather conditions and marine conditions.

22. System according to claim 20 or 21, wherein the normalized data are provided as input to the predictive model configured to predict a future value of the data representing the process parameter, being indicative of the production performance of the floating hydrocarbon production plant.

23. System according to claim 22, wherein the future value is compared to a predetermined critical value, wherein a performance maintaining operation or performance improving operation is carried out or planned, if the future value exceeds or falls below the predetermined critical value.

24. System according to any one of claims 17 to 23, wherein the predictive model comprises at least one of a neural network, a random forest, a k- nearest neighbor classifier, a logistic regression model, a k-means clustering model, a principal component analysis or a support vector machine.

25. System according to any one of the preceding claims 12 to 24, wherein the hydrocarbon processing equipment comprises at least one of: dynamic equipment, comprising at least one of a compressor, a pump and a gas turbine; and static equipment, comprising at least one of a pipe, a floating hydrocarbon production plant hull, a pressure vessel, a separator and a swivel.

26. System according to any one of the preceding claims 12 to 25, wherein at least one of the floating hydrocarbon production plant and/or the other floating hydrocarbon production plant are floating production, storage and offloading facilities (“FPSOs”).