WO2010070279A1

WO2010070279A1 - Fluid transmission control system and method

Info

Publication number: WO2010070279A1
Application number: PCT/GB2009/002888
Authority: WO
Inventors: Richard Greig Clark; Christopher Harding; Gareth Andrew Jones; Lawrence Lambon; Alistair Porter; Mathew James Sims; Alan Shore
Original assignee: Bp Exploration Operating Company Limited
Priority date: 2008-12-18
Filing date: 2009-12-16
Publication date: 2010-06-24
Also published as: GB0823170D0; EP2359207A1

Abstract

Embodiments of the invention are concerned with a fluid transmission control system arranged to control the transmission of fluid through a fluid transmission network. The fluid transmission network comprises a plurality of fluid sources, a fluid processing plant, and a plurality of upstream pipelines located upstream of the fluid processing plant, one or more of the upstream pipelines having a first end connectable to a fluid source and one or more of the upstream pipelines having a second end connectable to an input of the fluid processing plant. The fluid transmission control system comprises measurement means for measuring one or more characteristics of fluid output from one or more of the plurality of fluid sources, prediction means for predicting, on the basis of the one or more measured characteristics, characteristics of the fluid at the second ends of the plurality of upstream pipelines, and processing determining means arranged to receive data regarding predicted characteristics of the fluid at the second ends of said plurality of upstream pipelines and to determine whether the fluid is suitable for processing by the fluid processing plant. A corresponding fluid transmission control method is also provided. By analysing the nature of the fluid before it is supplied to a fluid processing plant, a determination can be made as to whether or not the fluid processing should be carried out on the fluid.

Description

FLUID TRANSMISSION CONTROL SYSTEM AND METHOD

Field of the Invention

The present invention relates to a fluid transmission control system and method for controlling the transmission of fluid through a fluid transmission network. Background of the Invention

Natural gas, for use as a fuel to provide power to industrial and domestic users via grid networks, turbines, etc., can be obtained from natural gas sources around the world, and can take the form of "associated" gas or "non-associated" gas. By "associated gas" is meant gas that originates from crude oil fields and is separated from produced oil and water at a production facility. In contrast, "non-associated" gas originates at a gas field, which has little or no crude oil but may have some gas condensate (a low density liquid hydrocarbon having hydrocarbon components of a relatively heavy molecular weight, such as propanes and butanes). Raw associated or non-associated gas must be processed to meet a certain specification (required characteristics) before it can be supplied to users as a fuel; this will depend on factors such as the intended use of the gas and the end user's machinery design.

In many processes dependent on natural gas, gas characteristics, such as inherent properties (e.g. gas composition) and supply parameters (e.g. gas flow rate, temperature, etc.) of a supplied fuel such as natural gas, affect the operation or outcome of the process. As demand increases and the components, construction and efficiency of the machinery and systems used by downstream or end users improve, the specifications of the required gas become more precise, narrowing the range of acceptable gas characteristics and providing a greater variety of characteristic values which must be met for supplies to different users from a certain gas source.

In addition to methane (CH₄), "raw" natural gas comprises relatively "heavy" gaseous hydrocarbons such as ethane (C₂H₆), propane (C₃H₈), butanes (C₄H₁₀), pentanes (C₅H₁₂) and higher molecular weight hydrocarbons; these heavier gaseous hydrocarbons are collectively known as natural gas liquids (NGL). Raw natural gas must be processed before it is supplied to end users, in order to extract these NGL and other unwanted components of the raw gas, such as acid gases, water, liquid hydrocarbons such as natural gas condensate, crude oil and mercury. The specification of a gas may depend on its calorific value or the rate of change of the calorific value (which is preferably low, providing an approximately constant calorific value). This calorific value is linked to the "Wobbe Index". The Wobbe Index is an indicator of the interchangeability of fuel gases and is used to compare the combustion energy output of different composition fuel gases in an appliance burning such gases. The Wobbe Index is frequently defined as a requirement specification of gas supplies and transport utilities.

For example, users may require a rate of change of calorific value of less than 1%, or a calorific value within a range of ±5% of a target value. Outside these parameters, factors such as the operational efficiency of the equipment, safety and cost may be affected.

Additionally, when gas from a plurality of sources is commingled or processed consecutively, gas composition can vary over time. Operating machinery receiving the gas typically has a limited range within which it can work; that is to say that the tolerance to variations in gas composition can be low. For machinery that is capable of making adjustments to accommodate variations in gas, efficiency is inevitably lost and/or machinery can be damaged by the variation in gas characteristics.

The composition of the gas (that is, the relative amounts of the components of the gas) reaching machinery employed by downstream users is therefore highly important, as the gas energy or calorific value of the gas is dependent upon this composition. In the case where one or more fuel supplies originating from one or more sources are available, these may be used in combination with one another in order to formulate a required gas composition for end users. Additionally or alternatively, the composition of the fuel can be altered from its original composition during processing of raw natural gas, for example by altering the quantity and molecular weight of any hydrocarbons extracted during a NGL extraction process, which modifies the Wobbe Index of the gas. The suitability of the gas for downstream users is also dependent upon factors such as the gas export rate (that is, the rate at which downstream users are able to consume the gas), the gas temperature and the gas pressure. Summary of the Invention

In accordance with the present invention, there is provided a fluid transmission control system and method in accordance with the appended claims.

The present invention provides a system and method for controlling the processing of fluid, such that the fluid supplied downstream of fuel sources to a user meets a predetermined specification.

According to embodiments of the present invention there is provided a fluid transmission control system arranged to control the transmission of fluid through a fluid transmission network, the fluid transmission network comprising: a plurality of fluid sources; a fluid processing plant; and a plurality of upstream pipelines located upstream of the fluid processing plant, one or more of the upstream pipelines having a first end connectable to a fluid source and one or more of the upstream pipelines having a second end connectable to an input of the fluid processing plant; the fluid transmission control system comprising: measurement means for measuring one or more characteristics of fluid output from one or more of the plurality of fluid sources; prediction means for predicting, on the basis of the one or more measured characteristics, characteristics of the fluid at the second ends of the plurality of upstream pipelines; and processing determining means arranged to receive data regarding predicted characteristics of the fluid at the second ends of said plurality of upstream pipelines and to determine whether the fluid is suitable for processing by the fluid processing plant.

The fluid transmission control system preferably further comprises adjustment determining means arranged to identify an adjustment to the fluid processing plant and/or the characteristics of fluid from the one or more fluid sources to account for the predicted characteristics, and control means arranged to control the fluid processing plant and/or the characteristics of fluid from the one or more fluid sources in accordance with the identified adjustment.

By analysing the nature of the fluid before it is supplied to a fluid processing plant, a determination can be made as to whether or not the fluid processing should be carried out on the fluid. The processing may not be suitable because it is not required, which is to say that the characteristics of the unprocessed fluid are acceptable without the need for processing, or because the processing equipment is too sensitive to process the fluid, given its current unprocessed composition, and in both cases one or more processing components of the fluid processing plant can be bypassed altogether in order to avoid damage to the processing equipment.

In some situations, the source fluid for the fluid processing plant can be adjusted to account for the sensitivity of the processing equipment, for example by adjusting the flow rate, temperature or percentage of fluids from different sources that combine to provide an input fluid for the fluid processing plant; the invention is therefore particularly applicable to a gas transmission network having multiple fluid sources, for example a network in which gas from a plurality of gas fields is combined.

The invention therefore determines whether or not the characteristics of the gas are such that the gas is suitable for processing at a gas processing plant, and can control the processing on the basis of this determination, for example by bypassing one or more processing components of the gas processing plant or by adjusting the gas characteristics of the input gas entering the gas transmission network.

According to further embodiments of the invention, the prediction means for predicting characteristics of the fluid at the second ends of the plurality of upstream pipelines comprise first prediction means, and the fluid transmission network further comprises one or more downstream pipelines located downstream of the fluid processing plant, one or more of the downstream pipelines having a first end connectable to an output of the fluid processing plant and one or more of the downstream pipelines having a second end connectable to a user's facility.

In such embodiments, the fluid transmission control system comprises: simulation means for simulating operation of the fluid processing plant on the basis of the predicted characteristics of said fluid at the second ends of the plurality of upstream pipelines, so as to generate data indicative of predicted characteristics of fluid output from the simulated fluid processing plant; and second prediction means for predicting, on the basis of the predicted characteristics of the fluid output from the simulated fluid processing plant, characteristics of the fluid at the second ends of one or more of the downstream pipelines connectable to the user's facility, wherein the adjustment determining means are arranged to receive data regarding predicted characteristics of the fluid at the second ends of said one or more of the downstream pipelines connectable to the user's facility, and are further arranged to identify an adjustment to the fluid processing plant to account for said predicted characteristics; and wherein the control means are arranged to control the fluid processing plant in accordance with the identified adjustment.

The fluid processing plant itself can be adjusted pre-emptively to coincide with a change, or to compensate for an undesired value, in fluid characteristics, such as fluid composition. The adjustment in one or more fluid process parameters at the plant may compensate for a change in fluid characteristics such that in some implementations, a product with relatively stable characteristics is exported from the plant, despite varying initial fluid characteristics.

Fluctuations or insufficiencies in gas characteristics with respect to a predetermined specification can be predicted and the characteristics can be adjusted accordingly, in order to pre-empt and thus avoid or minimise problems that currently occur upon the delivery of gas having characteristics outside an acceptable range, or which vary over time, to users. Such characteristics may include the calorific value, lower heating value (LHV, defined as the amount of heat released by combusting a specified quantity (initially at 25°C or another reference state) and returning the temperature of the combustion products to 150°C), higher heating value (HHV), rate of change of the calorific value, flow rate, temperature, pressure, and Wobbe Index of the gas exported to users. In addition, the hydrocarbon composition of the export gas may be determined (mole% and molecular weight of the various hydrocarbon components).

The invention therefore also allows the management of upstream and downstream conditions to optimise the specification of the delivered gas, which originates from multiple sources. The characteristics of the delivered gas can be controlled by predicting downstream gas characteristics and adjusting the parameters of processes applied to the gas at the gas processing plant.

Further features and advantages of the invention will become apparent from the following description of preferred embodiments of the invention, given by way of example only, which is made with reference to the accompanying drawings. Brief Description of the Drawings

Figure 1 shows a fluid transmission network which is controllable by a fluid transmission control system according to the present invention;

Figure 2 is a diagram of the control system of the invention; Figure 3 a is a flowchart of the processes performed by the system of the invention when measuring fluid characteristics and determining whether a change in characteristics has occurred;

Figure 3b is a flowchart of the processes performed by the system when applying a pipeline simulation upstream of a fluid processing plant of the network of Figure 1;

Figure 3 c is a flowchart of the processes performed by the system when applying a fluid processing plant simulation to the network of Figure 1;

Figure 3d is a flowchart of the processes performed by the system when applying a predictive pipeline simulation downstream of the fluid processing plant of the network of Figure 1 ;

Figure 4a is a schematic diagram of pipelines of the fluid transmission network of Figure 1 upstream of the fluid processing plant;

Figure 4b is a flowchart of a predictive pipeline simulation between two nodes A and B of Figure 4a;

Figure 4c is a flowchart of a predictive pipeline simulation between two nodes B and E of Figure 4a;

Figure 4d is a flowchart of a predictive pipeline simulation between two nodes E and G of Figure 4a;

Figure 5a is a flowchart of the processes performed by the system when applying a real time pipeline simulation to the network of Figure 1 ;

Figure 5b is a flowchart of a real time pipeline simulation between two nodes A and B of Figure 4a;

Figure 5c is a flowchart of a real time pipeline simulation between two nodes B and E of Figure 4a;

Figure 5d is a flowchart of a real time pipeline simulation between two nodes E and G of Figure 4a;

Figure 6 is a flowchart of the process performed by a data manager of the control system according to the invention;

Figure 7 is a flowchart showing the steps taken by the control system of the invention in instructing a controller of the fluid processing plant. Detailed Description of the Invention

Referring to Figure 1, an embodiment of a fluid transmission control system arranged to control the transmission of fluid through a fluid transmission network will be described. The network comprises a plurality of pipelines 1 through which fluid, such as natural gas, can flow; these pipelines 1 are situated upstream of a gas processing plant 2, and will therefore be referred to as "upstream" pipelines 1. A number of the plurality of upstream pipelines 1 is connected at one end thereof to one or more fluid sources 3, such as associated gas fields 3 a (Fields J, K, L, M and N) and non-associated gas fields 3 b (Fields A, B, C, D and F); one or more of the upstream pipelines is connected at a second end thereof to an input of the gas processing plant 2. Gas flows through the network of upstream pipelines 1 towards the gas processing plant 2, where it can be processed. Gas leaving the gas processing plant 2 flows downstream thereof through one or more "downstream" pipelines 4, which are situated downstream of the gas processing plant 2, in order to supply the gas to one or more downstream users 5, such as grid networks, industrial users or plants and domestic users. One or more characteristics of the gas at each of the gas fields 3a, 3b, such as gas composition, properties or parameters such as flow rate and temperature, are measurable at one or more measurement devices 6, which preferably include composition analysis equipment, such as gas chromatographic analysis equipment that allows identification of the components of the gas and the relative amounts thereof.

Samples are preferably taken from the gas at source, and may also be taken at a point where the gas from the plurality of sources has been combined and flows through a gas processing plant input pipeline 7, that is, as the gas enters the gas processing plant 2. Such samples may be taken continuously, according to a predetermined schedule, or on demand. Upon sampling and analysing the fluid, gas chromatographic analysis equipment provides information relating to the composition, and hence the net calorific value of, and/or rate of change of the calorific value of, the analysed gas. This characteristic is also known as the lower heating value (LHV), which is measured in KJ/kg (energy per unit mass) or MJ/m³ (energy per unit volume), based on which the efficiency of downstream users 5 such as power plants may be calculated.

Associated and/or non-associated gas from the plurality of gas fields 3a, 3b can be combined in order to ensure that the quality and/or quantity of gas supplied a) is suitable for processing at the gas processing plant 2, and b) is sufficient in quantity and meets specific requirements of the downstream users 5. The gas processing plant 2 comprises a natural gas liquids (NGL) extraction facility 8, which controls the amount of NGL that is extracted from the "raw" natural gas flowing through the gas transmission network by extracting hydrocarbons that are not wanted or required in the gas extracted from the gas fields. The gas processing plant 2 may also comprise gas compression components 9 and/or letdown components 10, in order to increase or decrease, respectively, the pressure of gas to be exported from the gas processing plant 2.

The NGL extraction facility 8 and other processing components of the gas processing plant 2 are relatively sensitive, in that they are capable of processing gas having characteristic values only within certain predetermined limits. When subjected to gas having characteristics outside these limits, undesirable effects such as overheating and/or damage to the components of the gas processing plant 2 can occur. By predicting the characteristics and thus pre-empting the processing of gas having unsuitable or unacceptable characteristic values, such situations can be avoided.

After extraction of the NGL and any other necessary processing, the remaining processed gas is sampled and its characteristics, such as its composition, flow rate and temperature, are measured by one or more further measurement devices 6, before or upon leaving the gas processing plant 2. This processed gas is then exported from the gas processing plant 2 and flows along the one or more "downstream" pipelines 4 situated downstream of the gas processing plant 2, towards the downstream users 5; pipelines 4 may also comprise further compression and/or letdown components 11 to control the pressure of the gas reaching end users. Once processed, gas may be exported to additional users such as petrochemical plants 12, and an additional measurement device 6 may be provided to measure the characteristics of this exported gas.

Referring to Figure 2, a gas transmission control system will now be described in more detail. The analysis and extraction processes are controlled by a controller 13, and the measured and/or determined data is stored in a data store 14. The controller 13 is operably connected to a server 15, which is typically an Openness, Productivity and Connectivity (OPC) server. The controller 13, data store 14 and server 15 are further operably connected to an Advanced Process Controller (APC) unit 16. The APC unit 16 can be used to control the characteristics of the input gas to the gas processing plant 2, by controlling the extent to which gas is extracted from each of the plurality of gas fields 3a, 3b. Such controlling may be implemented by altering the parameters of source fluid input control means, which control the extraction of gas from the sources. The APC unit 16 can additionally or alternatively be used to control parameters of the gas processing plant 2, including those of the NGL extraction facility 8, and hence to control characteristics such as the calorific value or LHV of the gas to be exported from, the gas processing plant 2. In this way, the gas extracted from the gas fields 3a, 3b can be mixed such that the characteristic values of the combined gas input into the gas processing plant 2 are acceptable for processing in the NGL extraction facility 8, or the NGL extraction facility 8 can be bypassed altogether, according to an instruction from the APC unit 16 which acts as a processing determining means. The gas processing ensures that the export gas has characteristics such that, when delivered to downstream users 5, the delivered gas characteristics match pre-set target characteristics or fall within a target range, as described further below.

For example, the APC unit 16 can increase or decrease the extent of NGL extraction, or be configured to ensure that the gas extracted from the gas fields by-passes the NGL extraction facility 8. For example, the APC unit 16 can be configured to decrease the extent of NGL extraction by adjusting the gas processing plant 2 parameters to increase the plant 2 operating temperature, so that fewer NGL components are condensed out and recovered from the gas. Additionally, the APC unit 16 can control the pressure of the gas that is exported to the downstream pipelines 4 by altering parameters of the compression components 9 and/or letdown components 10. The APC unit 16 can be configured with any fixed constraints that are required. In this way, the APC unit 16 can control the characteristics of the input gas entering, and the export gas transported from, the gas processing plant 2. A downstream controller (not shown), in the form of a downstream APC unit or an auto-tuner, controls a downstream user facility, such as a power station. The upstream APC unit 16 and the downstream APC unit can communicate with one another such that data can be passed between these two APC units.

The controller 13 controls all of the functions of the gas processing plant 2 and is a common component in many modern fluid processing plants. When the configuration of the gas processing plant 2 is changed, for example a control valve position is changed, the controller 13 communicates with the valve and makes the change. This is achieved by a set of control loops which use an operator defined set point to control the process function or variable (e.g. a valve position). Control of the main gas processing plant 2 is achieved by a large number of control loops which control a variable by using a plant function such as a valve, compressor speed, etc. The APC unit 16 determines the most optimum set point for the particular plant function and control loop and then uses the operator interface of the controller 13 to implement this change by writing a new set point into the controller 13 control loop for that function. The downstream APC unit (not shown) mentioned above is a similar optimisation unit which uses a downstream user process controller to implement any changes to a downstream user process.

In a similar manner, the APC unit 16 can instruct a source fluid input process, to control the extent to which gas is extracted from each of the plurality of gas fields 3a, 3b. Such control can be implemented via, for example, a valve system (not shown) such that the APC unit 16 instructs certain valves to close or open, either entirely or to a specified degree, to control the characteristics of gas flowing into the upstream pipelines 1 and hence into the gas processing plant 2.

In order to allow prediction and monitoring of the gas input into the gas processing plant 2 and exported and delivered to users, a process management server 20 and a simulation server 30 are provided. A process model, which is run by the simulation server 30, uses existing gas processing plant 2 conditions or parameters in order to predict the characteristics of gas exported from the plant 2. Predictive and real time gas flow models, which are also run by the simulation server 30, operate dynamically, and are preferably applied to the gas flowing through each of the upstream pipelines 1 and the downstream pipelines 4.

Prediction of the gas flow is achieved by the control system and associated pipeline network, the control system employing three predictive "stages". Firstly, a predictive model, in this case referred to as an "upstream" predictive model, is run on each of the upstream pipelines 1, in order to predict the characteristics of the gas that will enter the gas processing plant 2, based on the gas characteristic measurements and analysis of the gas in each of the gas fields 3a, 3b. The final results of this first predictive stage are used to determine whether the gas is suitable for processing by the gas processing plant 2, and are also fed into the process model. The process model is a simulation of the gas processing plant 2 and constitutes a second predictive stage, providing a prediction of the characteristics of the export gas leaving the gas processing plant 2 based on the current gas processing plant 2 parameters. The results of the process model are then input into a second set of predictive models, referred to as "downstream" predictive models, run on each of the downstream pipelines 4 in a third predictive stage. The results of this third predictive stage provide the predicted characteristics of the gas at the downstream ends of the downstream pipelines 4 that will be delivered to the downstream users 5.

Gas from each of the associated gas fields 3 a and non-associated gas fields 3b is sampled from the fields in order to obtain real time information on the characteristics, such as flow rate, temperature and composition of the gas flowing into the upstream pipelines 1. Thus, the gas flowing into the gas processing plant 2 via the gas processing plant input pipeline 7 is a blend of gas originating from different gas fields 3a, 3b.

The predictive model or "predictive pipeline tracker" runs simultaneously to, and is an exact copy of, the real time model which, as mentioned above, is applied to simulate the gas flowing through the pipelines 1, 4; however, while the real time model simulates the current characteristics of the gas in the pipelines and runs in real time, the predictive model is allowed to run at a faster rate, for example, 30 times faster than the real time model, in order to show the predicted characteristics of the gas, preferably at a predetermined time in the future.

The measured gas characteristics of samples taken from the gas fields 3a, 3b are used as input data for the upstream predictive models, the results of which provide a prediction of the gas characteristics of the gas in the input pipeline 7 that will flow into the gas processing plant 2.

In addition, sampling is preferably carried out on the combined gas by measurement devices 6 situated in the input pipeline 7, in order to provide real time information on gas characteristics and to act as a verification of predicted gas characteristics (resulting from the upstream predictive models) of the gas entering the gas processing plant 2.

The results of the upstream predictive models are sent to the process management server 20, which determines whether these predicted gas characteristics are suitable for processing by the gas processing plant 2 by determining whether the predicted values fall within pre-set target characteristic ranges which are stored in an information database 22 of the process management server 20. If the predicted values are within the target ranges, then the APC unit 16 makes no adjustment to the parameters of the source fluid input process. However, if the predicted characteristics do not fall within the target ranges, the process management server 20 instructs the APC unit 16 to make adjustments to the source fluid input process.

The process manager passes the results of the upstream predictive models to a process model run on a plant modelling server or process server 33 of the simulation server 30. The process model predicts the properties of the export gas which will be produced by the gas processing plant 2 if the existing plant process parameters are used to process gas having the predicted characteristics. For example, the online process model can predict the calorific value, the flow rate and the temperature of the gas which would be produced under the existing plant process parameters. The predicted data is then fed back to the process management server 20, which determines whether these predicted properties are acceptable by determining whether the predicted values fall within pre-set target characteristic ranges which are stored in an information database 22 of the process management server 20. If the predicted values are within the target ranges, then the APC unit 16 makes no adjustment to the gas plant processing parameters. However, if the predicted characteristics do not fall within the target ranges, the process management server 20 instructs the APC unit 16 to make adjustments to the NGL extraction process as mentioned above, for example by increasing or decreasing the extent of NGL extraction or by bypassing the NGL extraction facility 8. The process model is preferably run ahead of real time and in conjunction with predictive pipeline models run on the upstream pipelines 1 and the downstream pipelines 4, to provide a prediction of gas characteristics of the gas that will, based on the current processing parameters, flow through the downstream ends of the downstream pipelines 4 and hence be delivered to the downstream users 5; on this basis the system can determine whether or not to use the sampled gas and/or how to process the gas before the gas enters the upstream pipelines 1.

The results of the process model are fed into the downstream predictive models. The resulting prediction from the downstream predictive models is provided to the process management server 20, which uses the information to determine whether and to what extent the characteristics of the gas to be exported from the gas processing plant 2 should be adjusted such that the export gas meets a required gas specification upon delivery to a downstream user 5; once this determination is made and any necessary adjustment to the processing performed by the gas processing plant 2 is identified by the process management server 20, an instruction in the form of a characteristic setpoint (such as an LHV setpoint) is sent to the APC unit 16 at an appropriate time. The APC unit 16 then effects the adjustment at an appropriate time to achieve the required adjustment in exported, and delivered, gas characteristics.

Therefore, the downstream predictive model uses the results of the process model to continue the prediction of gas characteristics, such that a prediction of characteristics of the gas passing through the downstream ends of the downstream pipelines 4, for delivery to end users, can be predicted before any fluid is processed.

Once the gas is processed at the gas processing plant 2 in real time, further gas sampling and analysis is preferably performed at additional measurement devices 6 before feeding the processed export gas into the downstream pipelines 4, in order to determine the precise characteristics of the export gas. These measured values can be compared to the values predicted by the process model, in order to provide a verification of the process model, and can also be used as input data for the real time pipeline model.

The results obtained from measurement devices 6 are preferably passed via the process management server 20 to the downstream APC unit (not shown). The process management server 20 can be arranged to determine whether any of the downstream users' processes need to be altered by the downstream APC unit to account for the measured characteristics of the export gas that will be passed through the downstream pipelines 4. Alternatively, the predictive results of the process model and/or the predictive model can be provided to the downstream APC ahead of real time to allow for such monitoring. Downstream users 5 can access the predicted information via a user data interface (not shown) and request adjustments, such as composition and flow rate adjustments, for example for transition periods.

The real time model or "live pipeline tracker" uses information regarding the gas characteristics to provide the system, any downstream APC units and optionally downstream users 5, with information about the gas flowing through the transmission network. This enables the system and downstream users 5 to monitor the gas composition, calorific value, temperature, pressure, and other characteristics of the export gas as described above.

If any given downstream user's process cannot be altered to account for the predicted or real time gas characteristics, the user can send a request to the process management server 20 for the gas processing parameters of the upstream gas processing plant 2, or the characteristics of the gas from the gas fields 3a, 3b that is input into the gas transmission network, to be adjusted so as to provide gas of a required specification. Such a request is typically time-based and may comprise a LHV or flow rate request. The process management server 20 receives the request and can instruct the APC unit 16 to adjust the gas processing parameters to provide delivered gas in accordance with the requested characteristic for the requested time period. The request may include a maximum acceptable characteristic value or change, and in this case the upstream APC unit 16 also notes this as a reference point. Real time measurement and analysis from the measurement devices 6 can ensure that, should the time period be inaccurate, the rate of change of the gas characteristics is altered to ensure that the requested characteristics are met.

It should be understood that when a change to a characteristic of the gas in one or more of the gas fields 3a, 3b is measured at measurement devices 6, the process model and predictive models are used to determine any change that must be made to the parameters of the gas processing plant 2 before the new gas, having altered characteristics, reaches the plant 2. This is also possible by measuring a change to a characteristic of a blended gas stream before it reaches the gas processing plant 2.

The components and operations of the process management server 20 and the simulation server 30 will now be described in more detail. In figure 3a gas characteristics are measured and the data manager 21 determines whether a change in characteristics has occurred, and in figures 3b to 3d and 5a, the running of the upstream predictive model, the process model, the downstream predictive model and the real time model, respectively, are described.

Turning firstly to Figure 3a, gas characteristic measurement and analysis (SlOl) of the fluid from the gas fields 3a, 3b is preferably performed continuously, or alternatively is performed at regular intervals, at measurement devices 5. Data in relation to a characteristic, such as the LHV, is routinely passed (S 102) to the controller 13 and is stored or recorded in the data store 14. The process management server 20 comprises a data manager 21, which routinely requests, from the controller 13, data on key fluid characteristics stored in the data store 14. The data manager 21 may request such data at set intervals, for example. This data is then passed (S 103) through the OPC server 15 to the data manager 21. The data manager 21 then compares (S 104) the most recently received value(s) for the LHV stored within the information database 22 of the process management server 20 to assess whether a change has occurred. If the LHV is shown to be unacceptably lower (or higher, as appropriate) than a predetermined threshold value, such as a data tolerance value, which is stored in the database 22, further action by the data manager 21 is required.

The upstream pipeline network can be considered to comprise a plurality of pipeline "sections", as shown in Figure 1 and as will be described in more detail with reference to Figure 4a. The predictive model is typically applied to each section of the upstream pipeline network. In general, each predictive pipeline model run on each section of the upstream pipelines uses as its input data the results of the model run on the section of pipeline immediately upstream of that section of pipeline. Any given upstream predictive model run for one or more upstream pipeline sections of which one end is connected to a gas field 3a, 3b is based upon the measured characteristic data.

Referring to Figure 3b, the upstream predictive modelling will now be described. The data manager 21 sends (S201) the new LHV, together with all other measured gas characteristics (which may not have changed in conjunction with the LHV) to a predictive server 31 of the simulation server 30, and requests a list of critical information that is required by the server in order to run the predictive model for each of the upstream pipelines 1. The predictive server 31 feeds (S202) the new LHV, together with all other measured gas characteristics, into the predictive model for the relevant section of pipeline. A series of predictive models run (S203) ahead of real time as described further below with reference to Figures 4a to 4d, in order to determine information relating to predicted characteristics of the gas in the input pipeline 7 which will be input into the gas processing plant 2, based on the measured characteristics of the gas in the gas fields 3a, 3b. This information is then sent via the predictive server 31 and the data manager 21 to the information database 22 (S204), where it is recorded and an assessment is made as to whether or not the gas characteristics are such that the gas is suitable for processing by the gas processing plant 2, based, for example, on known acceptable characteristics for each of the components 8, 9, 10 of the gas processing plant 2. Such information is part of the critical information requested by the data manager 21, which may include information relating to time, pressure, temperature, LHV, gas flow rate, etc. If the gas is considered to be suitable for processing by the gas processing plant 2, no further action is required (S205).

The data manager 21 comprises a custom calculation package 23, and uses this to determine (S206) the type and extent of an adjustment required by the system to counteract the anticipated characteristic value of the gas flowing through the input pipeline 7, if the gas is considered not to be suitable for processing. In the case of a change to the flow rate, the time and flow rate may be used to calculate what adjustment is required by the gas field input processes, and the exact time at which this adjustment should be implemented. Such information is determined as setpoints sent as instructions to the upstream APC unit 16, as described in more detail below in relation to Figure 6.

Continuing to refer to Figure 3b, the results of step S206 are recorded (S207) in the information database 22 for a "patience time" period, so that this data can be checked or confirmed. The patience time can be defined as a period of time for which the upstream APC unit 16 waits whilst the data manager 21 confirms (S208) that any adjustment data determined by the data manager 21 is consistent, and hence may be acted upon. The data manager 21 receives a subsequent set of data sent, as in step S204, from the predictive server 31, and compares these data against the adjustment data stored in the information database 22, typically employing an algorithm in order to assess whether the adjustment data is "true" or "false" data. This process can be performed with a number of sets of data such that any number of sets of data are compared in providing the confirmation.

If the stored adjustment data is verified, the adjustment data are transferred (S209) into an action list of setpoints for the APC unit 16 stored within the information database 22, for instructing the APC unit 16 at an appropriate time to allow correct implementation of the setpoint. Each of the setpoints in the action list is time stamped with this appropriate instruction time. Alternatively, if the reading is determined to be false, the action is discarded by the data manager 21. The implementation or action time can be calculated for gas for which a predetermined release time is known. Alternatively, the action time can be based on an unknown release time, in which case a flag is sent to the data manager 21 upon release of the gas to calculate the exact action time. Once the action time is reached, the data manager 21 sends (S210) the new setpoint for the flow rate to the APC unit 16 via the OPC server 15, and the adjustment is implemented (S211) by suitable gas input control means (not shown). Steps S201 to S211 are repeated every time a change in excess of a predetermined threshold, to any of the gas characteristics, is determined. Referring to Figure 3 c, the gas processing plant simulation model will now be described. The data manager 21 sends (S301) the predicted gas characteristic data output by the upstream predictive models, which have been run using the new measured LHV and all other measured gas characteristics (which may not have changed in conjunction with the LHV) to the process server 33 of the simulation server 30, and requests a list of critical information that is required by the server in order to run the process model. The process server 33 feeds (S302) the results of the upstream predictive models into the process model. The process model runs (S303) ahead of real time as described above, in order to determine information relating to predicted characteristics of the export gas produced by the gas processing plant 2 based on the current plant 2 processing parameters. This information is then sent via the process server 33 and the data manager 21 to the information database 22 (S304), where it is recorded. Such information is part of the critical information requested by the data manager 21, which may include information relating to time, pressure, temperature, LHV, gas flow rate, etc.

Referring to Figure 3d, the downstream predictive model (s) will now be described. The data manager 21 sends (S401) the predicted gas characteristics output by the process model, which has been run using the results of the upstream predictive model(s), to the predictive server 31 of the simulation server 30, and requests a list of critical information that is required by the server in order to run the downstream predictive pipeline model(s). The predictive server 31 feeds (S402) the results of the process model into the predictive pipeline model. The predictive pipeline model runs (S403) ahead of real time as described above, in order to determine information relating to predicted characteristics of the gas when it flows through the downstream ends of the downstream pipelines 4, having been processed according to the gas processing plant 2 parameters used in the process model. In the case of a plurality of downstream pipelines 4, as shown in Figure 1 , a plurality of corresponding consecutive predictive pipelines models is applied, in a similar manner to that described below for the upstream pipelines 1 (to be described in more detail with reference to Figures 4a to 4d). This information is then sent to the predictive server 31. Such information is part of the critical information requested by the data manager 21, which may include information relating to time, pressure, temperature, LHV, gas flow rate, etc. The critical information is then sent (S404) to the data manager 21.

As mentioned above, the data manager 21 comprises a custom calculation package 23. In addition to its use in relation to the upstream pipeline network, the custom calculation package 23 is used to determine (S405) the type and extent of an adjustment required by the system to counteract the anticipated characteristic value of the gas flowing through the downstream ends of the downstream pipelines 4 that has been predicted by the downstream predictive models. In the case of a change to the LHV, the time and LHV may be used to calculate what adjustment is required by the gas processing plant 2 processes, and the exact time at which this adjustment should be implemented. Such information is determined as setpoints sent as instructions to the upstream APC unit 16, and is described in more detail below in relation to Figure 6.

Continuing to refer to Figure 3d, a process similar to that described with respect to Figure 3b above is followed. The results of step S405 are recorded (S406) in the information database 22 for the "patience time" period, so that this data can be checked or confirmed. During the patience time the upstream APC unit 16 waits whilst the data manager 21 confirms (S407) that any adjustment data determined by the data manager 21 is consistent, and hence may be acted upon. The data manager 21 receives a subsequent set of data sent, as in step S404, from the predictive server 31, and compares these data against the adjustment data stored in the information database 22, typically employing an algorithm in order to assess whether the adjustment data is "true" or "false" data. As with the process of Figure 3b, this process can be performed with a number of sets of data such that any number of sets of data are compared in providing the confirmation.

In general, each predictive pipeline model uses as its input data the results of the model run on the section of pipeline immediately upstream of that pipeline. Any given downstream predictive model run for one or more downstream pipelines of which one end is connected to an output of the gas processing plant 2 is based upon the results of the process model. The results of the process model are based on the results of the upstream predictive models, which in turn are based on measured characteristics of the gas from the plurality of gas fields 3a, 3b.

In a similar manner to the process described with respect to Figure 3b, if the stored adjustment data is verified, the adjustment data are transferred (S408) into an action list of setpoints for the upstream APC unit 16 stored within the information database 22, for instructing the upstream APC unit 16 at an appropriate time to allow correct implementation of the setpoint. Each of the setpoints in the action list is time stamped with this appropriate instruction time. Alternatively, if the reading is determined to be false, the action is discarded by the data manager 21. The implementation or action time is calculated for gas that has already left the field 3a, 3b, or for which a predetermined release time is known. Alternatively, the action time can be based on an unknown release time, in which case a flag is sent to the data manager 21 upon release of the gas to calculate the exact action time. Once the action time is reached, the data manager 21 sends (S409) the new setpoint for the LHV to the APC unit 16 via the OPC server 15, and the adjustment is implemented (S410) by the controller 13. Steps S401 to S410 are repeated every time a change in excess of a predetermined threshold, to any of the gas characteristics, is determined.

The process and predictive models are run in a stepping mode, therefore each time the model is re-run (in theory for the same gas as it flows along the pipeline), the results from the previous process or predictive model, respectively, are used as an input into the model re-run.

Further details of the upstream predictive model will now be described with reference to Figures 4a to 4d. Figure 4a is a schematic diagram of the upstream pipeline network running from the non-associated gas fields 3b, labelled fields A to D and Field F, to the gas processing plant 2. Each pipeline section IA to IG of the upstream pipelines 1 is connected to a gas field A to D, F, another section of pipeline and/or the gas processing plant by a node A to H. The gas processing plant input pipeline 7 is represented in Figure 4a by pipeline section 1 G.

Figure 4b shows the predictive model process between nodes A and B of Figure 4a. The system receives (S501) measurement data regarding the gas characteristics of the gas in Field A from measurement devices 6, which represents the gas that will be input into the network at node A. This data can additionally comprise analysis data, where the measurement devices further include analysis equipment such as gas chromatographic analysis equipment. As described above, samples of the gas are taken continuously, according to a predetermined schedule, or on demand. At step S502, the Field A data received at step S501 is sent to the data manager 21 where it is stored and is compared to stored Field A measurement data that has previously been received and stored in the information database 22 of the process management server 20. At step S503, the data manager 21 determines whether there has been a change to the characteristics of the Field A gas; such a change could be determined in relation to a predetermined threshold, beyond which further action must be taken. If no change to the characteristics is determined, or if a change that is within acceptable threshold limits is detected, no further action is taken (S504) in relation to the new measurement data.

Conversely, if the data manger 21 determines that a change to the gas characteristics has occurred, the predictive model is run, at step S505, for pipeline IA. At step S506, the results of the predictive model provide a calculation as to the arrival characteristics of the gas at the end of pipeline IA (which is effectively node B); that is, the predicted gas characteristics of the Field A gas at the node B, together with the time at which the gas will reach node B. This predicted characteristic data is timestamped with the predicted arrival time of the gas at the downstream end of pipeline IA. The timestamped predicted characteristic data is stored in the information database 22 (S507). This data is then used as the input to the predictive model for pipeline IB (S508). The database 22 of predicted gas characteristics and corresponding timestamps is continually monitored by the data manager 21.

Figure 4c shows the predictive model process between nodes B and E of Figure 4a. The system receives (S601) the predicted characteristic data resulting from running the predictive model on pipeline IA, as described in relation to Figure 4b. This data represents the predicted characteristics of the gas originating from Field A that will be present at node B. The system is additionally sent (S602) measurement data regarding the gas characteristics of the gas in Field B from measurement devices 6, which represents the gas that will be input into the network at node B from Field B. It should be understood that the control system is continually receiving predicted characteristic results from the previous pipeline IA and measured characteristics from the measured characteristics at Field B. The gas from pipeline IA and Field B will meet and combine at node B, and the data manager therefore uses the custom calculation package 23 to align the received data into common timestamps; the expected time of arrival of the gas from Field B, having the characteristics measured, at node B is calculated, typically by running the predictive model, and this measurement data is timestamped accordingly. Based on this and the timestamps of the predicted results from pipeline IA, the data manager 21 aligns (S603) the predicted and measured data into common timestamps, such that the characteristics of the gas reaching node B from pipeline IA and from Field B at the same time are matched. As the timestamped data is continually being received, the data is ordered by timestamp in step S604. The data manager 21 the uses the custom calculation package 23 to combine (S605) each of these time-ordered entries into a single data stream representing the predicted gas characteristics of the combined gas at node B; this is performed for each common timestamp.

At step S606, the predicted combined data is compared to stored node B predicted characteristic data that has previously been received and stored in the information database 22 of the process management server 20. At step S607, the data manager 21 determines whether there has been a change to the node B combined gas characteristics; such a change could be determined in relation to a predetermined threshold, beyond which further action must be taken. If no change to the characteristics is determined, or if a change that is within acceptable threshold limits is detected, no further action is taken (S608) in relation to the new predicted data.

Conversely, if the data manger 21 determines that a change to the gas characteristics has occurred, the predictive model is run with the changed, newly predicted data, at step S609, for pipeline IB. At step S610, the results of the predictive model provide a calculation as to the arrival characteristics of the gas at the end of pipeline IB (which is effectively node E), that is, the predicted gas characteristics of the combined gas from pipeline IA and B at node E, together with the time at which the gas will reach node E. This predicted characteristic data is timestamped with the predicted arrival time of the gas at node E. The timestamped predicted characteristic data is stored in the information database 22 (S611). This data is then used as a part of the input data for the predictive model for pipeline IE (S612). The database 22 of predicted gas characteristics and corresponding timestamps is continually monitored by the data manager 21.

A similar process to that carried out for the measured gas characteristics of Field A in predicting the characteristics of this gas at node B (as described with reference to Figure 4b), is carried out for the characteristics measured at each of Fields C and D to predict the gas characteristics at node E.

Figure 4d shows how the predicted characteristic data is used in the predictive model process between nodes E and G of Figure 4a. The system receives (S701) the predicted characteristic data resulting from running the predictive model on pipelines IB, 1C and ID, as described in relation to Figure 4c. This data represents the predicted characteristics of the gas originating from each of pipelines IB, 1C and ID that will be present at node E. It should be understood that the control system is continually receiving predicted characteristic results from the previous pipelines. The gas from pipelines IB, 1C and ID will meet and combine at node E, and the data manager 21 therefore uses the custom calculation package 23 to align (S702) the received data into common timestamps, such that the characteristics of the gas reaching node E from pipelines IB, 1C and ID at the same time are matched. As the timestamped data is continually being received, the data is ordered by timestamp in step S703. The data manager 21 the uses the custom calculation package 23 to combine (S704) each of these time-ordered entries into a single data stream representing the predicted gas characteristics of the combined gas at node E; this is performed for each common timestamp.

At step S705, the predicted combined data is compared to stored node E predicted characteristic data that has previously been received and stored in the information database 22 of the process management server 20. At step S706, the data manager 21 determines whether there has been a change to the node E combined gas characteristics; such a change could be determined in relation to a predetermined threshold, beyond which further action must be taken. If no change to the characteristics is determined, or if a change that is within acceptable threshold limits is detected, no further action is taken (S707) in relation to the new predicted data.

Conversely, if the data manger 21 determines that a change to the gas characteristics has occurred, the predictive model is run with the changed, newly predicted data, at step S708, for pipeline IE. At step S709, the results of the predictive model provide a calculation as to the arrival characteristics of the gas at the end of pipeline IE (which is effectively node G), that is, the predicted gas characteristics of the combined gas from pipelines IB, 1C and ID at node G, together with the time at which the gas will reach node G. This predicted characteristic data is timestamped with the predicted arrival time of the gas at node G. The timestamped predicted characteristic data is stored in the information database 22 (S710). This data is then used a) in order to determine any adjustment to the gas input process and b) as a part of the input data for the predictive model for pipeline IG (S711). The database 22 of predicted gas characteristics and corresponding timestamps is continually monitored by the data manager 21.

In the case that the network comprises a plurality of downstream pipelines 4, a predictive model and real time model is run on each section of pipeline, the first section model using the predicted export gas characteristics resulting from running the process model (and, in the case of the real time model, a calculated current time). Alternatively, in the case of the real time model, the measured gas characteristics from the measurement devices 6 situated in the first of the downstream pipelines to which an output of the gas processing plant 2 is connected, can be used as input data for the models of the first section of the downstream pipelines. Each subsequent section of pipeline uses as input data the results of the predictive model, or the real time model, respectively, for the previous section.

Turning now to aspects of the real time modelling, this is preferably performed in parallel with the predictive modelling. As mentioned above, the real time model simulates the current characteristics of the gas in each of the upstream pipelines 1 and downstream pipelines 4 in real time, while the predictive model runs at a faster rate, ahead of the real time model, in order to determine the predicted characteristics of the gas at a time in the future.

Referring to Figure 5 a, in the case of real time modelling, the data manager 21 sends (S 801) input characteristics, such as the predicted gas characteristics output by the process model, to a real time server 32 of the simulation server 30, and requests a list of critical information that is required by the server in order to run the real time model. The real time server 32 feeds (S802) the results of the process model into the real time model. The real time model runs (S803) as described above, using a characteristic tracking software element to time stamp the characteristic data. The time stamped characteristic data is then sent via the real time server 32 and the data manager 21 to the information database 22 (S804). Such information corresponds to the critical information requested by the data manager 21, which may include information relating to time, pressure, temperature, LHV, gas flow rate, etc. The information database 22 records the time and characteristic information received. The information database 22 can be accessed by system operators and engineers, as well as by downstream users 5 via the internet using a user data interface (not shown) to view the time and gas characteristic information as it is tracked down the pipelines.

The real time model is also used to determine "residence times" for various sections of the gas transmission network, these residence times being the predicted times for gas to flow from one end to another of the sections. For example, the residence time of the each of the upstream pipelines 1 (that is, each section of pipeline IA, IB, 1C, etc.) is the time it takes the export gas to flow from one end of the upstream pipeline section to another. There is also a residence time for the gas to pass through the gas processing plant 2. It is important that the system has sufficient time to calculate the required LHV of the export gas and appropriate action time at which to instruct the APC unit 16, as well as allowing for the reaction time of the APC unit 16 upon receipt of the instruction to be implemented. Accordingly, the system can ensure that there is sufficient time to make an adjustment, thereby ensuring that the characteristics of gas entering the gas processing plant 2 and of gas being delivered to downstream users 5 continue to match pre-set target characteristics or continue to fall within a target range. If there is insufficient time to perform these actions, no action is taken and the process is repeated when a further change to the gas characteristics is determined.

Further details of the real time model run on the upstream pipelines 1 will now be described with reference to Figures 4a and 5b to 5d; it will be appreciated that the processes of Figures 5b to 5d are similar in many respects to those described with reference to Figures 4b to 4d.

Figure 5b shows the real time model process between nodes A and B of Figure 4a. The system receives (S901) measurement data regarding the gas characteristics of the gas in Field A from measurement devices 6, which represents the gas that will be input into the network at node A. This data can additionally comprise analysis data, where the measurement devices further include analysis equipment such as gas chromatographic analysis equipment. Samples of the gas are taken continuously, according to a predetermined schedule, or on demand. At step S902, the Field A data received at step S801 is sent to the data manager 21 where it is stored and is compared to stored Field A measurement data that has previously been received and stored in the information database 22 of the process management server 20. At step S903, the data manager 21 determines whether there has been a change to the characteristics of the Field A gas; such a change could be determined in relation to a predetermined threshold, beyond which further action must be taken. If no change to the characteristics is determined, or if a change that is within acceptable threshold limits is detected, no further action is taken (S904) in relation to the new measurement data. Conversely, if the data manger 21 determines that a change to the gas characteristics has occurred, the real time model is run, at step S905, for pipeline IA. At step S906, the results of the real time model provide a calculation as to the arrival characteristics of the gas at the end of pipeline IA (which is effectively node B), that is, the real time gas characteristics of the Field A gas at the node B, together with the time at which the gas reaches node B; this real time characteristic data is timestamped with the calculated arrival time of the gas at the downstream end of pipeline IA. The timestamped characteristic data is stored in the information database 22 (S907). The data manager 21 then retrieves (S908) the current time and determines (S909) whether the calculated timestamp is equal to the current time. If the timestamp is not equal to the current time, steps S908 and S909 are repeated, to ensure that the real time timestamp is accurate. If the timestamp is equal to the current time, the data is used as the input to the real time model for pipeline IB (S910). The database 22 of real time gas characteristics and corresponding timestamps is continually monitored by the data manager 21.

Figure 5c shows the real time model process between nodes B and E of Figure 4a. The system receives (SlOOl) the real time characteristic data resulting from running the real time model on pipeline IA, as described in relation to Figure 5b. This data represents the real time characteristics of the gas originating from Field A that are be present at node B. The system is additionally sent (S 1002) measurement data regarding the gas characteristics of the gas in Field B from measurement devices 6, which represents the gas that is input into the network at node B from Field B. It should be understood that the control system is continually receiving real time characteristic results from the previous pipeline IA and measured characteristics from the measured characteristics at Field B. The gas from pipeline IA and Field B will meet and combine at node B, and the data manager therefore uses the custom calculation package 23 to combine (S 1003) each of these time-ordered entries into a single data stream representing the predicted gas characteristics of the combined gas at node B; this is performed for each common timestamp.

At step S 1004, the combined real time data is compared to stored node B real time characteristic data that has previously been received and stored in the information database 22 of the process management server 20. At step S 1005, the data manager 21 determines whether there has been a change to the node B combined gas characteristics; such a change could be determined in relation to a predetermined threshold, beyond which further action must be taken. If no change to the characteristics is determined, or if a change that is within acceptable threshold limits is detected, no further action is taken (S 1006) in relation to the new real time data.

Conversely, if the data manger 21 determines that a change to the gas characteristics has occurred, the real time model is run with the changed, newly calculated data, at step S1007, for pipeline IB. At step S1008, the results of the real time model provide a calculation as to the arrival characteristics of the gas at the end of pipeline IB (which is effectively node E), that is, the real time gas characteristics of the combined gas from pipeline IA and B at node E, together with the time at which the gas reaches node E. This real time characteristic data is timestamped with the calculated arrival time of the gas at node E. The timestamped real time characteristic data is stored in the information database 22 (1009). The data manager 21 then retrieves (SlOlO) the current time and determines (SlOl 1) whether the calculated timestamp is equal to the current time. If the timestamp is not equal to the current time, steps SlOlO and SlOI l are repeated, to ensure that the real time timestamp is accurate. If the timestamp is equal to the current time, the data is used as the input to the real time model for pipeline IE (S 1012). The database 22 of real time gas characteristics and corresponding timestamps is continually monitored by the data manager 21.

A similar process to that carried out for the measured gas characteristics of Field A in calculating the real time characteristics of this gas at node B (as described with reference to Figure 5b), is carried out for the characteristics measured at each of Fields C and D to calculate or model the real time gas characteristics at node E.

Figure 5d shows how the real time characteristic data is used in the real time model process between nodes E and G of Figure 4a. The system receives (Sl 101) the real time characteristic data resulting from running the real time model on pipelines IB, 1C and ID, as described in relation to Figure 5b. This data represents the real time characteristics of the gas originating from each of pipelines IB, 1C and ID that will be present at node E. It should be understood that the control system is continually receiving real time characteristic results from the previous pipelines. The gas from pipelines IB, 1C and ID will meet and combine at node E, and the data manager 21 therefore uses the custom calculation package 23 to combine (Sl 102) each of these time-ordered entries into a single data stream representing the real time gas characteristics of the combined gas at node E; this is performed for each common timestamp.

At step S 1103, the combined real time data is compared to stored node E real time characteristic data that has previously been received and stored in the information database 22 of the process management server 20. At step Sl 104, the data manager 21 determines whether there has been a change to the node E combined gas characteristics; such a change could be determined in relation to a predetermined threshold, beyond which further action must be taken. If no change to the characteristics is determined, or if a change that is within acceptable threshold limits is detected, no further action is taken (Sl 105) in relation to the new real time data.

Conversely, if the data manger 21 determines that a change to the gas characteristics has occurred, the real time model is run with the changed, newly calculated data, at step Sl 106, for pipeline IE. At step Sl 107, the results of the real time model provide a calculation as to the arrival characteristics of the gas at the end of pipeline IE (which is effectively node G), that is, the real time gas characteristics of the combined gas from pipelines IB, 1C and ID at node G, together with the time at which the gas reaches node G. This predicted characteristic data is timestamped with the calculated arrival time of the gas at node G. The timestamped real time characteristic data is stored in the information database 22 (Sl 108). The data manager 21 then retrieves (Sl 109) the current time and determines (Sl 110) whether the calculated timestamp is equal to the current time. If the timestamp is not equal to the current time, steps Sl 109 and Sl I lO are repeated, to ensure that the real time timestamp is accurate. If the timestamp is equal to the current time, the data is used as the input to the real time model for pipeline IG (Sl 111). The database 22 of predicted gas characteristics and corresponding timestamps is continually monitored by the data manager 21.

The process management server 20 employs suitable software, while methods for configuring a simulation model using the HYSYS application are known to those skilled in the art and can be employed by the simulation server 30 and associated predictive server 31, real time server 32 and process server 33.

As will be appreciated from the foregoing, the process and predictive models may be initiated by the data manager 21. Once the upstream predictive models are running, the data manager 21 can instruct the simulation server 30 to record the results of the upstream predictive model of the input pipeline 7 and initiate the process model using these results. Similarly, once the process model is running, the data manager 21 can instruct the simulation server 30 to record the results of the process model and initiate the downstream predictive model(s) using these results.

Turning now to aspects of the adjustment process described above with reference to steps S204 to S211 of Figure 3b, and steps S404 to S410 of Figure 3d, Figure 6 shows a flowchart of the process performed by the data manager 21 in calculating the adjustment data, in terms of a time and a LHV (the LHV characteristic can be replaced by any characteristic that is controllable with respect to time). The data manager 21 preferably runs through the process of Figure 6 continuously; in overview, the process involves the data manager 21 running the downstream predictive model(s) ahead of time and recording the results. The data manager 21 then uses the custom calculations package 23 to work out what to adjust and when, based on the results of the predictive model(s). The adjustment data is then stored for the patience time before being sent as a setpoint to the upstream APC unit 16. Each time a change in a measured characteristic or a change in the results of the process model occur, the same predictive actions of Figure 6 will occur. An appropriate algorithm in relation to the characteristic will be applied as part of the custom calculations package 23 for the data manager 21.

At step S 1201, any change to the gas characteristics determined as a result of a change to the measured characteristic data or to the results of the process model, and real time data, are received by the data manager 21 and are stored in the information database

22. At step S 1202, the data manager 21 receives the predicted characteristic(s) and critical information from the predictive server 31 (as in step S404 of Figure 3d). The data manager 21 then checks (S 1203) whether the predicted characteristic and critical information meet a value (for example, a threshold value), or are within a target range of values, in relation to current target delivery conditions. If this is the case, no action is taken (S 1204). Alternatively, if the predicted characteristic and critical information do not comply with the target value or range of values, a characteristic adjustment calculation is requested (S 1205), which involves the input of critical information from the information database 22 and the implementation of a calculation from the custom calculation package

23. A new required LHV value, one which is required to ensure that the gas flowing through the downstream ends of the downstream pipelines 4 to the users 5 complies with the target value or range, is calculated at step S 1206; effecting this change to the LHV value necessitates an adjustment to the gas input process or gas processing plant 2 parameters. As a result the NGL extraction process can be modified or bypassed accordingly. At step S 1207, the custom calculation package 23 is again employed to provide an action time calculation at which the adjustment must be instructed to the upstream APC unit 16. This action time is calculated at step S1208, and again critical and/or characteristic data may be accessed from the information database 22 for the calculation. At step S 1209, the adjustment data is timestamped with the action time at which the instruction is to be sent to the upstream APC unit 16, and at step S 1210 the timestamped adjustment data is stored in the upstream APC unit 16 action list in the information database 22.

The current timestamp parameters are checked (S 1211) against those used previously in calculating the action time at step S 1208, and an assessment is made (S 1212) as to whether or not the timestamp parameters have changed. For example, if the gas originating at the gas fields 3 a, 3 b and measured at the measurement devices 6 shows a decrease in LHV, the custom calculation package 23, optionally in association with the predictive model, calculates or predicts the required LHV of the input gas together with the time at which the gas processing plant 2 will have to make an adjustment to compensate for the decrease in LHV, in accordance with steps S 1205 and S 1207 above. The rate at which this input gas reaches the gas processing plant 2 is determined by the flow rate of the gas through the upstream pipelines 1 linking the gas fields 3a, 3b and gas processing plant 2.

As mentioned above, the time taken for that gas to flow from one end (the gas fields 3a, 3b) to another (the input of the gas processing plant 2) is important. Should the gas flow rate into the supply pipeline decrease at some point in the future, then the time it will take the gas with decreased LHV to flow through the upstream pipelines 1 will change; that is, the residence time of the at least one of the plurality of upstream pipelines 1 will increase. The APC unit 16 will therefore need to make the same change to the gas processing but at a different time; hence there is a requirement to re-run and re-timestamp the setpoint data. This process will be described further in connection with Figure 7 below, but first, and referring again to Figure 6, if the timestamp parameters have changed, the process returns to step S 1207 and the action time calculation is performed again with the changed timestamp parameters. This feedback loop is provided to ensure that the upstream APC unit 16 does not make changes to the gas processing plant 2 solely based on the change in gas characteristics.

If it is determined at step S 1212 that the timestamp parameters have not changed, the process progresses to step S 1213 where an assessment is made as to whether or not the action time of the timestamp is equal to the actual current time. If the action time has not been reached, the data manager 21 does not send the instruction or setpoint but waits (S 1214) and returns to perform steps S 1211 onwards again. Alternatively, if the action time is equal to the current time, that is, the action time has been reached in real time, the setpoint is sent by the data manager 21 and executed by the APC unit 16 at step S 1215, so that the calculated adjustment is implemented at the calculated action time.

It should be understood that the predictive model has additional functions when run on the pipeline sections; one is to act as a verification to ensure that the custom calculation package 23 has made the correct adjustments, and the other applies in more complex calculations (for example, when multiples are changing), when the predictive model can be run to help the custom calculations package 23 to achieve the correct change. Essentially the predictive model can provide more resolution to the calculations within the custom calculation package 23.

Preferably, the system can be programmed to evaluate a predetermined number of measurements and to run the upstream predictive models, the process model and then the downstream predictive model(s) for each of these as appropriate before allowing an adjustment to be instructed. For example, ten (eight, five or three) samples can be taken, analysed and used in the upstream predictive models, which give a prediction of the characteristics of the gas entering the gas processing plant 2, before the system will confirm the gas input process adjustment and action time. These predicted characteristics can then be input into the process model, and the results of the process model are used in the downstream predictive model(s), before the system will confirm the gas processing adjustment and action time. As additional verifications are made, the error margin of the adjustment data is reduced. If, when performing a verification with a subsequent set of data, it transpires that no action is required (for example, in the case that the previous data was erroneous), the data can be re-timestamped or the setpoint removed, as appropriate.

The custom calculations package 23 of the predictive model takes a number of factors into account when calculating predicted gas characteristics for the gas flowing into the gas processing plant 2 and to the downstream users 5.

Firstly, a "LHV balance" calculation is performed as described above to calculate the required LHV (or other required gas characteristic) to be input into or exported by the gas processing plant 2, in order that the gas entering the gas processing plant 2 is suitable for processing or that the gas delivered to a downstream user 5 has a specific LHV (or other required gas characteristic), respectively.

Another of the possible characteristics upon which such a prediction can be made is the flow rate of the gas, in which case a "flow balance" calculation is performed. This calculates the Standard Volumetric Flowrate of gas to be input into or exported by the gas processing plant 2, in order that the gas entering the gas processing plant 2 or the gas delivered to a downstream user 5 has a suitable or specified flow rate.

A further LHV balance calculation can be performed to aid the prediction of the rate of change of the LHV of the gas that enters the gas processing plant 2 or is delivered to a downstream user 5.

Additionally, as mentioned above, a "residence time" is calculated (typically by the real time model) for each section of the gas transmission network. The residence times allow the system to consider how many times the process model and the predictive model can be run (factoring in the patience time) to verify the adjustment data and hence reduce the error margin.

Referring to Figure 7, the steps taken in writing an instruction to the APC unit 16 will be described. At step S 1301, the data manager 21 reads the action data from the action table of the information database 22. The action table may include a flag field indicating the status of an action to be "true" if it has already been written to the APC unit 16 but is still stored in the database 22, or "false" if the action is yet to be written to the APC unit 16; in this case the data is only read if the status flag is false. The current cumulative residence times of the upstream pipelines 1 (ClRT) is identified by running the real time model, as described in Figures 5a to 5d, on the plurality of upstream pipelines 1 in step S1302.

At step S 1303, a "reconciled timestamp" (RTS) is calculated, by subtracting the difference between a cumulative residence time originally calculated during the running of the predictive models (PlRT) and the current cumulative residence time (ClRT), from the original timestamp (OTS) calculated during the running of the predictive models: RTS = OTS - (PlRT - ClRT). The reconciled timestamp is then stored in a relevant field in the action table. This reconciled timestamp is the new time at which the adjustment data needs to be written or instructed to the APC unit 16, owing to the change in the residence time of the upstream pipelines 1 from the time at which the predictive model runs took place, to the current time; such a change could occur due to a change in the flow rate of the gas.

At step S 1304, the data manager 21 determines whether there is a reconciled timestamp entry stored in the action table that is within a certain time, for example within one minute, of the current time. If there is such a reconciled timestamp entry, the corresponding adjustment data, such as a new LHV, is written (S 1305) to the APC unit 16 and the entry is marked as "true" accordingly. If no such entry is present in the action table, the system waits, at step S 1306, for an appropriate predetermined time, such as 30 seconds, and then returns to the start of the process.

Preferably, the adjustment is instructed as a single instruction of a specified rate of change, rather than instructing the APC 16 with a plurality of staged or stepped discrete value changes, which results in the desired overall adjustment, as typical gas processing plants 2 deal with a "rate of change" adjustment more effectively; however, both implementations are possible.

The system and method of the invention may be applied in a number of scenarios, for example: in order to maintain a constant LHV value; in order to maintain steady flow rate and LHV to a downstream user; to maximise NGL production whilst managing minimum rate of change of LHV to a downstream user; to maintain flow rate and LHV to match downstream user demand; and to maximise NGL production whilst managing a maximum rate of change of LHV to a downstream user.

The gas transmission control system may be implemented where gas is supplied to multiple downstream users 5 situated downstream of the gas processing plant 2. For example, the transmission network may supply three or four power stations, all of which have specific gas characteristic requirements, and one of which has a narrower acceptable range for the LHV or rate of change of the LHV than the other stations. In this case, gas having characteristics that meet the narrower specification, and which therefore meets all of the stations' specifications, can be produced. Adjustments can be made to the gas characteristics depending on which power stations are on or require a supply at a certain time, in order to manage gas transmission network and control system efficiency and costs. Pre-emptive analysis of the gas in the gas fields can be performed relatively quickly by the system, advantageously minimising gas processing time, effort and cost at the gas processing plant 2.

The invention provides significant advantages for downstream gas users in reliability, maintainability and efficiency of combustion. For example, gas fired turbine power stations are particularly sensitive to gas quality changes, particularly in LHV and Wobbe Index. The range of both of these characteristics is required to be within a defined range of values to ensure acceptable operation, while failure to adhere to this can lead to poor power station availability.

Plants which burn natural gas can suffer the problem of turbine rumble depending on the rate of change of composition of the supplied gas. However, the composition of gas from one source is rarely the same as that from another source. Thus, when gas from multiple sources is combined, a gas composition that varies over time can result. This variation can lead to turbine rumble since the plant's operating parameters are not matched to the variation in gas characteristics over time. The gas transmission control system and method described above avoid such situations, by predicting the variation in characteristics and responding to this prediction by pre-emptively adjusting the processing of the incoming fluid supply.

The above embodiments are to be understood as illustrative examples of the invention. Further embodiments of the invention are envisaged. It is to be understood that any feature described in relation to any one embodiment may be used alone, or in combination with other features described, and may also be used in combination with one or more features of any other of the embodiments, or any combination of any other of the embodiments. Furthermore, equivalents and modifications not described above may also be employed without departing from the scope of the invention, which is defined in the accompanying claims.

Claims

1. A fluid transmission control system arranged to control the transmission of fluid through a fluid transmission network, the fluid transmission network comprising: a plurality of fluid sources; a fluid processing plant; and a plurality of upstream pipelines located upstream of the fluid processing plant, one or more of the upstream pipelines having a first end connectable to a fluid source and one or more of the upstream pipelines having a second end connectable to an input of the fluid processing plant; the fluid transmission control system comprising: measurement means for measuring one or more characteristics of fluid output from one or more of the plurality of fluid sources; prediction means for predicting, on the basis of the one or more measured characteristics, characteristics of the fluid at the second ends of the plurality of upstream pipelines; and processing determining means arranged to receive data regarding predicted characteristics of the fluid at the second ends of said plurality of upstream pipelines and to determine whether the fluid is suitable for processing by the fluid processing plant.

2. The system according to claim 1, the system further comprising adjustment determining means arranged to identify an adjustment to the fluid processing plant and/or the characteristics of fluid from the one or more fluid sources to account for said predicted characteristics; and control means arranged to control the fluid processing plant and/or the characteristics of fluid from the one or more fluid sources in accordance with the identified adjustment.

3. The system according to claim 2, wherein the adjustment determining means are arranged to alter the characteristics of fluid from the one or more fluid sources by altering fluid input means associated with the one or more fluid sources in accordance with the identified adjustment.

4. The system according to claim 2, wherein the adjustment determining means are arranged to instruct bypass of one or more processing components of the fluid processing plant by the fluid from the one or more fluid sources, whereby to effect the identified adjustment.

5. The system according to any of claims 1 to 4, wherein the prediction means for predicting characteristics of the fluid at the second ends of the plurality of upstream pipelines comprise first prediction means, and wherein the fluid transmission network further comprises one or more downstream pipelines located downstream of the fluid processing plant, one or more of the downstream pipelines having a first end connectable to an output of the fluid processing plant and one or more of the downstream pipelines having a second end connectable to a user's facility, the fluid transmission control system further comprising: simulation means for simulating operation of the fluid processing plant on the basis of the predicted characteristics of said fluid at the second ends of the plurality of upstream pipelines, so as to generate data indicative of predicted characteristics of fluid output from the simulated fluid processing plant; and second prediction means for predicting, on the basis of the predicted characteristics of the fluid output from the simulated fluid processing plant, characteristics of the fluid at the second ends of one or more of the downstream pipelines connectable to the user's facility, wherein the adjustment determining means are arranged to receive data regarding predicted characteristics of the fluid at the second ends of said one or more of the downstream pipelines connectable to the user's facility, and are further arranged to identify an adjustment to the fluid processing plant to account for said predicted characteristics; and wherein the control means are arranged to control the fluid processing plant in accordance with the identified adjustment.

6. The system according to claim 5, wherein the adjustment identified by the adjustment determining means effects a change to the characteristics of the fluid at the fluid processing plant such that the fluid flowing through said second ends of said one or more of the downstream pipelines connectable to the user's facility conforms to specified characteristics.

7. The system according to claim 5 or 6, wherein the control means are arranged to control natural gas liquids (NGL) removal means of the fluid processing plant, said NGL removal means being arranged to remove NGL from the fluid in accordance with the identified adjustment.

8. The system according to any of claims 5 to 7, wherein the control means are arranged to control pressure control means of the fluid processing plant, in order to increase or decrease the pressure of the fluid in accordance with the identified adjustment.

9. The system according to any preceding claim, wherein the measurement means comprise fluid chromatographic analysis means.

10. The system according to any of claims 2 to 9, wherein the adjustment determining means are further arranged to verify the identified adjustment.

11. The system according to any of claims 6 to 10, wherein the specified characteristics comprise a target characteristic value, or a target range of characteristic values, with which the characteristics of the fluid flowing through the second ends of said one or more of the downstream pipelines connectable to the user's facility must comply.

12. The system according to any of claims 6 to 11, wherein the adjustment determining means are arranged to assess whether values of the specified and predicted characteristics differ by more than a predetermined amount and, in the case that the values differ by more than the predetermined amount, to identify the adjustment as a required value of a said characteristic of fluid exported from the fluid processing plant.

13. The system according to any of claims 2 to 12, wherein the adjustment determining means are further arranged to calculate an action time at which to instruct the adjustment to the control means.

14. The system according to claim 13, wherein the system is arranged to instruct the control means with the adjustment at the action time.

15. The system according to claim 13 or 14, wherein the adjustment determining means are further arranged to verify the action time.

16. The system according to any preceding claim, further comprising fluid tracking means arranged to simulate fluid characteristics of the fluid flowing through the fluid transmission network in real time.

17. A method of controlling fluid transmission through a fluid transmission network, the fluid transmission network comprising: a plurality of fluid sources; a fluid processing plant; and a plurality of upstream pipelines located upstream of the fluid processing plant, one or more of the upstream pipelines having a first end connectable to a fluid source and one or more of the upstream pipelines having a second end connectable to an input of the fluid processing plant; the method comprising the steps of: measuring one or more characteristics of fluid output from one or more of the plurality of fluid sources; predicting, on the basis of the one or more measured characteristics, characteristics of the fluid at the second ends of the plurality of upstream pipelines; receiving data regarding predicted characteristics of the fluid at the second ends of said plurality of upstream pipelines; and determining whether the fluid is suitable for processing by the fluid processing plant.

18. The method according to claim 17, the method further comprising the steps of: identifying an adjustment to the fluid processing plant and/or the characteristics of fluid from the one or more fluid sources to account for said predicted characteristics; and controlling the fluid processing plant and/or the characteristics of fluid from the one or more fluid sources in accordance with the identified adjustment.

19. The method according to claim 18, the method further comprising the step of alter the characteristics of fluid from the one or more fluid sources by altering fluid input means associated with the one or more fluid sources in accordance with the identified adjustment.

20. The method according to claim 18, the method further comprising the step of instructing bypass of one or more processing components of the fluid processing plant by the fluid from the one or more fluid sources, whereby to effect the identified adjustment.

21. The method according to any of claims 17 to 20, wherein the fluid transmission network further comprises one or more downstream pipelines located downstream of the fluid processing plant, one or more of the downstream pipelines having a first end connectable to an output of the fluid processing plant and one or more of the downstream pipelines having a second end connectable to a user's facility, the method further comprising the steps of: simulating operation of the fluid processing plant on the basis of the predicted characteristics of said fluid at the second ends of the plurality of upstream pipelines, so as to generate data indicative of predicted characteristics of fluid output from the simulated fluid processing plant; predicting, on the basis of the predicted characteristics of the fluid output from the simulated fluid processing plant, characteristics of the fluid at the second ends of one or more of the downstream pipelines connectable to the user's facility; receiving data regarding predicted characteristics of the fluid at the second ends of said one or more of the downstream pipelines connectable to the user's facility; identifying an adjustment to the fluid processing plant to account for said predicted characteristics; and controlling the fluid processing plant in accordance with the identified adjustment.

22. The method according to claim 21, the method further comprising the step of identifying the adjustment in order to effect a change to the characteristics of the fluid at the fluid processing plant such that the fluid flowing through said second ends of said one or more of the downstream pipelines connectable to the user's facility conforms to specified characteristics.

23. The method according to claim 21 or 22, wherein the step of controlling the fluid processing plant comprises controlling the removal of natural gas liquids (NGL) from the fluid in accordance with the identified adjustment.

24. The method according to any of claims 21 to 23, wherein the step of controlling the fluid processing plant comprises increasing or decreasing the pressure of the fluid in accordance with the identified adjustment.

25. The method according to any of claims 17 to 24, wherein the step of measuring one or more characteristics of the fluid comprises performing chromatographic analysis or the fluid.

26. The method according to any of claims 18 to 25, further comprising the step of verifying the identified adjustment.

27. The method according to any of claims 22 to 26, wherein the specified characteristics comprise a target characteristic value, or a target range of characteristic values, with which the characteristics of the fluid flowing through the second ends of said one or more of the downstream pipelines connectable to the user's facility must comply.

28. The method according to any of claims 22 to 27, further comprising the steps of assessing whether values of the specified and predicted characteristics differ by more than a predetermined amount and, in the case that the values differ by more than the predetermined amount, identifying the adjustment as a required value of a said characteristic of fluid exported from the fluid processing plant.

29. The method according to any of claims 18 to 28, further comprising the step of calculating an action time at which to instruct the adjustment to the control means.

30. The method according to claim 29, further comprising the step of instructing the adjustment at the action time.

31. The method according to claim 29 or 30, further comprising the step of verifying the action time.

32. The method according to any of claims 17 to 31, further comprising the step of simulating fluid characteristics of the fluid flowing through the fluid transmission network in real time.