System and Method for Flow Rate Data Determination

As oil, gas, and water are produced from a well, they typically flow as a non-homogeneous mixture of phases through a pipeline from the wellhead to a separator. A multiphase flow meter (MPFM) is a device that may be installed on a pipeline to measure the rate at which each phase (oil, gas, water) is flowing. Multiphase flow rate measurements are essential for reservoir monitoring and play a significant role in production optimization from oil and gas fields, especially in an offshore environment.

Virtual Flow Metering (VFM) techniques build models from the time series of sensor data from sensors on the pipeline to infer the flow rates. VFM models may be used as a standalone solution of multiphase flow rate monitoring, or in a combination with a multiphase flow meter (MPFM) as a back-up system such that it can use the information from a MPFM to further improve the flowrate estimates. However, the performance of VFM models heavily relies on the quality of the ground truth labels, i.e., the target measurements, such as oil flow rate, used during training.

Ideally, the model-building phase of a VFM uses a clean dataset with minimum error and no missing data points. However, this is only possible using synthetic datasets, and almost impossible when using real-world datasets from the sensors of the pipeline, due to the imperfection of the sensors and data conditioning. For example, a reference physical flow meter in the pipeline may sometimes malfunction and produce null readings or extreme readings, or even be offline and produce no measurements. In addition, the database for storing the reference physical flow meter readings may miss some readings or record wrong values. In fact, the data preparation and cleansing take significant effort from both domain experts and data scientists to acquire a good dataset for use in VFM model building and training.

Therefore, it is desirable to establish an efficient and robust method and system that can analyze, quality control and recover high quality flow rate data from the noisy meter readings in the raw database for use in VFM model building and training.

This summary is provided to introduce a selection of concepts that are further described below in the detailed description. This summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used as an aid in limiting the scope of the claimed subject matter.

In one aspect, embodiments disclosed herein relate to a method. The method includes obtaining raw multiphase flow rate data from a first set of sensors disposed on a pipeline of a well and obtaining auxiliary data from a second set of sensors disposed on the pipeline. The method further includes preprocessing the raw multiphase flow rate data to form a preprocessed multiphase flow rate dataset, and determining, with a first model processing the preprocessed multiphase flow rate dataset, a first cleansed flow rate dataset. The method further includes determining, with a second model processing the preprocessed multiphase flow rate dataset and the auxiliary data, a second cleansed flow rate dataset, and determining, with a third model processing the preprocessed multiphase flow rate dataset, the first cleansed flow rate dataset, and the second cleansed flow rate dataset, high quality flow rate data. The method further includes transmitting the high quality flow rate data to, at least, a control system of the well, wherein operation of the well is based on the high quality flow rate data.

In one aspect, embodiments disclosed herein relate to a system. The system includes a first set of sensors disposed on a pipeline of a well, a second set of sensors disposed on the pipeline, a set of models, comprising a first model, a second model and a third model; and a computer. The computer is configured to obtain raw multiphase flow rate data from a first set of sensors disposed on a pipeline of a well and obtain auxiliary data from a second set of sensors disposed on the pipeline. The computer is further configured to preprocess the raw multiphase flow rate data to form a preprocessed multiphase flow rate dataset, and determine, with a first model processing the preprocessed multiphase flow rate dataset, a first cleansed flow rate dataset. The computer is further configured to determine, with a second model processing the preprocessed multiphase flow rate dataset and the auxiliary data, a second cleansed flow rate dataset, and determine, with a third model processing the preprocessed multiphase flow rate dataset, the first cleansed flow rate dataset, and the second cleansed flow rate dataset, high quality flow rate data. The computer is further configured to transmit the high quality flow rate data to, at least, a control system of the well, wherein operation of the well is based on the high quality flow rate data.

In one aspect, embodiments herein relate to a non-transitory machine-readable medium comprising a plurality of machine-readable instructions executed by one or more processors. The plurality of machine-readable instructions cause the one or more processors to perform a method. The method includes obtaining raw multiphase flow rate data from a first set of sensors disposed on a pipeline of a well and obtaining auxiliary data from a second set of sensors disposed on the pipeline. The method further includes preprocessing the raw multiphase flow rate data to form a preprocessed multiphase flow rate dataset, and determining, with a first model processing the preprocessed multiphase flow rate dataset, a first cleansed flow rate dataset. The method further includes determining, with a second model processing the preprocessed multiphase flow rate dataset and the auxiliary data, a second cleansed flow rate dataset, and determining, with a third model processing the preprocessed multiphase flow rate dataset, the first cleansed flow rate dataset, and the second cleansed flow rate dataset, high quality flow rate data. The method further includes transmitting the high quality flow rate data to, at least, a control system of the well, wherein operation of the well is based on the high quality flow rate data.

Other aspects and advantages of the claimed subject matter will be apparent from the following description and the appended claims.

In the following detailed description of embodiments of the disclosure, numerous specific details are set forth in order to provide a more thorough understanding of the disclosure. However, it will be apparent to one of ordinary skill in the art that the disclosure may be practiced without these specific details. In other instances, well-known features have not been described in detail to avoid unnecessarily complicating the description.

Throughout the application, ordinal numbers (e.g., first, second, third, etc.) may be used as an adjective for an element (i.e., any noun in the application). The use of ordinal numbers is not to imply or create any particular ordering of the elements nor to limit any element to being only a single element unless expressly disclosed, such as using the terms “before,” “after,” “single,” and other such terminology. Rather, the use of ordinal numbers is to distinguish between the elements. By way of an example, a first element is distinct from a second element, and the first element may encompass more than one element and succeed (or precede) the second element in an ordering of elements.

It is to be understood that the singular forms “a,” “an,” and “the” include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to an “earth property” can include reference to one or more of such earth properties.

Terms such as “approximately,” “substantially,” etc., mean that the recited characteristic, parameter, or value need not be achieved exactly, but that deviations or variations, including for example, tolerances, measurement error, measurement accuracy limitations and other factors known to those of skill in the art, may occur in amounts that do not preclude the effect the characteristic was intended to provide.

It is to be understood that one or more of the steps shown in the flowchart may be omitted, repeated, and/or performed in a different order than the order shown. Accordingly, the scope disclosed herein should not be considered limited to the specific arrangement of steps shown in the flowchart.

Although multiple dependent claims are not introduced, it would be apparent to one of ordinary skill that the subject matter of the dependent claims of one or more embodiments may be combined with other dependent claims.

In the following description of, any component described with regard to a figure, in various embodiments disclosed herein, may be equivalent to one or more like-named components described with regard to any other figure. For brevity, descriptions of these components will not be repeated with regard to each figure. Thus, each and every embodiment of the components of each figure is incorporated by reference and assumed to be optionally present within every other figure having one or more like-named components. Additionally, in accordance with various embodiments disclosed herein, any description of the components of a figure is to be interpreted as an optional embodiment which may be implemented in addition to, in conjunction with, or in place of the embodiments described with regard to a corresponding like-named component in any other figure.

Virtual flow metering (VFM) models are an attractive way of monitoring multiphase flow, e.g., the flow of oil, gas and water phases in a pipeline, as they can leverage the sensor measurements from sensors already present on the pipeline without requiring additional parts or maintenance. VFM models can be used as a stand-alone solution, or in a combination with a multiphase flow meter (MPFM) as a back-up system such that it can use the information from the MPFM to further improve the flowrate estimates. In general, embodiments disclosed herein relate to methods and systems to provide high quality data for training Virtual Flow Metering (VFM) models, and for determining flowrates using the trained VFM model.

In accordance with one or more embodiments,depicts a simplified portion of a pipeline () of a multilateral well in an oil and gas field. Herein, an oil and gas field is broadly defined to consist of wells which produce at least some oil and/or gas. Hydrocarbon wells typically produce oil, gas, and water in combination. The relative amounts of oil, gas, and water may differ between wells and vary over any one well's lifetime.

For clarity, the pipeline () is divided into three sections; namely, a subsurface () section, a tree () section, and a flowline () section. It is emphasized that pipelines () and other components of wells and, more generally, oil and gas fields may be configured in a variety of ways. As such, one with ordinary skill in the art will appreciate that the simplified view ofdoes not impose a limitation on the scope of the present disclosure. As part of the subsurface () section,shows an inflow control valve (ICV) (). An ICV () is an active component usually installed during well completion. The ICV () may partially or completely choke flow into a well. Generally, multiple ICVs () are installed along the reservoir section of a wellbore. Each ICV () is separated from the next by a packer. Each ICV () can be adjusted and controlled to alter flow within in the well and, as the reservoir depletes, prevent unwanted fluids from entering the wellbore.

The subsurface () section of the pipeline () has a subsurface safety valve (SSSV) (). The SSSV () is designed to close and completely stop flow in the event of an emergency. Generally, an SSSV () is designed to close on failure. That is, the SSSV () requires a signal to stay open and loss of the signal results in the closing of the valve. Also shown as part of the subsurface () section is a permanent downhole monitoring system (PDHMS) (). The PDHMS () consists of a plurality of sensors, gauges, and controllers to monitor subsurface flowing and shut-in pressures and temperatures. As such, a PDHMS () may indicate, in real-time, the state or operating condition of subsurface equipment and the fluid flow.

Turning to the tree () section ofis a master valve (MV) (), a surface safety valve (SSV) (), and a wing valve (WV) (). The MV () controls all flow from the wellbore. For safety considerations, a MV () is usually considered so important that two master valves (MVs) (second not shown) are used wherein one acts as a backup. Like unto the SSSV (), the SSV () is a valve installed on the upper portions of the wellbore to provide emergency closure and stoppage of flow. Again, SSVs () are designed to close on failure. One or more WVs () may be located on the side of the tree () section, or on temporary surface flow equipment (not shown). WVs () may be used to control and isolate production fluids and/or be used for treatment or well-control purposes.

Also shown inis a control valve (CV) () and a pressure gauge (PG) (). The CV () is a valve that controls a process variable, such as pressure, flow, or temperature, by modulating its opening. The PG () monitors the fluid pressure at the tree () section.

Turning to the flowline () section, the flowline () transports () the fluid from the well to a storage or processing facility (not shown). A choke valve () is disposed along the flowline (). The choke valve () is used to control flow rate and reduce pressure for processing the extracted fluid at a downstream processing facility. In particular, effective use of the choke valve () prevents damage to downstream equipment and promotes longer periods of production without shut-down or interruptions. The choke valve () is bordered by an upstream pressure transducer () and a downstream pressure transducer () which monitor the pressure of the fluid entering and exiting the choke valve (), respectively. The flowline () shown inhas a block and bleed valve system () which acts to isolate or block the flow of fluid such that it does not reach other downstream components. The flowline () may also be outfitted with one or more temperature sensors ().

The various valves, pressure gauges and transducers, and sensors depicted inmay be considered field devices of an oil and gas field. As shown, these field devices may be disposed both above and below the surface of the Earth. These field devices are used to monitor and control components and sub-processes of an oil and gas field. It is emphasized that the oil and gas field devices depicted inare non-exhaustive. Additional devices, such as electrical submersible pumps (ESPs) (not shown) may be present in an oil and gas field with their associated sensing and control capabilities. For example, an ESP may monitor the temperature and pressure of a fluid local to the ESP and may be controlled through adjustments to ESP speed or frequency.

The field devices may be distributed, local to the sub-processes and associated components, global, connected, etc. The field devices may be of various control types, such as a programmable logic controller (PLC) or a remote terminal unit (RTU). For example, a programmable logic controller (PLC) may control valve states, pipe pressures, warning alarms, and/or pressure releases throughout the oil and gas field. In particular, a programmable logic controller (PLC) may be a ruggedized computer system with functionality to withstand vibrations, extreme temperatures, wet conditions, and/or dusty conditions, for example, around a pipeline (). With respect to an RTU, an RTU may include hardware and/or software, such as a microprocessor, that connects sensors and/or actuators using network connections to perform various processes in the automation system. As such, a distributed control system may include various autonomous controllers (such as remote terminal units) positioned at different locations throughout the oil and gas field to manage operations and monitor sub-processes. Likewise, a distributed control system may include no single centralized computer for managing control loops and other operations.

In accordance with one or more embodiments,depicts the virtual flowmeter (VFM) model (). The VFM model includes functionality for field device monitoring and data collection, and to estimate multiphase flow rates from field device data (i.e., a VFM estimate), so as to continuously provide accurate multiphase flow rate measurements regardless of the state of any MPFM. To emphasize that the VFM model () can monitor the various field devices of a well and/or an oil and gas field, dashed lines connecting various field devices to the VFM model () are shown in.

depicts a simplified view of a cross-section of a flowline () carrying a multiphase fluid. As seen, the multiphase fluid may have multiple constituents such as gas (), water (), and oil (). The various constituents of the multiphase fluid may be distributed within the flowline () in a myriad of ways. As a non-limiting example, gas () may be enclosed by liquids (water or oil) forming bubbles (). Or, in contrast, liquid droplets, such as oil droplets () and water droplets (), may be dispersed in the gas () to form a mist. In general, the state of the multiphase fluid may be described using broad classifications. That is, the multiphase fluid may be categorized as “bubbly,” “annular,” “churn,” “mist,” “stratified,” or other designations (flow classes) based on the distribution of the constituents and their relative quantities. The state of the multiphase fluid may be transient such that any assignment of flow class may change with time.

Oil and gas field devices, like those shown in(and others not shown), monitor and govern the behavior of the components and sub-processes of the well and/or the oil and gas field. Therefore, the productivity of the well and/or the oil and gas field is directly affected, and may be altered by, at least some, of the field devices. Generally, complex interactions between oil and gas field components and sub-processes exist such that configuring field devices for optimal production is a difficult and laborious task. Further, the state and behavior of oil and gas fields is transient over the lifetime of the constituent wells requiring continual changes to the field devices to enhance production.

To inform and optimize the settings of the field devices of a pipeline () to maximize hydrocarbon production, it is beneficial, if not critical, to determine the instantaneous state of the multiphase flow. To this end, the pipeline () depicted inis outfitted with a multiphase flow meter (MPFM) (). A MPFM () is a device installed on the flowline () to measure the rate at which each phase—oil, gas, water—is flowing. That is, the MPFM () may detect the instantaneous amount of gas, oil, and water flowing in the pipeline (). As such, the MPFM () indicates additional quantities such as percent water cut (% WC) and the gas-to-oil ratio (GOR).

In general, a MPFM () cannot directly measure the flow rate of the individual phases in a fluid. Rather, a MPFM () is a collection of sensors, transmitters, mechanical devices, flow conduits, and programmed relationships that are used to determine the individual phase flow rates.depicts a MPFM () in accordance with one or more embodiments. A MPFM () may be disposed inline with a flowline (), or proximate to a flowline (), such that the MPFM () can receive the multiphase fluid () through a flow inlet () and return the fluid through a flow outlet (). The MPFM () ofincludes one or more pressure sensors () and temperature sensors () to measure the pressure (P) and temperature (T), respectively, at various locations within the MPFM (). The pressure (P) and temperature (T) measurements are used, in part, to determine the thermophysical state and the thermophysical properties of the multiphase fluid (). For example, the pressure (P) and temperature (T) values may be used with a functional or tabulated equation of state (EoS) to determine other properties of the multiphase fluid ().

The MPFM () ofuses a gamma densitometer () to measure the bulk density of the multiphase fluid (). The gamma densitometer () emits a beam of photons from a nuclear source (). The emitted photons are attenuated by the multiphase fluid () and the amount of attenuation is determined using a nuclear detector () that measures the number of received photons. The amount of attenuation is greatly affected by the bulk density of the multiphase fluid (). Further, because gas has a significantly lower density compared to water and oil, the gamma densitometer () can be used to accurately determine the liquid (water and oil) and gas fractions of the multiphase fluid ().

The MPFM () depicted infurther includes a Venturi section. The Venturi section is composed of a Venturi inlet (), a nozzle (), a throat section (), and a diffuser (). The constriction in flow that occurs in the Venturi section acts to increase the bulk flow velocity of the multiphase fluid () and is associated with a decrease in pressure (P). The MPFM () is configured with pressure sensors () to determine the difference in pressure (DP) () of the multiphase fluid () between the Venturi inlet () and the throat section (). The Venturi section is used to measure mass flow rates.

The MPFM () offurther includes a blind tee or static mixer (). The blind tee () serves to condition the flow of the multiphase fluid () such that it is homogeneous before entering the main body of the MPFM (). The blind tee () is disposed immediately upstream of the Venturi section and creates a mixing effect that stabilizes intermittent flow regimes commonly encountered in multiphase fluids () associated with wells. The blind tee () also mitigates any flow interaction with field devices located upstream from the MPFM () such as the choke valve ().

The MPFM () is outfitted with a flow computer (). The flow computer () receives the readings from the sensors (e.g., temperature sensor (), pressure sensor ()) of the MPFM (). That is, the flow computer () acts, in part, as a data acquisition unit. Upon collecting the sensor data, the flow computer () calculates the individual flow rates of the oil, gas, and water present in the multiphase fluid (). The flow computer () can transmit the computed flow rates and acquired sensor data to an external system such as the VFM model (). Generally, the flow computer () makes use of programmed relationships to determine the phase flow rates from the acquired sensor data. The programmed relationships may include or make use of analytical or tabulated equations of state (EoS) data, phenomenological models, physics-based relationships, bounded correlations, and governing equations (e.g., conservation of mass). In one or more embodiments, the flow computer () may be a computer system as depicted in, which will be described in greater detail later in the disclosure.

It is emphasized that the MPFM () depicted inis provided only as an example. In practice, many different types of MPFMs () exist. MPFMs () may differ in the types of sensors used, the data they collect, and the programmed relationships used to convert the measured flow properties to phase flow rates. For example, in some instances, a MPFM () may further be outfitted with a capacitance sensor to determine the fraction of oil, water, and gas in the multiphase fluid (). In other instances, a MPFM () may use any combination of an X-ray source and detector, electrodes, strain gauges, magnetic resonance, and optical sensors and computer vision-based algorithms. One with ordinary skill in the art will recognize that the above description of a MPFM () or the components that may make up a MPFM () are non-exhaustive and should not be construed to impose a limitation on the instant disclosure.

In general, the VFM model () disclosed herein can work with any type of MPFM (). In accordance with one or more embodiments, there may be a VFM model () for each MPFM (). Again, for clarity, the VFM model () will be discussed herein in reference to a single MPFM (). In general, a VFM model () operates by using one or more numerical models to estimate the individual phase flow rates of a multiphase fluid using readily available field device data. In accordance with one or more embodiments, the VFM model () receives VFM inputs (inputs from field devices) and processes the VFM inputs with a model to determine the individual phase flow rates. When the phase flow rates are estimated using the VFM model (), they are often referred to as VFM model determined phase flow rates.

Likewise, to train the VFM model (), modelling data is required which consists of pairs of VFM inputs (inputs from field devices) and the associated phase flow rates as the targets. In accordance with one or more embodiments, the associated phase flow rates are high quality flow rate data obtained by processing the raw multiphase flow rate data obtained from a set of sensors, such as the MPFM, as discussed in detail below. In accordance with one or more embodiments, the VFM inputs are auxiliary data from a set of field devices, where the auxiliary data are data that are not flow rate, and include, but are not limited to: wellhead pressure, upstream wellhead temperature, downstream wellhead pressure, Venturi differential pressure, choke valve position, ESP frequency, and ESP motor current. The VFM inputs are collected using field devices appropriately disposed on the pipeline ().

In other words, in general, the modelling data consists of the expected input and desired output for the machine-learned model. In accordance with one or more embodiments, the modelling data is acquired from one or more existing pipelines or from previously collected historical pipeline data.

illustrates a system () for providing data suitable for training a VFM model (), according to one or more embodiments. It is noted that the elements shown inare abstractions and that, in practice, an element may not be unique or independent from other elements of the system. Further, the functionality of one or more elements may be shared between any number of elements. The system () ofacquires raw multiphase flow rate data () and produces high quality flow rate data (), through the use of a first model (), a second model (), and a third model (). As will be further described below, the first model (), the second model () and the third model () are not VFM models. The purpose of the first model (), the second model () and the third model () is to produce high quality flow rate data () from raw multiphase flow rate data (), so that the high quality flow rate data () can be used by VFM model building () to build a VFM model ().

Raw multiphase flow rate data () are acquired, for example for a first time period T, where Tis a historical time period.illustrates that the raw multiphase flow rate data () is acquired from a database (). According to one or more embodiments, the raw multiphase flow rate data () in database () has been acquired from a MPFM () installed on a pipeline (). Alternatively, according to other embodiments, the raw multiphase flow rate data () may be acquired directly from a flowmeter, such as the MPFM (), without being stored in the database ().

According to one or more embodiments, the raw multiphase flow rate data () is pre-processed by at least one pre-processing algorithm () to perform a myriad of pre-processing steps. According to one or more embodiments, more than one pre-processing algorithm () may be combined together to form a single pre-processing algorithm. According to one or more embodiments, the pre-processing algorithms () may be used to preform basic data cleansing or imputation to make the raw multiphase flow rate data () suitable for use with the first model (), the second model () and/or the third model (). These pre-processing steps may include, but are not limited to, outlier detection and replacement of outliers, imputation, and normalization. One with ordinary skill in the art will recognize that many pre-processing (or processing) steps exist for dealing with a raw multiphase flow rate data (). As such, one with ordinary skill in the art will appreciate that not all pre-processing (or processing) steps can be enumerated herein and that zero or more pre-processing (or processing) steps may be applied with the methods disclosed herein without imposing a limitation on the instant disclosure.

According to one or more embodiments, the pre-processing (or processing) steps are optional depending on a type of model used for at least one of the models (,,). Some examples of when a pre-processing algorithm () may be included in the system () are given here, and are not intended as imposing a limitation on the instant disclosure. Gaussian autoregressive (AR) models usually require the normally distributed error of the data. Hence, the raw multiphase flow rate data () may be normalized prior to being provided as an input to an AR model. As another example, Decline Curve Analysis (DCA) models expect the input data, to follow certain patterns, such as exponential decline. Therefore, if a DCA model is being used for one of the models (,,) the raw multiphase flow rate data () may be pre-processed to meet this requirement. Alternatively, some other modeling techniques (e.g. moving average smoothing) do not have such requirements, and therefore the pre-processing steps are optional.

According to one or more embodiments, the pre-processing algorithms (,) may be used to process the raw multiphase flow rate data () using both domain knowledge from subject matter experts and model knowledge from data scientists. The pre-processing algorithms () may further include exploratory data analysis (EDA) and data quality and control by a combined effort from subject matter experts and data scientists.

According to one or more embodiments, the pre-processing algorithms (,) output a preprocessed multiphase flow rate dataset () for use by the plurality of models (,,).

The preprocessed multiphase flow rate dataset () is provided as input to a first model (). The first model (), and the data used to train it, will be described in greater detail later in the instant disclosure. However, for now, it is stated that the first model () is configured to receive the preprocessed multiphase flow rate dataset () and, upon processing, output a first cleansed flow rate dataset ().

According to one or more embodiments, the first model () is a classical model, where a classical model is defined herein as a model that uses past flow rates to predict future flow rates. The first model () is described further below.

The preprocessed multiphase flow rate dataset () is also provided as input to a second model (). The second model (), and the data used to train it, will be described in greater detail later in the instant disclosure. However, for now, it is stated that the second model () is configured to receive the preprocessed multiphase flow rate dataset () and auxiliary data () from field devices () for the same time period Tand, upon processing, output a second cleansed flow rate dataset (). According to one or more embodiments, the field devices () are disposed on the pipeline (), as discussed previously with respect to. According to one or more embodiments, the field devices () are any sensors that do not directly return a flow rate. As detailed previously, the field devices () can include temperature and pressure sensors (on the flowline or downhole), ESP frequency, or even sensor data (e.g., temperature and pressure) originating from the MPFM, if the MPFM returns those values in addition to the flow rate measurements.

According to one or more embodiments, the second model () is a virtual sensing model, as described further below.

The preprocessed multiphase flow rate dataset (), the first cleansed flow rate dataset () and the second cleansed flow rate dataset () are provided as inputs to a third model (). The third model (), and the data used to train it, will be described in greater detail later in the instant disclosure. However, for now, it is stated that the third model () is configured to receive the preprocessed multiphase flow rate dataset (), and upon processing, infer high quality flow rate data () from the preprocessed multiphase flow rate dataset (), where the third model () is guided by the first cleansed flow rate dataset () and the second cleansed flow rate dataset ().

According to one or more embodiments, the third model () is a guided model, as described in detail below. According to one or more embodiments, the third model () performs an average or a weighted average of the preprocessed multiphase flow rate dataset (), the first cleansed flow rate dataset (), and the second cleansed flow rate dataset () to determine the high quality flow rate data (). The third model () is discussed further below.