US-20260125977-A1 - Machine Learning Assisted Downhole Multiphase Flowmeter

US20260125977A1US 20260125977 A1US20260125977 A1US 20260125977A1US-20260125977-A1

Abstract

To characterize fluid flow in a conduit disposed in a wellbore, a system obtains a time series of flow values of the fluid flow measured in real-time using a flowmeter. The flow values at least include pressure, temperature, bulk velocity, and speed of sound. The system determines whether any of the flow values at each step in the time series is of low-quality having a quality below a threshold. Any other flow values in the step may be of high-quality. Using the high-quality values and a Long Short-Term Memory (LSTM) neural network, the system predicts a respective predicted value for each low-quality value at each step in the time series. Based on the respective predicted values and the high-quality values, the system calculates flow rates of multiphase components in the fluid flow at the at least one location.

Inventors

Bora Alican Unalmis
Omer Haldun Unalmis

Assignees

WEATHERFORD TECHNOLOGY HOLDINGS, LLC

Dates

Publication Date: 20260507
Application Date: 20241108

Claims (20)

1 . A method of characterizing fluid flow in a conduit disposed in a wellbore, the method comprising: obtaining, at a processing system, a time series of flow values of the fluid flow measured in real-time at at least one location in the conduit using at least one flowmeter; determining, at the processing system, that any given one or more of the flow values at any given time step in the time series is a low-quality value of low-quality, each of any remaining ones of the flow values at the given time step being a high-quality value of high-quality; predicting, at the processing system based on the high-quality values in the time series, a respective predicted value for each low-quality value at each given time step in the time series; and calculating, at the processing system based on the respective predicted values and the high-quality values, flow rates of multiphase components in the fluid flow at the at least one location.
2 . The method of claim 1 , wherein: obtaining the time series of the flow values comprises obtaining, at each time step in the time series, the flow values at least including a pressure value, a temperature value, a bulk velocity value, and a speed of sound (SoS) value associated with the fluid flow; and determining that the any given one or more of the flow values is of low-quality comprises determining that any of at least one of the bulk velocity value and the SoS value is of low-quality having a respective quality below a respective threshold.
3 . The method of claim 2 , wherein determining that the SoS value is of low-quality comprises determining that at least one of: (i) a speed of sound (SoS+) of waves opposite to a flow direction of the fluid flow and (ii) a speed of sound (SoS−) of waves in the flow direction has the respective quality below the respective threshold.
4 . The method of claim 1 , wherein calculating the flow rates of the multiphase components in the fluid flow comprises calculating a total flow rate and calculating phase flow rates of one or more of oil, water, and gas.
5 . The method of claim 1 , further comprising at least one of: adjusting well production based on the calculated flow rates; adjusting zonal production in a multi-zone well based on the calculated flow rates; adjusting one or more inflow control devices on the conduit in the wellbore based on the calculated flow rates; allocating production quantities based on the calculated flow rates; and preventing corrosion based on the calculated flow rates.
6 . The method of claim 1 , wherein determining comprises detecting a data gap in the time steps of the time series having the any given one or more of the flow values determined to be of low-quality.
7 . The method of claim 6 , wherein predicting the respective predicted value for each low-quality value at each given time step in the time series comprises forward modeling, in real-time processing of the time steps in the time series of the data gap, the respective predicted value for each low-quality value in each given time step.
8 . The method of claim 7 , wherein forward modeling comprises using a Long Short-Term Memory (LSTM) neural network.
9 . The method of claim 7 , wherein forward modeling comprises: encoding a memory cell, a hidden state, and a predicted value set based on the high-quality values in an input sequence in the time series before the data gap; and decoding, for each current time step from a start time step to a later time step in the time series of the data gap, by successively performing the acts of: inputting a current value set of the flow values for the current time step; inputting a predicted value set forwarded from a previous time step; producing an updated value set by replacing each low-quality value of the current value set with each predicted value of the forwarded predicted value set; updating the memory cell and the hidden state based on the updated value set; and outputting a predicted value set for forwarding to a successive time step up until completion.
10 . The method of claim 6 , wherein predicting the respective predicted value for each low-quality value at each time step in the time series comprises backward modeling, in post processing of the time steps in the time series of the data gap, the respective predicted value for each low-quality value in each given time step.
11 . The method of claim 10 , wherein backward modeling comprises using a Long Short-Term Memory (LSTM) neural network.
12 . The method of claim 10 , wherein backward modeling comprises: encoding a memory cell, a hidden state, and a predicted value set based on the high-quality values in an input sequence in the time series after the data gap; and decoding, for each current time step from a last time step to a first time step in the time series of the data gap, by successively performing the acts of: inputting a current value set of the flow values for the current time step; inputting a predicted value set back fed from a later time step; producing an updated value set by replacing each low-quality value of the current value set with each predicted value of the back fed predicted value set; updating the memory cell and the hidden state based on the updated value set; and outputting a predicted value set for back feeding to an earlier time step up until completion.
13 . The method of claim 1 , wherein calculating the flow rates of the multiphase components in the fluid flow being oil/water two-phase (liquid/liquid) flow comprises: calculating a speed of sound (SoS) in an infinite medium based on the Wood equation, the Korteweg-Lamb equation, and first parameters, the first parameters including a temperature value and a pressure value from the flow values and including an SoS parameter, the SoS parameter being the respective predicted value for an SoS value being of low quality, otherwise the SoS parameter being the SoS value of high-quality; and determining a water-in-liquid ratio (WLR) of the fluid flow at the at least one location based on the SoS in the infinite medium, the first parameters, and single-phase properties of the multiphase components in the fluid flow.
14 . The method of claim 13 , wherein calculating the flow rates comprises: calculating a total flow rate (Q total ) based on the bulk velocity parameter, the bulk velocity parameter being the respective predicted value for a bulk velocity value being of low quality, otherwise the bulk velocity parameter being the bulk velocity value of high-quality; and calculating phase flow rates (Q oil , Q water ) for the oil/water two-phase (liquid/liquid) flow based on the determined WLR and the bulk velocity parameter, the bulk velocity parameter being the respective predicted value for the bulk velocity value being of low quality, otherwise the bulk velocity parameter being the bulk velocity value of high-quality.
15 . The method of claim 13 , further comprising determining the single-phase properties of the multiphase components of the fluid flow based on an analysis of a bottomhole fluid sample.
16 . The method of claim 1 , wherein calculating the flow rates of the multiphase components in the fluid flow being oil/gas two-phase (gas/liquid) flow comprises: calculating a speed of sound (SoS) in an infinite medium based on the Wood equation, the Korteweg-Lamb equation, and first parameters, the first parameters including a temperature value and a pressure value from the flow values and including an SoS parameter, the SoS parameter being the respective predicted value for an SoS value being of low quality, otherwise the SoS parameter being the SoS value of high-quality; and determining a liquid volume fraction (LVF) of the fluid flow at the at least one location based on the SoS in the infinite medium, the first parameters, and single-phase properties of the multiphase components in the fluid flow.
17 . The method of claim 16 , wherein calculating the flow rates comprises: calculating a total flow rate (Q total ) based on the bulk velocity parameter, the bulk velocity parameter being the respective predicted value for a bulk velocity value being of low quality, otherwise the bulk velocity parameter being the bulk velocity value of high-quality; and calculating phase flow rates (Q gas , Q oil ) for the gas/oil two-phase (gas/liquid) flow based on the determined LVF and the bulk velocity parameter, the bulk velocity parameter being the respective predicted value for the bulk velocity value being of low quality, otherwise the bulk velocity parameter being the bulk velocity value of high-quality.
18 . The method of claim 16 , further comprising determining the single-phase properties of the multiphase components of the fluid flow based on an analysis of a bottomhole fluid sample.
19 . The method of claim 1 , further comprising sensing the flow values by using an optical flowmeter for the at least one flowmeter.
20 . The method of claim 19 , wherein sensing the flow values further comprises using a distributed acoustic sensing (DAS) system, the DAS system being communicatively coupled to a first optical waveguide of the optical flowmeter or having a second optical waveguide separate from the optical flowmeter.

Description

This application claims the benefit of U.S. Provisional Appl. 63/717,338 filed Nov. 7, 2024, which is incorporated herein by reference. BACKGROUND OF THE DISCLOSURE In the petroleum industry, as in many other industries, the ability to monitor flow of fluids in process pipes in real-time offers considerable value. Oil and gas operators measure individual oil, water, and/or gas flow rates within an overall production flow stream containing a mixture of these three phase components. This information may be used to improve and optimize well production, allocate royalties, prevent corrosion based on the amount of water, and/or determine the well performance. Production from wells can vary over time, and production tends to reduce as the flow rate decreases. Various flowmeters in the art may provide flow rate measurements or determinations as long as the flow rates are above a sufficient velocity. For example, determining the phase flow rates in a 3-phase flow may use several measurements, including measuring speed of sound (SoS). Distributed acoustic sensing (DAS) is one technology that may be used in measuring flow in wells. A DAS system is usually capable of measuring SoS. Additionally, a DAS system may also be capable of measuring flow velocity depending on its installation, configuration, and the type of application. In another example, an in-well optical flowmeter (OFM) may be used to accurately measure 1- and 2-phase flows by measuring flow velocity and speed of sound (SoS). Such an OFM is typically unable to accurately measure 3-phase flows without the use of secondary pressure and temperature sensors, which may be separated from the optical flowmeter, and which are used to predict the density of the fluid mixture. The measurements of flow velocity, the SoS, and the mixture density are sufficient for solving 3-phase flows. However, the 3-phase solution from such a system has been found to be inaccurate when the velocity decreases or the SoS measurement fades out as production rates decline. Although current techniques may be useful, the subject matter of the present disclosure is directed to improving measurements and analysis for a downhole multiphase flowmeter. SUMMARY OF THE DISCLOSURE According to the present disclosure, a method is directed to characterizing fluid flow in a conduit disposed in a wellbore. The method comprises: obtaining, at a processing system, a time series of flow values of the fluid flow measured in real-time at at least one location in the conduit using at least one flowmeter; determining, at the processing system, that any given one or more of the flow values at any given time step in the time series is a low-quality value of low-quality, each of any remaining ones of the flow values at the given time step being a high-quality value of high-quality; predicting, at the processing system based on the high-quality values in the time series, a respective predicted value for each low-quality value at each given time step in the time series; and calculating, at the processing system based on the respective predicted values and the high-quality values, flow rates of multiphase components in the fluid flow at the at least one location. In the method, obtaining the time series of the flow values can comprise obtaining, at each time step in the time series, the flow values at least including a pressure value, a temperature value, a bulk velocity value, and a speed of sound (SoS) value associated with the fluid flow. Also, determining that the any given one or more of the flow values is of low-quality can comprise determining that any of at least one of the bulk velocity value and the SoS value is of low-quality having a respective quality below a respective threshold. In this instance, determining that the SoS value is of low-quality can comprise determining that at least one of: (i) a speed of sound (SoS+) of waves opposite to a flow direction of the fluid flow and (ii) a speed of sound (SoS−) of waves in the flow direction has the respective quality below the respective threshold. In the method, calculating the flow rates of the multiphase components in the fluid flow can comprise calculating a total flow rate and calculating phase flow rates of one or more of oil, water, and gas. The method can further comprise at least one of: adjusting well production based on the calculated flow rates; adjusting zonal production in a multi-zone well based on the calculated flow rates; adjusting one or more inflow control devices on the conduit in the wellbore based on the calculated flow rates; allocating production quantities based on the calculated flow rates; and preventing corrosion based on the calculated flow rates. In a first arrangement of the method, determining the quality can comprise detecting a data gap in the time steps of the time series having the any given one or more of the flow values determined to be of low-quality. For instance, predicting the respective predicted value for eac