Methods and Systems for Interpreting Multiphase Fluid Flow in A Conduit

Abstract

Systems and methods are disclosed for interpreting measurements by a meter of defined dimension in a multiphase flow in a conduit in a wellbore. The meter may be a spinner used to obtain measurements at locations in the flow. The measurements may be averaged across a defined dimension, and a measurement model generated. A parameter profile (e.g., velocity) can be generated by curve fitting to the model.

Description

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No. 61/447,174, filed Feb. 28, 2011, U.S. Provisional Application No. 61/426,625, filed Dec. 23, 2010, and U.S. Provisional Application No. 61/426,640, filed Dec. 23, 2010, the entire disclosure of each application is incorporated herein by reference.

This application relates to U.S. Application titled “APPARATUS AND METHOD FOR GENERATING STEAM QUALITY DELIVERED TO A RESERVOIR” and filed Dec. 4, 2011 with Attorney Docket Number 21.1948 US and U.S. Application titled “SYSTEMS AND METHODS FOR INTERPRETING MULTI-PHASE FLUID FLOW DATA” and filed Dec. 4, 2011 with Attorney Docket Number 21.1949 US, the entire disclosure of each application is incorporated herein by reference.

BACKGROUND

Multi-phase fluid flow may have at least two phases: a fluid phase (e.g. oil phase) and a vapor phase (e.g. gas). In some cases, a multi-phase fluid flow may have three phases: a heavy fluid phase (e.g. aqueous phase), a light fluid phase (e.g. oil phase) and a vapor phase. Multi-phase fluid flow in a conduit can be encountered in industries, such as petrochemical plants, oil/gas production fields, and food stuff processing plants. It may be useful to measure and interpret characteristics of multi-phase fluid flow.

Multi-phase fluid flows may be encountered in the oil/gas production industry. In an oil/gas production context, various flow regimes reflective of various multi-phase fluid flows may be found, such as stratified flow, wavy stratified flow, plug flow, slug flow, dispersed bubble flow, and annular flow. Annular flow refers to gas flowing at high velocities in a center of a borehole with a fluid confined to a thin film on walls of the borehole. Stratified flow can occur in horizontal wells when two or more phases are separated due to gravity. Wavy flow can result in stratified systems when interference occurs between the two phases traveling at different velocities.

Factors influencing the flow regimes can include a degree of borehole deviation and proportion of each phase; relative differences in phase densities, surface tension and viscosity of each phase; and average velocity. In a fluid-gas system, when small bubbles of gas are uniformly distributed, the flow regime can be referred to as dispersed bubble flow. When, some of these bubbles aggregate to form larger bubbles, plug flow and slug flow can result.

Understanding the multi-phase fluid flow regime in a conduit or pipe may be used to understand how a production well is performing. A production log records one or more in-situ measurements that describe the nature and behavior of fluids in or around the borehole during a production operation, including an injection operation. Production logs can provide, for example, information about dynamic well performance, and the productivity or injectivity of different zones. This information may be used to help diagnose problem wells, or monitor the results of a stimulation or completion.

Various downhole tools can be used for making downhole measurements used to produce logs, including flowmeters (e.g., spinners), local probes, nuclear logging tools, phase-velocity logging tools, production logging sensors, meters, etc. Downhole tools may be used to measure various downhole parameters, such as temperature, flow rate, density, phase velocity, phase holdup, mixture density, mixture velocity, water (or liquid) holdup, water velocity, gas holdup, and the like.

SUMMARY

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 an embodiment, a method is disclosed for interpreting measurements by a meter of defined dimension in a multiphase flow in a conduit. The method can include obtaining a measurement with the meter at each of a plurality of locations in the multiphase flow, each measurement representing an average of a flow parameter across a defined dimension of the meter. The method can include generating a measurement model as a function of the defined dimension of the meter and the average of the flow parameter represented by the measurements. The method can include generating a parameter profile of the flow parameter by fitting a curve to the measurement model.

In another embodiment, a method is disclosed for interpreting production logging data. The method can include measuring a flow parameter, such as flow velocity, with a sensor (or meter) that averages the velocity over a dimension of investigation. The method can include providing a sensor response model including the dimension averaged velocity. The method can include developing a flow velocity profile using the sensor response model. The method can include calculating a flow rate using the flow velocity profile.

In still another embodiment, a system is disclosed for interpreting measurements in a multiphase flow in a conduit. The system can include a meter of defined dimension that obtains a measurement at a plurality of locations in the multiphase flow, each measurement representing an average of a flow parameter across the defined dimension of the meter. The system can include a controller that generates a measurement model as a function of the defined dimension of the meter and the average of the flow parameter represented by the measurements, and applies a curve-fitting algorithm to the measurement model. The system can include an output interface that produces a parameter profile of the flow parameter based on the curve-fitted measurement model.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of methods and apparatuses for interpretation of multiphase fluid flow in a conduit are described with reference to the following figures. Like numbers are used throughout the figures to reference like features and components.

FIG. 1 is a schematic diagram showing an example of a wellsite system for a production well.

FIG. 2 is a schematic diagram of a production logging tool that may be used in the wellsite system of FIG. 2 to make measurements that can be used in accordance with various embodiments of the present disclosure.

FIG. 3 is a schematic diagram of a mini-spinner flow meter that may be used in the production logging tool of FIG. 3.

FIG. 4 is schematic diagram depicting an example of velocity distribution across a near horizontal pipe or conduit showing the swept area of five mini-spinners.

FIG. 5 is a graph depicting an example plot of local velocity along a vertical pipe diameter showing a spinner swept area average velocity and true velocity.

FIG. 6 is a method flowchart in accordance with various embodiments of the present disclosure.

FIG. 7 is a method flowchart in accordance with an embodiment of the present disclosure.

FIG. 8 is a method flowchart in accordance with an embodiment of the present disclosure.

FIG. 9 shows a computer system by which the methods can be implemented.

DETAILED DESCRIPTION

In the following description, numerous details are set forth to provide an understanding of the present disclosure. However, it will be understood by those skilled in the art that the present disclosure may be practiced without these details and that numerous variations or modifications from the described embodiments are possible.

With reference to multi-phase flow in pipes, holdup can be described as the fraction of a particular fluid present in an interval of pipe. In the multi-phase flow, each fluid may move at a different speed due to gravitational forces and other factors. Heavier phases may move more slowly, or may be more held up, than the lighter phase. Phase velocity can be described as the velocity with which a particular phase (gas, oil, or water) moves in a producing well.

This disclosure describes methods and tools for flow modeling and interpretation of, for example, non-radially symmetric flows with centered and non-centered spinners or sensors that can sample over a local area or volume. A spinner can, to a first order approximation, average the velocities passing through the swept area of the spinner blades, and in the interpretation of spinner data, a two dimensional model of the velocity across the cross section of the pipe can be adjusted until the modeling results match local spinner velocity readings. To produce a velocity profile, a curve can be fitted to the two dimensional model according to a curve fitting algorithm.

This disclosure describes sensor (e.g., mini-spinner) response models including area or volume average velocities that can be used, for example, in regression algorithms for production log interpretations. In various implementations, a velocity measured by a mini-spinner may be the average velocity present within the swept area of the spinner blades. In the case of multiphase flow (with or without a local slip velocity between the phases present), the spinner velocity can correspond to the volumetric average or the mass fraction average (or any other suitable multiphase average) of the velocities passing through the swept area of the spinner blades. In the case of holdup sensors with a significant sensing volume, the holdup response model can use a volumetric linear average or an inverse square law volumetric average or any other averaging algorithms suitable for the dimensions of the holdup sensors and the physics for the measurements. For other local measurements with a significant area or volume of investigation, the response model can use an average over the region being investigated with any suitable geometric weighting functions.

In the oil industry, a downhole tool (e.g., a production logging tool) may be used to obtain data about a well in an oilfield, including the fluid flow in the well. Understanding and using the data, in complex flow regimes, such as those found in deviated wells, for decision making regarding well operation can be difficult. An example embodiment using data from a downhole tool (e.g., a flow scanner logging tool) is provided, but data from any other downhole tool can be used as well in various other embodiments.

FIG. 1 depicts a wellsite 100 with a downhole tool (or production logging tool) 130 deployed into a wellbore 105 from a surface rig 101. As shown, the downhole tool 130 may be designed to be run on a low side of a pipe 110 in the wellbore 105 (low being the bottom of the pipe in a deviated or horizontal position relative to the surface). The downhole tool 130 may include two spring loaded arms 134, 136 with sensors mounted thereon for orienting the downhole tool 130 along a pipe axis Z.

FIG. 2 shows additional detail of a downhole tool 30 usable as the downhole tool 130 of FIG. 1 with three (3) mini-spinner flow meters (or mini-spinners) 37 on a leading arm 34, three holdup probes 31 on the trailing arm 36, one mini-spinner flow meter 37 and one holdup probe 31 on the tool 130 body 39. The mini-spinners 37 and holdup probes 31 may be used to acquire downhole data at various locations in the wellbore 105.

Referring back to FIG. 1, the leading arm 134 may include various sensors, such as four mini-spinners 137 mounted thereon, and the trailing arm 136 may include five water holdup probes 131a and five gas holdup probes 131b. Additional mini-spinners, water holdup probes, and gas holdup probes may optionally be mounted on the tool body 139. Measurements of a spring loaded arm caliper and tool relative bearing may be used to provide a physical location of each sensor to be determined within a circular cross section of the pipe. As would be recognized by one of skill in the art, any number of sensors (e.g., probes) for holdup and velocity measurements could be included in the downhole tool 130 in order to obtain a number of measurements adequate to support a flow rate determination.

A surface unit 150 may be in communication with the downhole tool 130 via a wireline 103 (or other telemetry device) for receiving the data acquired. Various computer systems and processors at either the surface unit 150 and/or downhole tool 130, or distributed between the two, may be used to interpret the data to determine, for example, the performance of the well.

In an example embodiment, the downhole data can be processed using various interpretation techniques, such as SCHLUMBERGER™'s OP FSI-IP™, BORFLOW™, or FSI²™; EMERAUDE™ commercially available from Kappa Petroleum Engineering Software Company; or other applicable software packages. Data may also be curve-fitted with various commercial packages, such as Schlumberger's MAPFLO™. Any number of curve-fitting algorithms may be employed, such as, for example, any non-linear least squares curve fitting like the Levenberg-Marquardt algorithm.

The downhole tool 130 of FIGS. 1 and 2 may be used in vertical or deviated wells (e.g., horizontal having a deviation of about ninety degrees, or angled at deviation of from about eighty to about one hundred degrees from vertical). When the axial direction of the well has a deviation of from about eighty to about ninety degrees as shown in portion 107b of wellbore 105, the flow in the well may be slightly uphill. On the other hand, when the axial direction of the well 105 has a deviation of about from about ninety to about one hundred degrees as shown in portion 107a of wellbore 105, the flow in the well is slightly downhill.

FIG. 3 illustrates an example of a mini-spinner flow meter 300, which has blades 310 and a hub 320, and may be used as a sensor (e.g., probe 131a or 131b) in one or more embodiments of the downhole tool 130 of FIGS. 1 and 2. The blades 310 and hub 320 rotate around the mini-spinner axis when fluid flow passes through the mini-spinner flow meter 300. It may be assumed that the hub 320 does not interact with the fluid flow which passes through the blades 310. Though the size of a spinner flow meter 300 may be small relative to the pipe in which it meters fluid flow, the mini-spinner flow meter 400 may not be considered as a point measurement device in some embodiments, unlike measurements as taken by a point device (such as, for example, an optical holdup device or electrical holdup probe). In one example, the blades in a spinner flow meter may have a nominal diameter of about 1″ (2.54 cm), while a pipe may have a diameter of about 6″ (15.24 cm).

Turning now to FIG. 4, an end view of a multi-phase flow 420 in a pipe 410 having a low (or bottom) side 423 is shown. FIG. 4 illustrates a multi-phase flow 420 having two phases: water phase and oil phase with increasing mixture velocities indicated by velocity gradients 422, 424, 426 and 428, respectively. Five mini-spinner flow meters 431, 432, 433, 434 and 435 may be used to measure the velocities at various locations in the pipe 410. The bottom three flow meters (431, 432 and 433) may encounter a relatively homogenous slow moving phase, but mini-spinner flow meters 434 and 435 may be in regions of the velocity gradient 422, 424, 426 and the 428.

In cases of non-linear or non-symmetric velocity distributions, the average swept area velocity of the mini-spinners can differ from the velocity present at an axis of the mini-spinners as depicted in the graph of FIG. 5. This graph depicts a true velocity profile 500 and a spinner swept area average velocity profile 502 plotted as a function of vertical pipe diameter (y-axis) versus axial pipe velocity (x-axis). Curve 500 represents true velocity along a vertical pipe diameter. Curve 502 indicates velocities recorded by a mini-spinner and averaged over the swept area of the mini-spinner at that height in the pipe. In the example of FIG. 5, a one inch (2.54 cm) diameter mini-spinner in a six inch (15.24 cm) internal diameter has been used.

In portions of the pipe, a difference 503 between the true velocity profile 500 and average velocity 502 is negligible. The difference 503 may be considered the error introduced when using curve 502 rather than curve 500. In portions of the pipe, the difference 504 between the curve 500 and the curve 502 becomes more visible. In some portions of the pipe, the difference 505 (i.e., the error introduced) may be large. As depicted by FIG. 4, a lower half of the pipe can be unaffected by a choice of spinner response model, for example, where the fluid may have a negligible velocity gradient at that point in the pipe. However, at around 75% of the height of the pipe (e.g., at 434 in FIG. 4), errors may be introduced first in one direction and then in the other, and the velocity profile can be associated with a change in fluid phases within the pipe. In such cases, the errors may not cancel, and may overestimate the heavy phase flow rate and/or underestimate the light phase flow rate.

FIGS. 6-8 depict various methods for interpreting measurements by a meter (e.g., flowmeter 300 of FIG. 3) of defined dimension in a multiphase flow in a conduit. These methods involve curve fitting to align measurements with model adjust for potential effects of meters, such as mini-spinners, in multiphase fluids. In various implementations, a velocity measured by a mini-spinner represents an average velocity present within a swept area of spinner blades of the meter. In cases of multiphase flow (with or without a local slip velocity between the phases present), spinner velocity can correspond to a volumetric average or a mass fraction average (or any other suitable multiphase average) of the velocities passing through the swept area of the spinner blades.

Interpretation of a mini-spinner flow meter reading may be done with an aid of a two dimensional model of the measured parameter (i.e., velocity or holdup) across the cross section of the pipe, referred to as a parameter profile (i.e., density, velocity or holdup profile). A parameter profile plots measurements (representative of an average within a swept area of the spinner blades) obtained at various locations in the pipe against the dimensionless height of the pipe. In some embodiments, a curve fitting algorithm may be used to smooth a polynomial function along the measurement points that represent a swept-area velocity average. With reference to multi-phase flow in pipes, holdup can be described as the fraction of a particular fluid present in an interval of pipe. In the multi-phase flow, each fluid may move at a different speed due to gravitational forces and other factors. Heavier phases may move more slowly, or may be more held up, than the lighter phase. Phase velocity can be described as the average velocity with which a particular phase (gas, oil, or water) moves in a producing well.

Turning now to calculations that could be used in generating the two dimensional measurement model, a two dimensional mathematical solver for flow rates may attempt to minimize the following error function at each depth along the wellbore:

Error=Σ[2DModel(x_iy_i)−meas_i]^Z  (Eq. 1)

where 2DModel(x_iy_i) represent sensor response models where x_i and y_i are coordinates for measurement points with respect to a coordinate system defined centered in the diameter of pipe; and the measurements, meas_i, correspond to an individual probe holdup, an individual mini-spinner velocity, or any other flow related local measurements. A single logging pass, multiple logging passes, or stationary measurements can be used to supply meas_i. For example, a single logging pass of a SCHLUMBERGER™ FLOWSCANNER™ may generate five velocity measurements, six water holdup measurements and six gas holdup measurements, and the error function (Eq. 1) may contain seventeen terms.

In various implementations, several two-dimensional flow features can be used for the models 2DModel(x_iy_i). For example, for horizontal wells where local measurements are often used, the models can be reduced by one dimension and a horizontally stratified holdup and velocity imposed. In this case, the holdup Y of a phase n may vary with height x and may not vary with horizontal position y, as shown in the following equation:

Y_n=f(x)  (Eq. 2)

The measurements of local holdup probes may be projected back onto the reference vertical pipe diameter. Also, for horizontal wells, the water holdup can monotonically increase from the top to the bottom of the pipe while the gas holdup can monotonically decrease from the top to the bottom of the pipe. In this case, the regression solver may be constrained by:

Y_wxn≦Y_wxn+1 where x_n<x_n+1  (Eq. 3)

Y_gxn≧Y_gxn+1 where x_n<x_n+1  (Eq. 4)

where Yw_xn is the local water holdup at height x_n and Yg_xn is the local gas holdup at height x_n.

Another technique that can be used is to impose the shape of a SCHLUMBERGER™ MAPFLO™ curve to a holdup profile, and then use a shape of a holdup curve to describe a velocity profile (after applying a gain and offset to MAPFLO™ and taking a wall velocity to zero using a Prandtl profile). Such technique is described in U.S. Provisional Application No. 61/426,640 filed Dec. 23, 2010, and U.S. Application titled “SYSTEMS AND METHODS FOR INTERPRETING MULTI-PHASE FLUID FLOW DATA” and filed Dec. 4, 2011 with Attorney Docket Number 21.1949 US. Further, a global pipe slip correlation can be imposed as another constraint:

$\begin{array}{cc}\frac{q_w}{Y_w}-\frac{q_0}{Y_0}=V_{\mathrm{oil}\text{-}\mathrm{water}\mathrm{slip}}& \left(\mathrm{Eq}.5\right)\\ \frac{q_g}{Y_g}-\frac{q_1}{Y_1}=V_{\mathrm{gas}\text{-}\mathrm{liquid}\mathrm{slip}}& \left(\mathrm{Eq}.6\right)\end{array}$

where q is flow rate; Y is holdup fraction; and w is for water, o for oil, g is gas, l for liquid. The slip velocity can be supplied by various mechanistic models known to one of ordinary skill in the art such as SCHLUMBERGER™'S STRATFLO™ model; a Hasan and Kabir mechanistic model; a Petalas and Aziz mechanistic model; or any of a number of drift flux correlations; or any number of empirical correlations. Moreover, a global pipe holdup can be imposed from another logging measurement, such as a pulsed neutron three phase holdup log, by introducing another error term:

Error_n=[Y_wmodel−Y_wTPHL]²+[y_omodel−Y_oTPHL]²+[Y_gmodel−Y_gTPHL]²  (Eq. 7)

where Y is holdup fraction; w is for water, o for oil, g for gas; and TPHL stands for Three Phase Holdup Log (a measurement supplied, e.g., from SCHLUMBERGER™'s pulsed neutron logging tool). The above techniques can enable a regression algorithm to derive usable multi-phase flow rates from local measurements of velocity and holdup.

This disclosure describes methods for averaging sensor response models that can provide more accurate production logging interpretations including the computed flow rates. A swept area velocity averaging model can be expressed as:

$\begin{array}{cc}2\mathrm{DModel}\left(x,y\right)=\frac{1}{A_s}{\int }_{A_S}\mathrm{velocity}\left(x,y\right)S& \left(\mathrm{Eq}.8\right)\end{array}$

where A_s is the area of the mini-spinner.

With a correction for the dead volume of the mini-spinner hub, the swept area velocity averaging model can then be expressed:

$\begin{array}{cc}2\mathrm{DModel}\left(x,y\right)=\frac{1}{A_s-A_h}{\int }_{A_s-A_h}\mathrm{velocity}\left(x,y\right)S& \left(\mathrm{Eq}.9\right)\end{array}$

where A_h is the cross-sectional area of the mini-spinner hub.

If the mini-spinner hub is ignored and the fluid density is considered, a mass fraction average can give the following response model:

$\begin{array}{cc}2\mathrm{DModel}\left(x,y\right)=\frac{1}{A_s}\frac{{\int }_{A_s}\mathrm{density}\left(x,y\right)\times \mathrm{velocity}\left(x,y\right)S}{{\int }_{A_S}\mathrm{density}\left(x,y\right)S}& \left(\mathrm{Eq}.10\right)\end{array}$

This model may be used, for example, in gas-liquid wells where there can be density and velocity differences between the gas and the liquid. In some cases, a sum of a product of phase holdups and at least one phase density can be calculated.

For a sensor that can sample a volume of the pipe, the sensor response model can be expressed as:

$\begin{array}{cc}2\mathrm{DModel}\left(x,y,z\right)=\frac{1}{V_S}{\int }_{V_S}\mathrm{measurement}\left(x,y,z\right)V\mathrm{or}& \left(\mathrm{Eq}.11\right)\\ 2\mathrm{DModel}\left(x,y,z\right)=\frac{1}{V_s-V_p}{\int }_{V_s-V_p}\mathrm{measurement}\left(x,y,z\right)V& \left(\mathrm{Eq}.12\right)\end{array}$

where V_s is the volume investigated by the sensor; and V_p is the volume occupied by the sensor. This model can further be modified by a geometrical weighting factor such as an inverse square law for sensors with sensitivity that may be used to diminish with distance.

Turning now to various method flow charts, FIG. 6 shows a flow chart for a method 600 in accordance with one embodiment of the present disclosure. At 610, the method includes disposing a meter, such as for example, a spinner or series of mini-spinners, in a conduit having a multi-phase flow passing therethrough. At 620, the method includes obtaining measurements at a plurality of locations. In one embodiment, the plurality of measurements may be obtained by moving a single meter or spinner about within the conduit to a plurality of locations. In another embodiment, the plurality of measurements may be obtained when the meter is a series of mini-spinners, each taking a measurement in its own location relatively spaced apart from the other mini-spinners in the series (see, e.g., FIG. 1). In an embodiment, the measurements may include velocity measurements, holdup measurements, and/or a quantifiable flow parameter of the multi-phase flow.

At 630, the method includes generating a two-dimensional measurement model. In an embodiment, this generation of a measurement model may be performed according to the modeling calculations described above. The model may be a function of the dimension of the meter (i.e., area and/or volume, depending on the type of meter) and averages of the measured flow parameter (such as velocity or holdup) across the dimension of the meter. At 640, the method includes iteratively adjusting the measurement model until the measurement model aligns with the actual measurements obtained at 620.

At 650, the method includes applying a curve fitting algorithm to the measurement model, such as a non-linear least squares curve fitting like the Levenberg-Marquardt algorithm. At 660, the method includes outputting a flow parameter profile based on the curve fitted to the measurement model. The flow parameter profile may include, for example, a velocity profile or a holdup profile, and may be used, in turn, to determine a flow rate in the conduit.

FIG. 7 shows a flow chart for another method 700 in accordance with one embodiment of the present disclosure. At 710, the method includes disposing a meter, such as for example, a spinner or series of mini-spinners, in a conduit having a multi-phase flow passing therethrough. At 720, the method includes obtaining velocity measurements at a plurality of locations. At 730, the method includes generating a velocity measurement model as a function of the dimension of the meter and averaged velocities across the swept area of the meter represented by the measurements. At 740, the method includes iteratively adjusting the velocity measurement model until the velocity measurement model aligns with the actual velocity measurements obtained at 720. At 750, the method includes applying a curve fitting algorithm to the velocity measurement model, such as, for example but not limited to, any non-linear least squares curve fitting like the Levenberg-Marquardt algorithm. At 760, the method includes outputting a velocity profile based on the curve fitted to the velocity measurement model. The velocity profile may be used, optionally, as part of a determination of a flow rate in the conduit.

FIG. 8 shows a flow chart for another method 800 in accordance with one embodiment of the present disclosure. At 810, the method includes disposing a meter in a conduit having a multi-phase flow passing therethrough. At 820, the method includes obtaining holdup measurements at a plurality of locations. At 830, the method includes generating a holdup measurement model as a function of the dimension of the meter and averaged holdup across the swept area of the meter represented by the measurements. At 840, the method includes iteratively adjusting the holdup measurement model until the holdup measurement model aligns with the actual holdup measurements obtained at 820. At 850, the method includes applying a curve fitting algorithm to the holdup measurement model, such as, for example but not limited to, any non-linear least squares curve fitting like the Levenberg-Marquardt algorithm. At 860, the method includes outputting a holdup profile based on the curve fitted to the holdup measurement model. The holdup profile may be used, optionally, as part of a determination of a flow rate in the conduit.

As those with skill in the art will understand, one or more of the parts of methods discussed above may be combined and/or the order of some operations may be changed. Further, some operations in methods may be combined with aspects of other example embodiments disclosed herein, and/or the order of some operations may be changed. The process of measurement, its interpretation and actions taken by operators may be done in an iterative fashion; this concept is applicable to the methods discussed herein.

Portions of methods may be performed by any suitable techniques, including on an automated or semi-automated basis on computing system 900 in FIG. 9. An example computer system 900 is shown in FIG. 9. The computer system 900 includes a computer 930 in communication with disk storage devices 929, 931, 933 and 935, which may be external hard disk storage devices and measurement sensors (not shown). It is contemplated that disk storage devices 929, 931, 933 and 935 may be conventional hard disk drives and, as such, may be implemented by way of a local area network or by remote access. While disk storage devices are illustrated as separate devices, a single disk storage device may be used to store the program instructions, measurement data, and results as desired.

In one implementation, petroleum real-time data from the sensors may be stored in disk storage device 931. Various non-real-time data from different sources may be stored in disk storage device 933. The system computer 930 may retrieve the appropriate data from the disk storage devices 931 or 933 to process data according to program instructions that correspond to implementations of various techniques described herein. The program instructions may be written in a computer programming language, such as C++, Java and the like. The program instructions may be stored in a computer-readable medium, such as program disk storage device 935. Such computer-readable media may include computer storage media. Computer storage media may include volatile and non-volatile, and removable and non-removable media implemented in any method or technology for storage of information, such as computer-readable instructions, data structures, program modules or other data. Computer storage media may further include RAM, ROM, erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other solid state memory technology, CD-ROM, digital versatile disks (DVD), or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by the system computer 930. Combinations of any of the above may also be included within the scope of computer readable media.

In one implementation, the system computer 930 may present outputs primarily onto graphics display 927, or via a printer (not shown). The system computer 930 may store the results of the methods described above on disk storage 929, for later use and further analysis. The keyboard 926 and the pointing device (e.g., a mouse, trackball, or the like) 925 may be provided with the system computer 930 to enable interactive operation.

The system computer 930 may be located on-site near the well or at a data center remote from the field. The system computer 930 may be in communication with equipment on site to receive data of various measurements. Such data, after conventional formatting and other initial processing, may be stored by the system computer 930 as digital data in the disk storage 931 or 933 for subsequent retrieval and processing in the manner described above. While FIG. 9 illustrates the disk storage, e.g. 931 as directly connected to the system computer 930, it is also contemplated that the disk storage device may be accessible through a local area network or by remote access. Furthermore, while disk storage devices 929, 931 are illustrated as separate devices for storing input petroleum data and analysis results, the disk storage devices 929, 931 may be implemented within a single disk drive (either together with or separately from program disk storage device 933, or in any other conventional manner as will be fully understood by one of skill in the art having reference to this specification.

While the disclosure has been disclosed with respect to a limited number of embodiments, those skilled in the art, having the benefit of this disclosure, will appreciate numerous modifications and variations therefrom. While the disclosure has been described in the context of applications in downhole tools, the system of the disclosure can be used in many applications requiring shear velocity evaluation of a medium.

Although only a few example embodiments have been described in detail above, those skilled in the art will readily appreciate that many modifications are possible in the example embodiments without materially departing from this invention. Accordingly, all such modifications are intended to be included within the scope of this disclosure as defined in the following claims. In the claims, means-plus-function clauses are intended to cover the structures described herein as performing the recited function and not only structural equivalents, but also equivalent structures. Thus, although a nail and a screw may not be structural equivalents in that a nail employs a cylindrical surface to secure wooden parts together, whereas a screw employs a helical surface, in the environment of fastening wooden parts, a nail and a screw may be equivalent structures. It is the express intention of the applicant not to invoke 35 U.S.C. §112, paragraph 6 for any limitations of any of the claims herein, except for those in which the claim expressly uses the words ‘means for’ together with an associated function.

Claims

1. A method for interpreting measurements by a meter of defined dimension in a multiphase flow in a conduit in a wellbore, comprising:

obtaining a measurement with the meter at each of a plurality of locations in the multiphase flow, each measurement representing an average of a flow parameter across a defined dimension of the meter;

generating a measurement model as a function of the defined dimension of the meter and the average of the flow parameter; and

generating a parameter profile of the flow parameter by fitting a curve to the measurement model.

2. The method of claim 1, wherein the average of the flow parameter across the defined dimension of the meter comprises an average of the measurements over a cross-sectional area of the meter.

3. The method of claim 1, wherein the average of the flow parameter across the defined dimension of the meter comprises an average of the measurements over a volume of the meter.

4. The method of claim 1, wherein the average of the flow parameter across the defined dimension of the meter comprises a mass weighted average of the measurements over a volume of the meter.

5. The method of claim 1, wherein the defined dimension of the meter excludes a dead portion of the meter through which fluid does not flow.

6. The method of claim 1, wherein the parameter profile comprises one of a velocity profile and a holdup profile.

7. The method of claim 1, further comprising iteratively adjusting the measurement model to align with the measurements.

8. The method of claim 1, wherein the parameter profile comprises a velocity profile; the method further comprising:

obtaining holdup measurements;

generating a holdup profile by fitting a curve to the holdup measurements; and

determining a volumetric flow rate as a function of the holdup profile and the velocity profile over the conduit.

9. The method of claim 8, further comprising calculating a sum of a product of phase holdups and at least one phase density.

10. A method for interpreting measurements in a multiphase flow in a conduit in a wellbore, the method comprising:

measuring a flow parameter with a sensor that averages the flow parameter over a dimension of investigation to generate a dimension averaged flow parameter;

providing a sensor response model including the dimension averaged flow parameter;

developing a flow parameter profile using the sensor response model; and

calculating a flow rate using the flow parameter profile.

11. The method according to claim 10, wherein the sensor comprises a spinner; and wherein providing a sensor response model comprises averaging the flow parameter over a swept area of the spinner.

12. The method according to claim 11, wherein providing a sensor response model further comprises correcting for a cross-sectional area of a non-flow area of the spinner.

13. The method according to claim 11, wherein providing a sensor response model further comprises mass fraction averaging the flow parameter over the swept area of the spinner.

14. The method according to claim 10, wherein providing a sensor response model comprises averaging the flow parameter over a volume investigated by the sensor.

15. The method according to claim 14, wherein providing a sensor response model, further comprises correcting for a volume occupied by a stationary portion of the sensor.

16. The method according to claim 10, wherein providing a sensor response model comprises modifying the sensor response model by a geometrical weighting factor.

17. The method according to claim 16, wherein the geometrical weighting factor comprises an inverse law for the sensor.

18. The method according to claim 10, wherein the flow parameter is one of density, velocity and holdup.

19. A system for interpreting measurements in a multiphase flow in a conduit in a wellbore, comprising:

a meter of defined dimension that obtains a measurement at a plurality of locations in the multiphase flow, each measurement representing an average of a flow parameter across the defined dimension of the meter; and

a controller that generates a measurement model as a function of the defined dimension of the meter and the average of the flow parameter and applies a curve-fitting algorithm to the measurement model to generate a curve-fitted measurement model and to produce a parameter profile of the flow parameter based on the curve-fitted measurement model.

20. The system of claim 19, wherein the meter comprises a spinner and the measurement model generated by the controller is based on one of an average velocity over a swept area of the spinner, a mass fraction average velocity over the swept area of the spinner, and an average velocity over a volume investigated by the spinner.

21. The system of claim 20, wherein the controller further corrects the average of the flow parameter for a portion of the meter through which the multi-phase flow does not pass.