WO2016204752A1 - Automated pvt characterization and flow metering - Google Patents
Automated pvt characterization and flow metering Download PDFInfo
- Publication number
- WO2016204752A1 WO2016204752A1 PCT/US2015/036270 US2015036270W WO2016204752A1 WO 2016204752 A1 WO2016204752 A1 WO 2016204752A1 US 2015036270 W US2015036270 W US 2015036270W WO 2016204752 A1 WO2016204752 A1 WO 2016204752A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- pvt
- fluid
- model
- measurement
- downhole
- Prior art date
Links
- 238000012512 characterization method Methods 0.000 title description 3
- 239000012530 fluid Substances 0.000 claims abstract description 108
- 238000004519 manufacturing process Methods 0.000 claims abstract description 63
- 238000005259 measurement Methods 0.000 claims abstract description 39
- 238000000034 method Methods 0.000 claims description 33
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 10
- 230000015572 biosynthetic process Effects 0.000 claims description 8
- 230000005484 gravity Effects 0.000 claims description 5
- 239000000203 mixture Substances 0.000 description 25
- 239000012071 phase Substances 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- 230000001413 cellular effect Effects 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 239000007791 liquid phase Substances 0.000 description 2
- 239000000126 substance Substances 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000009472 formulation Methods 0.000 description 1
- 229930195733 hydrocarbon Natural products 0.000 description 1
- 150000002430 hydrocarbons Chemical class 0.000 description 1
- 230000004941 influx Effects 0.000 description 1
- 239000007788 liquid Substances 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000011084 recovery Methods 0.000 description 1
- 230000001360 synchronised effect Effects 0.000 description 1
- 239000012808 vapor phase Substances 0.000 description 1
Classifications
-
- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B47/00—Survey of boreholes or wells
- E21B47/06—Measuring temperature or pressure
-
- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B43/00—Methods or apparatus for obtaining oil, gas, water, soluble or meltable materials or a slurry of minerals from wells
- E21B43/12—Methods or apparatus for controlling the flow of the obtained fluid to or in wells
-
- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B47/00—Survey of boreholes or wells
- E21B47/003—Determining well or borehole volumes
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
Definitions
- a PVT model allows operators and other users to understand certain behavior or characteristics of the production fluid under certain conditions and at various stages. For example, a reservoir engineer may use the PVT model to estimate how much oil/gas may be produced from the reservoir and how quickly the oil/gas can be produced. A process plant operator may use data from the PVT to determine treatment processes for processing the fluid or for creating intermediary products. An allocation engineer may use the PVT model to help determine allocation of the produced fluid.
- thermodynamic properties of the fluid In order to properly model the flow of oil from the reservoir into the well and through production facilities (production piping, surface pipelines, etc.), it is necessary to understand the thermodynamic properties of the fluid, and to be able to calculate them to a certain degree of accuracy. These properties can often vary quite a bit even within a reservoir depending on the zone or well as well as over time.
- FIG.1 illustrates a production well system, in accordance with example embodiments of the present disclosure
- FIG.2 is a high level system diagram of a PVT modeling system, in accordance with example embodiments of the present disclosure
- FIG.3 illustrates a multiple well system instrumented with a PVT modeling system, in accordance with example embodiments of the present disclosure
- FIG.4 is a high level system diagram of a multiple well PVT modeling system, in accordance with example embodiments of the present disclosure.
- the illustrated figures are only exemplary and are not intended to assert or imply any limitation with regard to the environment, architecture, design, or process in which different embodiments may be implemented.
- DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS [0010]
- the present disclosure is directed towards novel systems and methods for creating a pressure-volume-temperature (PVT) model and characterization of production fluid from fluid measurements obtained directly from the well, including at the point of influx from the reservoir, throughout the wellbore, and through the associated surface facilities.
- PVT pressure-volume-temperature
- a sample of production fluid is taken from the reservoir and brought into a lab facility for analysis.
- the sampled production fluid may not be representative of the entire reservoir as different areas of the reservoir may produce fluid having different properties.
- the properties of the production fluid may also change over time, and since there is a significant delay between obtaining the fluid sample and receiving the PVT model from the lab analysis, the PVT model may be out of date.
- the present disclosure employs a system of downhole and surface sensors, such as multi-phase flow meters, pressure sensors, and temperature sensors, to obtain production fluid characteristics, which are used to generate a PVT model of the fluid on the fly.
- a PVT model is produced with production fluid characteristics taken under the proper environmental conditions, and which has minimum time delay.
- FIG.1 illustrates an example production well system 100.
- the well system 100 includes a well 102 formed within a formation 104.
- the well 102 may be a vertical wellbore as illustrated or it may be a horizontal or directional well.
- the formation 104 may be made up of several zones which may include oil reservoirs.
- the well system 100 may include a production tree 108 and a wellhead 109 located at a well site 106.
- a production tubing 112 extends from the wellhead 109 into the well 102.
- production tubing 112 includes a plurality of perforations 126 through which fluids from the formation 104 can enter the production tubing 112 and flow upward into the production tree 108.
- the subsurface pressure on the fluids is large enough to push the fluid upward naturally.
- the production fluid is recovered using artificial lift or enhanced recovery techniques.
- the wellbore 102 is cased with one or more casing segments 130.
- the casing segments 130 help maintain the structure of the well 102 and prevents the well 102 from collapsing in on itself.
- a portion of the well is not cased and may be referred to as“open hole”.
- the space between the production tubing 112 and the casing 130 or wellbore 102 is an annulus 110.
- the well system 100 includes one or more downhole sensors 116.
- the sensors 116 measure one or more conditions of the production fluid in the downhole environment. This data is used in generating the PVT model.
- the sensors 116 are coupled to the outside of the production tubing 112 near the target formation.
- the sensors 116 can be located inside the production tubing 112, on the borehole 102 wall, or otherwise disposed downhole.
- the sensors 116 may include a flow meter, a pressure sensor, a temperature sensor, a fluid composition sensor, or any combination thereof, among other types of sensors.
- the flow meter measures the rate of fluid flow into the production tubing 112 downhole near the perforations 126.
- the pressure sensor measures the amount of production fluid pressure downhole near the perforations 126.
- the temperature sensor measures the temperature of the production fluid downhole near the perforations 126.
- the fluid composition sensor detects the chemical makeup of the production fluid downhole near the perforations 126.
- the well system 100 may include any one of these sensors, any combination of these sensors, or other types of sensors.
- the well system 100 includes one or more surface sensors 118 configured to measure properties of production fluid at the surface. This data is used in generating the PVT model.
- the sensors 118 are coupled to a surface pipeline 114.
- the sensors 118 may include a multiphase flow meter, a pressure sensor, a temperature sensor, a fluid composition sensor, or any combination thereof, among other types of sensors.
- the flow meter measures the rate of fluid flow through the pipeline 114.
- the pressure sensor measures the amount of fluid pressure in the pipeline 114.
- the temperature sensor measures the temperature of the production fluid in the pipeline 114.
- the fluid composition sensor detects the chemical makeup of the production fluid in the pipeline 114.
- the well system 100 may include any one of these sensors disposed above ground, any combination of these sensors, or other types of sensors.
- the well system 100 may only include one or more downhole sensors 116 and no surface sensors.
- the well system 100 may only include one or more surface sensors 118.
- FIG.2 is a high level system diagram of a PVT modeling system 200, in accordance with example embodiments of the present disclosure. In some
- a processor 202 receives at least one downhole sensor data 204 collected from the downhole sensors 116 and/or at least one surface sensor data 206 from the surface sensors 118 and generates a PVT model 208 from the data.
- the data 204, 206 is received in real time or quasi-real time.
- the processor 202 receives the data 204, 206 through a direct wired connection.
- the processor 202 receives the data 204, 206 through a wireless communication protocol such as Wi-Fi, Bluetooth, cellular network, and the like.
- the processor 202 is coupled to or integral with one or more of the sensors 116, 118.
- the processor 202 may be located downhole or above ground.
- the processor 202 is disposed at the well site 106 as a computing device or as a part of a control station.
- the sensors 116, 118 are coupled to a transmitter or a transceiver, which communicates the data from the sensors 116, 118 to the processor 202.
- the processor 202 can be remotely located from the sensors 116, 118 in a facility such as an office building or laboratory.
- the processor 202 is communicatively coupled to or integrally includes a memory device.
- the memory device holds instructions for building a PVT model 208 using the data 204, 206 as inputs.
- the PVT model 208 can be generated using various PVT modeling algorithms.
- the PVT modeling algorithms may be considered a PVT model builder or a system to which one or more inputs are applied.
- the processor 202 utilizes the sensor data 202, 206 as well as one or more previously measured or known parameters with the PVT modeling algorithm.
- the PVT model 208 is a black-oil model, in which the composition of the production fluid is not taken into account.
- the sensor data 202, 206 includes a downhole flow rate, a surface flow rate, a downhole fluid temperature, a surface fluid temperature, a downhole fluid pressure, a surface fluid pressure, a gas rate, an oil rate, a water rate, a gas gravity, a water salinity, or any combination of such.
- the PVT modeling algorithm for a black-oil PVT model includes the following equations:
- the processor 202 runs these equations as a system of equations and solves for the following variables:
- the PVT model is a compositional model, which takes into account the composition of the production fluid. In one such example
- the sensor data 202, 206 includes a downhole fluid pressure, a downhole fluid temperature, a surface fluid pressure, and a surface fluid temperature.
- the values of (oil density) and (gas density) will be measured by surface sensors.
- the overall composition (z i for each component i) of the fluid at bottom hole conditions is measured by the fluid composition sensor.
- the PVT modeling algorithm for a compositional PVT model includes the following equations: [0024]
- the approximate equilibrium ratio for each component is measured using a correlation, for example:
- an initial value for the number of moles in the gas phase, n v is found using the following equations.
- a nonlinear root finding algorithm such as Newton-Raphson, is used to converge to a final value.
- the following system of equations is solved for the phase compositions (gas composition y i for each component i and liquid composition x i for each component i).
- an equation of state is used to compute the liquid and vapor phase densities.
- the calculated densities are compared with those measured using the multiphase flow meters. If the densities differ by more than a specified tolerance, a i and b i can be tuned by adjusting the factors ⁇ a and ⁇ b . This can be done using a nonlinear root finding algorithm on functions of the form:
- Viscosities may be calculated using the following equations once the densities are known.
- All other variables may be considered to be either known in advance, or else to be parameters that are calculated explicitly as part of the process.
- the solved variables are the PVT model parameters and define the PVT model.
- the equations and algorithms used in the examples above are purely illustrative and are not limiting.
- the data 204, 206 received from the downhole sensors 116 and surface sensors 118 can be manipulated and applied in various different ways to generate a PVT model 208.
- the parameters of the PVT model 208 calculated therefrom are more accurate.
- the generated PVT model 208 is more accurate as well.
- the PVT model 208 is generated by the processor 202 and published or sent to various receiving parties.
- the parameters of the PVT model 208 are generated by the processor 202 and sent to another data processing means which generates the PVT model 208 from the parameters.
- the PVT model parameters or the PVT model 208 can be directly sent to one or more recipients.
- the PVT model 208 can be updated in real time or quasi-real time with up-to-date data 204, 206 measured by the sensors 116, 118.
- the PVT model 208 is updated when one or more of the sensed measurements changes by a predetermined amount.
- FIG.3 illustrates a multiple well system 300, in accordance with example embodiments of the present disclosure.
- the multiple well system 300 includes a plurality of individual production well systems 302 such as a first well system 302a, a second well system 302b, and a third well system 302c.
- Each of the well systems 302 is similar to the well system 100 of FIG.1, and includes a wellbore 304, a production tubing 312, a production tree 308, a wellhead 309, and a surface pipeline 314 coupled to a main pipeline 320.
- production fluid recovered from each of the wells 304 flows into the main pipeline 320 which delivers the combined
- one or more of the well systems 302a, 302b, 302c is instrumented with one or more downhole sensors 316.
- the one or more downhole sensors 316 may be coupled to a portion of the production tubing, the wellbore, or elsewhere near the production formation.
- the downhole sensors 316 may include a multiphase flow meter, a pressure sensor, a temperature sensor, a fluid composition sensor, or any combination thereof, among other types of sensors.
- one or more of the well systems 302 is instrumented with one or more surface sensors 318.
- the surface sensors 318 can be coupled to the respective surface pipelines 314, production trees 308, or other surface portion of the well system 302 through which production fluid flows.
- the surface sensors 318 may include a multiphase flow meter, a pressure sensor, a temperature sensor, a fluid composition sensor, or any combination thereof, among other types of sensors.
- one or more of the well systems 302 may include a fluid composition sensor.
- one or more main line sensors 322 are coupled to the main pipeline 320.
- the one or more main line sensors 322 may include a multiphase flow meter, a pressure sensor, a temperature sensor, a fluid composition sensor, or any combination thereof, among other types of sensors.
- the one or more main line sensors 322 can measure the combined production fluid temperature, pressure, flow rate, composition, or any combination thereof.
- FIG.4 is a high level system diagram of a multiple well PVT modeling system 400, in accordance with example embodiments of the present disclosure.
- one or more pieces of data collected from each well system 402, such as flow rate 404 is transmitted to a processor 410.
- each well system 402 generates an individual PVT model 406, such as in the fashion illustrated and described above with reference to FIGS.1 and 2.
- the generated individual well PVT model 406 is transmitted to the processor 410.
- one or more measured data from the main line sensor 322, such as flow rate 408, is transmitted to the processor 410.
- data transmission is via wired or wireless communication protocols, such as Bluetooth, cellular networks, Wi-Fi, and the like.
- the processor 410 utilizes the data from each individual well 302 as well as the main pipeline sensor 322 to generate a mixture PVT model 412.
- the mixture PVT model 412 models the combined production fluid.
- the mixture PVT model 412 can be a black oil model or a compositional model.
- the individual wells 302 do not generate individual PVT models.
- the data collected from the downhole and/or surface sensors are transmitted to the processor to generate the mixture PVT model 412.
- the mixture PVT model 412 can then be transmitted to or accessed by one or more users.
- the mixture PVT model 412 can be updated on the fly, based on a predetermined time interval, upon a certain condition, or on demand.
- PVT pressure-volume-temperature
- Example 2 The PVT modeling system of example 1, wherein the measurement of a condition of the fluid includes at least one of downhole flow rate, surface flow rate, downhole fluid temperature, surface fluid temperature, downhole fluid pressure, surface fluid pressure, gas rate, oil rate, water rate, gas gravity, water salinity, or any combination of such.
- Example 3 The PVT modeling system of example 1, wherein the PVT model builder includes a system of equations configured to use the fluid measurement and known parameters of the well system to solve for a set of PVT model parameters.
- Example 4 The PVT modeling system of example 3, wherein the PVT model is defined by the PVT parameters.
- Example 5 The PVT modeling system of example 3, wherein the PVT parameters include a solution gas ratio, a bubble point pressure, an oil formation volume factor, an oil viscosity, a gas viscosity, or any combination of such.
- Example 6 The PVT modeling system of example 1, wherein the PVT model is a black-oil PVT model.
- Example 7 The PVT modeling system of example 1, wherein the PVT model is a compositional PVT model.
- Example 8 The PVT modeling system of example 1, wherein the PVT model is updated when the fluid measurement changes.
- Example 9 A method of generating a PVT model for a production fluid from a well system, comprising:
- Example 10 The method of example 9, further comprising:
- Example 11 The method of example 10, further comprising:
- Example 12 The method of example 9, wherein the PVT model is a black-oil PVT model.
- Example 13 The method of example 9, wherein the PVT model is a compositional PVT model.
- Example 14 The method of example 9, wherein the measurement includes a downhole flow rate, a downhole fluid temperature, a downhole fluid pressure, or any
- Example 15 The method of example 9, wherein the measurement includes at least one of surface flow rate, surface fluid temperature, surface fluid pressure, or any combination of such received from a second sensing device coupled to a surface pipeline.
- Example 16 The method of example 10, further comprising:
- Example 17 The method of example 9, wherein the PVT model is generated in quasi- real time upon receiving the measurement.
- Example 18 The method of example 9, comprising:
- Example 19 The method of example 11, further comprising determining the set of PVT parameters from the measurements and at least one known parameter of the well system.
- Example 20 The method of example 9, wherein the measurement of a condition of the production fluid includes at least one of downhole flow rate, surface flow rate, downhole fluid temperature, surface fluid temperature, downhole fluid pressure, surface fluid pressure, gas rate, oil rate, water rate, gas gravity, water salinity, or any combination of such.
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Abstract
Description
Claims
Priority Applications (8)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US15/567,351 US20180112517A1 (en) | 2015-06-17 | 2015-06-17 | Automated PVT Characterization and Flow Metering |
CA2985574A CA2985574A1 (en) | 2015-06-17 | 2015-06-17 | Automated pressure-volume-temperature (pvt) characterization and flow metering |
PCT/US2015/036270 WO2016204752A1 (en) | 2015-06-17 | 2015-06-17 | Automated pvt characterization and flow metering |
GB1718753.5A GB2555962A (en) | 2015-06-17 | 2015-06-17 | Automated PVT characterization and flow metering |
AU2015399038A AU2015399038A1 (en) | 2015-06-17 | 2015-06-17 | Automated PVT characterization and flow metering |
FR1653583A FR3037683B1 (en) | 2015-06-17 | 2016-04-22 | AUTOMATED CHARACTERIZATION OF PVT AND FLOW MEASUREMENT |
ARP160101353A AR104588A1 (en) | 2015-06-17 | 2016-05-11 | PVT CHARACTERIZATION AND AUTOMATED FLOW MEASUREMENT |
NO20171712A NO20171712A1 (en) | 2015-06-17 | 2017-10-26 | Automated pvt characterization and flow metering |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
PCT/US2015/036270 WO2016204752A1 (en) | 2015-06-17 | 2015-06-17 | Automated pvt characterization and flow metering |
Publications (1)
Publication Number | Publication Date |
---|---|
WO2016204752A1 true WO2016204752A1 (en) | 2016-12-22 |
Family
ID=57538819
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/US2015/036270 WO2016204752A1 (en) | 2015-06-17 | 2015-06-17 | Automated pvt characterization and flow metering |
Country Status (8)
Country | Link |
---|---|
US (1) | US20180112517A1 (en) |
AR (1) | AR104588A1 (en) |
AU (1) | AU2015399038A1 (en) |
CA (1) | CA2985574A1 (en) |
FR (1) | FR3037683B1 (en) |
GB (1) | GB2555962A (en) |
NO (1) | NO20171712A1 (en) |
WO (1) | WO2016204752A1 (en) |
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US20030182061A1 (en) * | 2002-03-19 | 2003-09-25 | Ferworn Kevin A. | Method and apparatus for simulating PVT parameters |
US20110224835A1 (en) * | 2009-06-03 | 2011-09-15 | Schlumberger Technology Corporation | Integrated flow assurance system |
US20120209542A1 (en) * | 2009-08-11 | 2012-08-16 | Expro Meters, Inc. | Method and apparatus for monitoring multiphase fluid flow |
US20130035920A1 (en) * | 2011-08-02 | 2013-02-07 | Saudi Arabian Oil Company | Methods for performing a fully automated workflow for well performance model creation and calibration |
US20140102697A1 (en) * | 2012-10-16 | 2014-04-17 | Expro Meters, Inc. | Systems and methods for managing hydrocarbon material producing wellsites using clamp-on flow meters |
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MY127805A (en) * | 2001-01-18 | 2006-12-29 | Shell Int Research | Determining the pvt properties of a hydrocarbon reservoir fluid |
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US7809538B2 (en) * | 2006-01-13 | 2010-10-05 | Halliburton Energy Services, Inc. | Real time monitoring and control of thermal recovery operations for heavy oil reservoirs |
WO2008101333A1 (en) * | 2007-02-23 | 2008-08-28 | Warren Michael Levy | Fluid level sensing device and methods of using same |
US7966273B2 (en) * | 2007-07-27 | 2011-06-21 | Schlumberger Technology Corporation | Predicting formation fluid property through downhole fluid analysis using artificial neural network |
AU2010254079B2 (en) * | 2009-05-26 | 2014-11-06 | Expro Meters, Inc. | Method and apparatus for monitoring multiphase fluid flow |
US9482077B2 (en) * | 2009-09-22 | 2016-11-01 | Baker Hughes Incorporated | Method for controlling fluid production from a wellbore by using a script |
US20110067882A1 (en) * | 2009-09-22 | 2011-03-24 | Baker Hughes Incorporated | System and Method for Monitoring and Controlling Wellbore Parameters |
US9046399B2 (en) * | 2010-06-15 | 2015-06-02 | Expro Meters, Inc. | Minimally intrusive monitoring of a multiphase process flow using a tracer and a spatially arranged array of at least two sensors on a flow pipe |
US9394783B2 (en) * | 2011-08-26 | 2016-07-19 | Schlumberger Technology Corporation | Methods for evaluating inflow and outflow in a subterranean wellbore |
US9347310B2 (en) * | 2012-09-20 | 2016-05-24 | Weatherford Technology Holdings, Llc | Multiphase flowmeter for subsea applications |
US9518434B1 (en) * | 2013-10-23 | 2016-12-13 | Drill Cool Systems, Inc. | System for ascertaining and managing properties of a circulating wellbore fluid and method of using the same |
EP2925964A4 (en) * | 2014-01-27 | 2016-07-13 | Halliburton Energy Services Inc | Optical fluid model base construction and use |
EP3100161B1 (en) * | 2014-03-12 | 2022-11-23 | Landmark Graphics Corporation | Modified black oil model for calculating mixing of different fluids in a common surface network |
US10280722B2 (en) * | 2015-06-02 | 2019-05-07 | Baker Hughes, A Ge Company, Llc | System and method for real-time monitoring and estimation of intelligent well system production performance |
-
2015
- 2015-06-17 WO PCT/US2015/036270 patent/WO2016204752A1/en active Application Filing
- 2015-06-17 CA CA2985574A patent/CA2985574A1/en not_active Abandoned
- 2015-06-17 US US15/567,351 patent/US20180112517A1/en not_active Abandoned
- 2015-06-17 GB GB1718753.5A patent/GB2555962A/en not_active Withdrawn
- 2015-06-17 AU AU2015399038A patent/AU2015399038A1/en not_active Abandoned
-
2016
- 2016-04-22 FR FR1653583A patent/FR3037683B1/en not_active Expired - Fee Related
- 2016-05-11 AR ARP160101353A patent/AR104588A1/en unknown
-
2017
- 2017-10-26 NO NO20171712A patent/NO20171712A1/en not_active Application Discontinuation
Patent Citations (5)
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US20030182061A1 (en) * | 2002-03-19 | 2003-09-25 | Ferworn Kevin A. | Method and apparatus for simulating PVT parameters |
US20110224835A1 (en) * | 2009-06-03 | 2011-09-15 | Schlumberger Technology Corporation | Integrated flow assurance system |
US20120209542A1 (en) * | 2009-08-11 | 2012-08-16 | Expro Meters, Inc. | Method and apparatus for monitoring multiphase fluid flow |
US20130035920A1 (en) * | 2011-08-02 | 2013-02-07 | Saudi Arabian Oil Company | Methods for performing a fully automated workflow for well performance model creation and calibration |
US20140102697A1 (en) * | 2012-10-16 | 2014-04-17 | Expro Meters, Inc. | Systems and methods for managing hydrocarbon material producing wellsites using clamp-on flow meters |
Also Published As
Publication number | Publication date |
---|---|
GB2555962A (en) | 2018-05-16 |
AR104588A1 (en) | 2017-08-02 |
US20180112517A1 (en) | 2018-04-26 |
FR3037683B1 (en) | 2019-08-02 |
FR3037683A1 (en) | 2016-12-23 |
GB201718753D0 (en) | 2017-12-27 |
CA2985574A1 (en) | 2016-12-22 |
AU2015399038A1 (en) | 2017-11-09 |
NO20171712A1 (en) | 2017-10-26 |
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