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Virtual Multiphase Flow Meter using combination of Ensemble Learning and first principle physics based

Mohd Azmin Ishak
Mohd Azmin Ishak
, 
Tareq Aziz Hasan Al-qutami
Tareq Aziz Hasan Al-qutami
 and 
Idris Ismail
Idris Ismail
   | Jun 29, 2022
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Article Category: Article
Published Online: Jun 29, 2022
Page range: -
Received: Nov 15, 2021
DOI: https://doi.org/10.2478/ijssis-2022-0010
Keywords
© 2022 Mohd Azmin Ishak et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1

Typical real-time measurement devices installed in upstream production well.
Typical real-time measurement devices installed in upstream production well.

Figure 2

MPM (now TechnipFMC™) Multiphase Flow Meter.
MPM (now TechnipFMC™) Multiphase Flow Meter.

Figure 3

Well 101 and 102 is diverted manually through a common Multiphase Flow Meter (MPFM).
Well 101 and 102 is diverted manually through a common Multiphase Flow Meter (MPFM).

Figure 4

Overall Virtual Flow Meter architecture incorporating the combiner.
Overall Virtual Flow Meter architecture incorporating the combiner.

Figure 5

Illustration of bias-variance trade-off.
Illustration of bias-variance trade-off.

Figure 6

Data-driven VFM based on ensemble learning.
Data-driven VFM based on ensemble learning.

Figure 7

3 Distinct models were developed namely subsurface model, surface model and complete model.
3 Distinct models were developed namely subsurface model, surface model and complete model.

Figure 8

Fluid characterization in Multiflash®.
Fluid characterization in Multiflash®.

Figure 9

Inflow Performance Relationship (IPR) and Vertical Lift Performance (VLP) (Fetoui, [Online]).
Inflow Performance Relationship (IPR) and Vertical Lift Performance (VLP) (Fetoui, [Online]).

Figure 10

Inflow Performance Model (IPR) provide relationship between well flowing bottom-hole pressure, Pwf as a function of production rate, BPD.
Inflow Performance Model (IPR) provide relationship between well flowing bottom-hole pressure, Pwf as a function of production rate, BPD.

Figure 11

The intersection of the IPR with the VLP, called the operating point, yields the well deliverability.
The intersection of the IPR with the VLP, called the operating point, yields the well deliverability.

Figure 12

Geothermal profile along the vertical well (depth), ft vs. Temperature ºF.
Geothermal profile along the vertical well (depth), ft vs. Temperature ºF.

Figure 13

Initial calibration step in Flux Simulator.
Initial calibration step in Flux Simulator.

Figure 14

Flux Simulator in autonomous optimization.
Flux Simulator in autonomous optimization.

Figure 15

Combiner general workflow.
Combiner general workflow.

Figure 16

Detailed combiner algorithm.
Detailed combiner algorithm.

Figure 17

Model performance evaluation metrics.
Model performance evaluation metrics.

Figure 18

Cumulative deviation for well 101 (a) Qgas and (b) Qoil.
Cumulative deviation for well 101 (a) Qgas and (b) Qoil.

Figure 19

Cumulative deviation for well 102 (a) Qgas and (b) Qoil.
Cumulative deviation for well 102 (a) Qgas and (b) Qoil.

Figure 20

Error bands for well 101 (a) for Qgas (b) for Qoil.
Error bands for well 101 (a) for Qgas (b) for Qoil.

Figure 21

Error bands for well 102 (a) for Qgas, (b) for Qoil.
Error bands for well 102 (a) for Qgas, (b) for Qoil.

Figure 22

Flow rates with confidence intervals for well 101 (a) for Qgas, (b) for Qoil.
Flow rates with confidence intervals for well 101 (a) for Qgas, (b) for Qoil.

Figure 23

Flow rates with confidence intervals for well 102 (a) for Qgas, (b) for Qoil.
Flow rates with confidence intervals for well 102 (a) for Qgas, (b) for Qoil.

Figure 24

Flow rate with confidence interval for Qwater well 102.
Flow rate with confidence interval for Qwater well 102.

Pilot Experiment Parameters.

Parameter Detail
Producing Wells Well 101 and Well 102
Well-testing equipment Shared MPFM
Flow type Multiphase (3 phases)
Training data 6 weeks multi-rate well-tests
Online test duration 6 months
Measurements Downhole P/T, Upstream P/T, downstream P/T, Choke opening
Data source OSIsoft PI

Flow rate deviation (delta Q) performance summary for data-driven VFM estimators.

DD-VFM
Well Output Full Surface Subsurface Combiner
101 Qgas 216 248 222 189
Qoil 242 263 377 248
Qwater 0.5 0.3 - 0.2
102 Qgas 68 135 106 90
Qoil 35 25 40 27
Qwater 12 16 6 10

Combiner group description.

Type Description
combiner-all Combining the three DD-VFM estimator categories and TF-VFM
combiner-dd Combining the three DD-VFM estimator categories only
combiner-full Combining the full-type DD-VFM estimator and TF-VFM
combiner-surface Combining the surface-type DD-VFM estimator and TF-VFM

MAPE performance summary for data-driven VFM estimators.

DD-VFM
Well Output Full Surface Subsurface Combiner-dd
101 Qgas 16.6 19.2 16.9 13.9
Qoil 9.7 10.0 15.7 9.8
102 Qgas 6.0 9.5 10.2 7.7
Qoil 3.1 2.3 3.8 2.5

Format for well test report to be for both training and testing dataset.

INPUT OUTPUT
Date/Time Well No Flowing tubing head pressure (FTHP), psi Flowing tubing head temperature (FTHT), °C Differential pressure across the well (dPWell), psi Differential temperature across the well (dTWell), °C Choke opening (CV), % Differential pressure across the choke (dPChoke) Differential temperature across the choke (dTChoke) Oil Flow Rate (bbl/day) Gas Flow Rate (MMscf/day) Water Flow Rate (bbl/day)
DD:MM:YYYY HH:MM:SS 101 – – – – – – – – – –
DD:MM:YYYY HH:MM:SS 102 – – – – – – – – – –
DD:MM:YYYY HH:MM:SS 101 – – – – – – – – – –
DD:MM:YYYY HH:MM:SS 102 – – – – – – – – – –

Tested combiner VFM.

Well Output Hybrid-VFM Input Estimators
101 Qgas combiner-all [full, surface, subsurface, TF]
combiner-dd [full, surface, subsurface]
combiner-full [full, TF]
combiner-surface [surface, TF]
Qoil combiner-all [full, surface, subsurface, TF]
combiner-dd [full, surface, subsurface]
combiner-full [full, TF]
combiner-surface [surface, TF]
Qwater combiner-all [full, surface, TF]
combiner-dd [full, surface]
combiner-full [full, TF]
combiner-surface [surface, TF]
102 Qgas combiner-all [full, surface, subsurface, TF]
combiner-dd [full, surface, subsurface]
combiner-full [full, TF]
combiner-surface [surface, TF]
Qoil combiner-all [full, surface, subsurface, TF]
combiner-dd [full, surface, subsurface]
combiner-full [full, TF]
combiner-surface [surface, TF]
Qwater combiner-all [full, surface, subsurface, TF]
combiner-dd [full, surface, subsurface]
combiner-full [full, TF]
combiner-surface [surface, TF]

Data-driven model category with the associated inputs and outputs for each well.

Well Model Category Inputs Output Algorithm
101 full CV, FTHP, FTHT, dPWell, dTWell, dPChoke, dTChoke Qgas AdaBoost
Qoil RandomForest
Qwater AdaBoost
surface CV, FTHP, FTHT, dPChoke, dTChoke Qgas RandomForest
Qoil RandomForest
Qwater AdaBoost
subsurface FTHP, FTHT, dPWell, dTWell Qgas AdaBoost
Qoil RandomForest
102 full CV, FTHP, FTHT, dPWell, dTWell, dPChoke, dTChoke Qgas Bagging
Qoil RandomForest
Qwater AdaBoost
surface CV, FTHP, FTHT, dPChoke, dTChoke Qgas AdaBoost
Qoil RandomForest
Qwater AdaBoost
subsurface FTHP, FTHT, dPWell, dTWell Qgas RandomForest
Qoil AdaBoost
Qwater Bagging

Flow rate deviation (delta Q) performance for all VFM.

Hybrid VFM (combiner)
Well Output DD-VFM TF-VFM All Full+TF Surface+TF
101 Qgas 214 155 186 148 146
Qoil 278 221 220 126 146
Qwater 0.2 15.4 0.6 0.6 0.7
102 Qgas 83 85 77 54 87
Qoil 14 152 12.6 12 13
Qwater 7 53 4 4 5

MAPE performance summary for all VFM.

Hybrid VFM (combiner)
Well Output DD-VFM TF-VFM All Full+TF Surface+TF
101 Qgas 16.6 9.6 11.8 9.0 8.7
Qoil 9.7 8.3 8.5 4.7 5.5
102 Qgas 6.0 5.6 4.9 3.6 5.4
Qoil 3.1 17.5 1.3 1.2 1.3
eISSN:
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Language:
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