IP Library › Granted Patent US 12,523,516
Granted Patent B2
US 12,523,516 · App. 18/365,383 · Granted Jan 13, 2026

Flow meter calibration

Inventors: Sourabh Shukla (Algiers, DZ); Garud Sridhar (London, GB)
Assignee: Schlumberger Technology Corporation
G01F25/10E21B47/10
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,523,516
App. No.
18/365,383
Granted
Jan 13, 2026
Kind
B2
Abstract

A method can include receiving flow rate estimates from a computational, virtual flow meter at a wellsite; receiving flow rate measurements from a physical flow meter at the wellsite; and calling for calibration of the physical flow meter based on the flow rate estimates and the flow rate measurements.

Claims (26)

1 . A method comprising:

receiving flow rate measurements from a physical flow meter at a wellsite;

receiving, via a network interface at the wellsite, flow rate estimates from a computational, virtual flow meter at the wellsite;

tracking differences comprising at least one of a first difference between the flow rate estimates and the flow rate measurements with respect to time or a second difference between a first stability exhibited by the flow rate estimates and a second stability exhibited by the flow rate measurements;

predicting a time for calibration using a trained machine learning model;

optimizing a schedule for calibrating a plurality of physical flow meters including the physical flow meter based at least in part on the time, wherein optimizing comprises utilizing one or more criteria, and wherein the one or more criteria comprise a distance criterion between the wellsite and at least one other wellsite; and

calibrating the physical flow meter via the network interface based on at least one of (i) the flow rate estimates and the flow rate measurements or (ii) the differences.

2 . The method of claim 1 , wherein calibrating the physical flow meter comprises performing manual tasks at the wellsite.

3 . The method of claim 1 , wherein calibrating the physical flow meter comprises performing tasks at the wellsite responsive to receipt of a command by the network interface at the wellsite.

4 . The method of claim 1 , wherein the one or more criteria further comprise a time span criterion.

5 . The method of claim 1 , wherein the physical flow meter is at a manifold.

6 . The method of claim 5 , wherein a remainder of the plurality of physical flow meters is upstream from the manifold.

7 . The method of claim 6 , further comprising a plurality of computational, virtual flow meters including the computational, virtual flow meter, each computational, virtual flow meter of the plurality of computational, virtual flow meters corresponding to a respective physical flow meter of the plurality of physical flow meters.

8 . A method comprising:

receiving flow rate measurements from a physical flow meter at a manifold at a wellsite, wherein a plurality of additional physical flow meters is upstream from the manifold;

receiving, via a network interface at the wellsite, flow rate estimates from a computational, virtual flow meter of a plurality of computational, virtual flow meters, wherein the computational, virtual flow meter is at the wellsite and corresponds to the physical flow meter, and wherein each computational, virtual flow meter of a remainder of the plurality of computational, virtual flow meters corresponds to a respective additional physical flow meter of the plurality of additional physical flow meters;

tracking differences comprising at least one of a first difference between the flow rate estimates and the flow rate measurements with respect to time or a second difference between a first stability exhibited by the flow rate estimates and a second stability exhibited by the flow rate measurements; and

calibrating the physical flow meter via the network interface based on at least one of (i) the flow rate estimates and the flow rate measurements or (ii) the differences.

9 . The method of claim 8 , wherein calibrating the physical flow meter comprises performing manual tasks at the wellsite.

10 . The method of claim 8 , wherein calibrating the physical flow meter comprises performing tasks at the wellsite responsive to receipt of a command by the network interface at the wellsite.

11 . The method of claim 8 , comprising predicting a time for calibration using a trained machine learning model.

12 . The method of claim 11 , comprising optimizing a schedule for calibrating the physical flow meter and the plurality of additional physical flow meters based at least in part on the time.

13 . The method of claim 12 , wherein optimizing comprises utilizing one or more criteria.

14 . The method of claim 13 , wherein the one or more criteria comprise a time span criterion.

15 . The method of claim 8 , wherein calibrating the physical flow meter occurs responsive to a time on a schedule.

16 . The method of claim 15 , wherein the schedule comprises times for calibration of the physical flow meter and the plurality of additional physical flow meters.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Aug 10, 2023
From: SHUKLA, SOURABH; SRIDHAR, GARUD
To: SCHLUMBERGER TECHNOLOGY CORPORATION
Reel/Frame 064544/0365 →
Continuity (2)
Provisional Application 63395636 · Aug 5, 2022
Related Publication 20240044691A1 · Feb 8, 2024
References Cited (27)
US 7523639B2 · Hays · 2009 [cited by applicant]
US 9658097B2 · Berndt · 2017 [cited by examiner]
US 11697991B2 · Machocki · 2023 [cited by examiner]
US 11965769B2 · Collver · 2024 [cited by examiner]
US 20080295568A1 · Nanaji · 2008 [cited by examiner]
US 20100229965A1 · Kashima · 2010 [cited by examiner]
US 20120095733A1 · Rossi · 2012 [cited by examiner]
US 20130340498A1 · Tanabe · 2013 [cited by examiner]
US 20140379134A1 · Tsuchiya · 2014 [cited by examiner]
US 20150276449A1 · Ito · 2015 [cited by examiner]
US 20160356125A1 · Bello · 2016 [cited by examiner]
US 20170160727A1 · Ishikawa · 2017 [cited by examiner]
US 20180073902A1 · Gonzaga · 2018 [cited by examiner]
US 20180073904A1 · Parolini et al. · 2018 [cited by applicant]
US 20210355814A1 · Shetty · 2021 [cited by examiner]
US 20220057244A1 · Gonzaga · 2022 [cited by examiner]
US 20240003242A1 · Ambade et al. · 2024 [cited by applicant]
JP 2021085723A · 2021 [cited by applicant]
KR 100971785B1 · 2010 [cited by applicant]
KR 102162312B1 · 2020 [cited by applicant]
KR 20210075361A · 2021 [cited by applicant]
WO WO2018195368A1 · 2018 [cited by examiner]
WO 2023091386A1 · 2023 [cited by applicant]
Kargarpour et al., “Oil and gas well rate estimation by choke formula: semi-analytical approach”, Journal of Petroleum Exploration and Production Technology, vol. 9, 2019, pp. 2375-2386. [cited by applicant]
Search Report and Written Opinion of International Patent Application No. PCT/US2023/071644 dated Nov. 7, 2023, 12 pages. [cited by applicant]
Ishak, M. A. B. et al., “Data Driven Versus Transient Multiphase Flow Simulator for Virtual Flow Meter Application”, 2020 8th International Conference on Intelligent and Advanced Systems (ICIAS), IEEE, 2021, pp. 1-4. [cited by applicant]
Mercante, R. et al., “Virtual flow predictor using deep neural networks”, Journal of Petroleum Science and Engineering, 2022, 213, 11 pages. [cited by applicant]