GB2591391A - Using distributed sensor data to control cluster efficiency downhole - Google Patents

Using distributed sensor data to control cluster efficiency downhole Download PDF

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Publication number
GB2591391A
GB2591391A GB2103738.7A GB202103738A GB2591391A GB 2591391 A GB2591391 A GB 2591391A GB 202103738 A GB202103738 A GB 202103738A GB 2591391 A GB2591391 A GB 2591391A
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GB
United Kingdom
Prior art keywords
pump
downhole
data
wellbore
surface data
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
GB2103738.7A
Other versions
GB202103738D0 (en
GB2591391B (en
Inventor
Madasu Srinath
Dev Ashwani
Prasad Rangarajan Keshava
Priyadarshy Satyam
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Landmark Graphics Corp
Original Assignee
Landmark Graphics Corp
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Landmark Graphics Corp filed Critical Landmark Graphics Corp
Publication of GB202103738D0 publication Critical patent/GB202103738D0/en
Publication of GB2591391A publication Critical patent/GB2591391A/en
Application granted granted Critical
Publication of GB2591391B publication Critical patent/GB2591391B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

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Classifications

    • E—FIXED CONSTRUCTIONS
    • E21—EARTH DRILLING; MINING
    • E21B—EARTH DRILLING, e.g. DEEP 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
    • E21B43/25—Methods for stimulating production
    • E21B43/26—Methods for stimulating production by forming crevices or fractures
    • E—FIXED CONSTRUCTIONS
    • E21—EARTH DRILLING; MINING
    • E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B47/00—Survey of boreholes or wells
    • E21B47/12—Means for transmitting measuring-signals or control signals from the well to the surface, or from the surface to the well, e.g. for logging while drilling
    • E—FIXED CONSTRUCTIONS
    • E21—EARTH DRILLING; MINING
    • E21B—EARTH DRILLING, e.g. DEEP 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 DRILLING; MINING
    • E21B—EARTH DRILLING, e.g. DEEP 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 DRILLING; MINING
    • E21B—EARTH DRILLING, e.g. DEEP 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
    • E21B47/07—Temperature
    • E—FIXED CONSTRUCTIONS
    • E21—EARTH DRILLING; MINING
    • E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B47/00—Survey of boreholes or wells
    • E21B47/10—Locating fluid leaks, intrusions or movements
    • E—FIXED CONSTRUCTIONS
    • E21—EARTH DRILLING; MINING
    • E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B47/00—Survey of boreholes or wells
    • E21B47/12—Means for transmitting measuring-signals or control signals from the well to the surface, or from the surface to the well, e.g. for logging while drilling
    • E21B47/13—Means for transmitting measuring-signals or control signals from the well to the surface, or from the surface to the well, e.g. for logging while drilling by electromagnetic energy, e.g. radio frequency
    • E21B47/135—Means for transmitting measuring-signals or control signals from the well to the surface, or from the surface to the well, e.g. for logging while drilling by electromagnetic energy, e.g. radio frequency using light waves, e.g. infrared or ultraviolet waves
    • E—FIXED CONSTRUCTIONS
    • E21—EARTH DRILLING; MINING
    • E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B2200/00—Special features related to earth drilling for obtaining oil, gas or water
    • E21B2200/22—Fuzzy logic, artificial intelligence, neural networks or the like

Abstract

A system for determining real time cluster efficiency for a pumping operation in a wellbore includes a pump, a surface sensor, a downhole sensor system, and a computing device. The pump can pump slurry or diverter material in the wellbore. The surface sensor can be positioned at a surface of the wellbore to detect surface data about the pump. The downhole sensor system can be positioned in the wellbore to detect downhole data about an environment of the wellbore. The computing device can receive the surface data from the surface sensor, receive the downhole data from the downhole sensor system, apply the surface data and the downhole data to a long short-term memory (LSTM) neural network to produce a predicted cluster efficiency associated with operational settings of the pump, and control the pump using the operational settings to achieve the predicted cluster efficiency.

Claims (20)

Claims
1. A system comprising: a pump in operable communication with a wellbore having multiple stages, to pump slurry or diverter material into the wellbore; a surface sensor positionable at a surface of the wellbore to detect surface data about the pump; a downhole sensor system positionable in the wellbore to detect downhole data about an environment of the wellbore; and a computing device to communicate with the pump, the surface sensor, and the downhole sensor system, the computing device being operable to: receive the surface data from the surface sensor; receive the downhole data from the downhole sensor system; apply the surface data and the downhole data to a long short-term memory (LSTM) neural network to produce a predicted cluster efficiency associated with operational settings of the pump; and control the pump using the operational settings to achieve the predicted cluster efficiency.
2. The system of claim 1 , wherein the LSTM neural network is a deep recurrent neural network (DRNN) that is trained using a subset of the surface data and of the downhole data.
3. The system of claim 1 , wherein the surface data includes a pump pressure from the pump and flow rate of slurry or diverter material.
4. The system of claim 1 , wherein the downhole sensor system is a distributed acoustic sensing system or a distributed temperature sensing system implemented by a fiber optic cable.
5. The system of claim 1 , wherein the downhole data includes flow rate percentage at different depth ranges in the wellbore.
6. The system of claim 1 , wherein the predicted cluster efficiency represents a measurement of how uniformly that slurry or diverter material is distributed among perforation clusters in the wellbore.
7. The system of claim 1 , wherein the computing device is operable to control the pump using the operational settings to achieve the predicted cluster efficiency substantially in real time with respect to receiving the surface data and the downhole data.
8. A method comprising: receiving surface data from a surface sensor positioned at a surface of a wellbore to detect surface data about a pump; receiving downhole data from a downhole sensor system disposed in the wellbore to detect the downhole data about an environment of the wellbore; applying the surface data and the downhole data to a long short-term memory (LSTM) neural network to produce a predicted cluster efficiency associated with operational settings of the pump; and controlling the pump using the operational settings to achieve the predicted cluster efficiency.
9. The method of claim 8, wherein the LSTM neural network is a deep neural network (DRNN) that is trained using a subset of the surface data and of the downhole data.
10. The method of claim 8, wherein the surface data includes a pump pressure from the pump and flow rate of slurry or diverter material.
11. The method of claim 8, wherein the downhole sensor system is a distributed acoustic sensing system or a distributed temperature sensing system implemented by a fiber optic cable.
12. The method of claim 8, wherein the downhole data includes flow rate percentage at different depth ranges in the wellbore.
13. The method of claim 8, wherein the predicted cluster efficiency represents a measurement of how uniformly that slurry or diverter material is distributed among perforation clusters in the wellbore.
14. The method of claim 8, wherein controlling the pump using the operational settings to achieve the predicted cluster efficiency comprises controlling the pump substantially in real time with respect to receiving the surface data and the downhole data.
15. A non-transitory computer-readable medium that includes instructions that are executable by a processing device for causing the processing device to perform operations comprising: receiving surface data from a surface sensor positioned at a surface of a wellbore to detect surface data about a pump; receiving downhole data from a downhole sensor system disposed in the wellbore to detect the downhole data about an environment of the wellbore; applying the surface data and the downhole data to a long short-term memory (LSTM) neural network to produce a predicted cluster efficiency associated with operational settings of the pump; and controlling the pump using the operational settings to achieve the predicted cluster efficiency.
16. The non-transitory computer-readable medium of claim 15, wherein the LSTM neural network is a deep recurrent neural network (DRNN) that is trained using a subset of the surface data and of the downhole data.
17. The non-transitory computer-readable medium of claim 15, wherein the surface data includes a pump pressure from the pump and flow rate of slurry or diverter material, wherein the downhole data includes flow rate percentage at different depth ranges in the wellbore.
18. The non-transitory computer-readable medium of claim 15, wherein the downhole sensor system is a distributed acoustic sensing system or a distributed temperature sensing system implemented by a fiber optic cable.
19. The non-transitory computer-readable medium of claim 15, wherein the predicted cluster efficiency represents a measurement of how uniformly that slurry or diverter material is distributed among perforation clusters in the wellbore.
20. The non-transitory computer-readable medium of claim 15, wherein the operation of controlling the pump using the operational settings to achieve the predicted cluster efficiency comprises controlling the pump substantially in real time with respect to receiving the surface data and the downhole data.
GB2103738.7A 2018-11-30 2018-11-30 Using distributed sensor data to control cluster efficiency downhole Active GB2591391B (en)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/US2018/063251 WO2020112132A1 (en) 2018-11-30 2018-11-30 Using distributed sensor data to control cluster efficiency downhole

Publications (3)

Publication Number Publication Date
GB202103738D0 GB202103738D0 (en) 2021-05-05
GB2591391A true GB2591391A (en) 2021-07-28
GB2591391B GB2591391B (en) 2022-07-13

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GB2103738.7A Active GB2591391B (en) 2018-11-30 2018-11-30 Using distributed sensor data to control cluster efficiency downhole

Country Status (6)

Country Link
US (1) US20220034220A1 (en)
CA (1) CA3109470A1 (en)
FR (1) FR3090728A1 (en)
GB (1) GB2591391B (en)
NO (1) NO20210545A1 (en)
WO (1) WO2020112132A1 (en)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2023249663A1 (en) * 2022-06-24 2023-12-28 Halliburton Energy Services, Inc. Data-driven feature engineering and machine learning for analysis of distributed sensing data

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20020152030A1 (en) * 2001-02-16 2002-10-17 Schultz Roger L. Downhole sensing and flow control utilizing neural networks
US20080306892A1 (en) * 2007-06-11 2008-12-11 Alexander Crossley Multiphase flow meter for electrical submersible pumps using artificial neural networks
WO2009111412A2 (en) * 2008-03-03 2009-09-11 Intelliserv, Inc. Monitoring downhole conditions with drill string distributed measurement system
US20110247824A1 (en) * 2010-04-12 2011-10-13 Hongren Gu Automatic stage design of hydraulic fracture treatments using fracture height and in-situ stress
WO2017041074A1 (en) * 2015-09-03 2017-03-09 Schlumberger Technology Corporation Method of integrating fracture, production, and reservoir operations into geomechanical operations of a wellsite

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10935684B2 (en) * 2015-10-28 2021-03-02 Halliburton Energy Services, Inc. Near real-time return-on-fracturing-investment optimization for fracturing shale and tight reservoirs
US10761894B2 (en) * 2017-10-30 2020-09-01 Sas Institute Inc. Methods and systems for automated monitoring and control of adherence parameters

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20020152030A1 (en) * 2001-02-16 2002-10-17 Schultz Roger L. Downhole sensing and flow control utilizing neural networks
US20080306892A1 (en) * 2007-06-11 2008-12-11 Alexander Crossley Multiphase flow meter for electrical submersible pumps using artificial neural networks
WO2009111412A2 (en) * 2008-03-03 2009-09-11 Intelliserv, Inc. Monitoring downhole conditions with drill string distributed measurement system
US20110247824A1 (en) * 2010-04-12 2011-10-13 Hongren Gu Automatic stage design of hydraulic fracture treatments using fracture height and in-situ stress
WO2017041074A1 (en) * 2015-09-03 2017-03-09 Schlumberger Technology Corporation Method of integrating fracture, production, and reservoir operations into geomechanical operations of a wellsite

Also Published As

Publication number Publication date
CA3109470A1 (en) 2020-06-04
GB202103738D0 (en) 2021-05-05
GB2591391B (en) 2022-07-13
US20220034220A1 (en) 2022-02-03
NO20210545A1 (en) 2021-04-30
WO2020112132A1 (en) 2020-06-04
FR3090728A1 (en) 2020-06-26

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