EP3693538A1 - Procédé pour déterminer les caractéristiques de fond de trou d'un puits de production - Google Patents

Procédé pour déterminer les caractéristiques de fond de trou d'un puits de production Download PDF

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Publication number
EP3693538A1
EP3693538A1 EP19156494.7A EP19156494A EP3693538A1 EP 3693538 A1 EP3693538 A1 EP 3693538A1 EP 19156494 A EP19156494 A EP 19156494A EP 3693538 A1 EP3693538 A1 EP 3693538A1
Authority
EP
European Patent Office
Prior art keywords
downhole
physical property
tool
determining
characteristic
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.)
Withdrawn
Application number
EP19156494.7A
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German (de)
English (en)
Inventor
Carsten NESGAARD
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.)
Welltec AS
Original Assignee
Welltec AS
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 Welltec AS filed Critical Welltec AS
Priority to EP19156494.7A priority Critical patent/EP3693538A1/fr
Priority to PCT/EP2020/053341 priority patent/WO2020165098A1/fr
Priority to US16/786,095 priority patent/US20200263538A1/en
Publication of EP3693538A1 publication Critical patent/EP3693538A1/fr
Withdrawn legal-status Critical Current

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    • 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
    • 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
    • E21B49/00—Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells
    • E21B49/003—Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells by analysing drilling variables or conditions
    • 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
    • E21B49/00—Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or 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
    • E21B49/00—Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells
    • E21B49/005—Testing the nature of borehole walls or the formation by using drilling mud or cutting data
    • 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
    • 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

Definitions

  • the present invention relates to a method for determining downhole characteristics in a production well.
  • Production wells are an important part of certain industries, especially for mining and oil drilling.
  • various types of operations downhole such as drilling, cutting, etc.
  • it is often difficult to achieve accurate measurements of downhole characteristics For example, downhole measurements normally require interruption of production, which costs precious operational time.
  • certain measurements may be made, not all downhole characteristics are easily measured.
  • an object of the present invention is to provide a method for determining these downhole characteristics using estimations from more easily measured data.
  • the above and other objects of the invention are achieved, in full or in part, by a method for determining one or more downhole characteristics in a production well.
  • the method comprises measuring a physical property of the production well using at least one sensor; pre-processing the measured values of the physical property using feature scaling and/or an automatic gain control technique; and determining one or more downhole characteristics using a supervised machine learning method.
  • the supervised machine learning method comprises cross-validating a predictive analytics algorithm with training data that are previously measured physical properties, and applying one or more algorithms to the measured values of the physical property to converge on an estimate for the determined downhole characteristic(s).
  • the present solution is purely data-driven, and no physical model is required. Especially, the present invention provides a regression model to predict a quantity of the downhole characteristic(s) from measured data in the production well.
  • physical model is meant a mathematical model which describes how the earth formation responds to the transmitting signal from the downhole tool.
  • Using feature scaling and/or automatic gain control technique changes how the data is distributed across the possible data values. Such pre-processing will improve any machine learning classifier to more accurately perform classification.
  • the training data which are previously measured physical properties, are measured using the at least one sensor.
  • the method may further comprise transmitting a control signal based on the determined downhole characteristic(s). This is advantageous in that improved control of equipment can be accomplished.
  • the control signal may be transmitted to a data-processing unit at the surface or to an operator.
  • the control signal may be transmitted to a downhole tool. Hence, real-time control of downhole equipment is possible.
  • the method may further comprise displaying the measured physical property and/or the determined downhole characteristic(s) on a display, which allows for immediate feedback of the downhole characteristic to an operator.
  • the method may further comprise a pre-determining step comprising using a non-linear or a linear system identification. Improved pre-determining of the downhole characteristic is thereby provided for.
  • the pre-determining step may be performed after the step of measuring a physical property of the production well using at least one sensor and before the step of pre-processing the measured values of the physical property using feature scaling and/or automatic gain control techniques.
  • the physical property may be measured using at least one sensor in at least two different configurations, and pre-determining the downhole characteristic(s) comprises using the nonlinear or linear system identification.
  • the method will thereby have access to increased amount of data, thereby improving the accuracy of the method.
  • the physical property may comprise the permittivity of a fluid in the well.
  • the method can thereby be performed in order to determine the water flow regime downhole.
  • the physical property may comprise the current provided to a downhole tool of the well.
  • the downhole tool may be a suction tool comprising a bailer, and in an embodiment, the downhole characteristics comprise the fullness of the bailer.
  • the method will thereby provide accurate and reliable information of the downhole tool operation, which will allow for improved monitoring of downhole operation as well as predictive operation and maintenance.
  • the downhole tool is a milling tool having miller teeth
  • the downhole characteristics may comprise the miller teeth health.
  • the method will provide accurate and reliable information of the downhole tool operation, which will allow for improved monitoring of downhole operation as well as predictive operation and maintenance.
  • At least one sensor may be an electrode and/or an ampere metre. The method can thereby be performed using standard equipment.
  • the method may further comprise registering the angle of the at least one sensor with respect to gravity, preferably using an accelerometer. Improved determining of the downhole characteristic is thereby possible.
  • the supervised machine learning method may be configured to operate a random forest algorithm.
  • the method comprises pre-processing the measured values of the physical property by applying feature scaling or an automatic gain control technique to overcome data imbalance, e.g. by changing how the data are distributed across the possible data values.
  • pre-processing will improve any machine learning classifier to more accurately perform classification.
  • a computer programme product comprising a computer-readable medium having thereon a computer programme comprising programme instructions.
  • the computer programme is loadable into a data-processing unit and adapted to cause execution of the method according to the first aspect when the computer programme is run by the data-processing unit.
  • Fig. 1 shows a system for determining one or more downhole characteristic(s) in a production well 10.
  • the system comprises a data-processing unit 30 configured to execute a method 100 for measuring one or more physical properties and to determine the one or more downhole characteristics in the production well 10.
  • the data-processing unit 30 is operatively connected to a sensor 12, which in turn is operatively connected to a downhole tool 14 of the well 10.
  • the downhole tool 14 may be a milling tool, a tubing cutting tool, a suction tool, such as a cleaning tool with a bailer, or any other tool suitable to be lowered downhole.
  • the downhole tool 14 may be provided with one or more sensors 12.
  • the one or more sensors 12 can be implemented as an ampere metre configured to measure 110 the current provided to the downhole tool 14.
  • the current provided to the downhole tool 14 will normally vary over time; in the case of a milling tool, less current will be needed as the miller teeth become worn out and damaged.
  • suction tools as the drive current will vary depending on the fullness of the bailer of the suction tool.
  • a machine learning method 200 is used to determine the miller teeth health based on the measured current values.
  • the machine learning method 200 will be further detailed with reference to Fig. 4 .
  • the method is preferably used to classify working status of the milling tool and to predict its remaining teeth life.
  • the working status of the milling tool could be clogged, breakthrough, or that the miller teeth are reaching the end of their lifetime.
  • Machine learning classifiers can therefore be used to classify the working status of the milling tool, and machine learning regressors can be used to predict the residue of the teeth life.
  • the downhole tool 14 may in an alternative embodiment be a suction tool comprising a bailer.
  • the current provided to the suction tool 14 also varies over time as the bailer fills up.
  • the machine learning method 200 may consequently be used in a similar manner to determine the fullness of the bailer from initial measurements of the current supplied to the suction tool.
  • the method can be used to predicting how full the associated bailors are.
  • the bailors store the debris and/or sand, and the ability to predict its fullness during an operation helps the field personnel plan for operation.
  • machine learning is used to help predict the fullness of the bailor based on the electrical current as the input.
  • the downhole tool 14 may also in an alternative embodiment be a downhole fluid property detection tool comprising multiple electrodes arranged in a circle or along a cylinder periphery as in Fig. 2b .
  • the side view of the sensors 12 for measuring the fluid physical property downhole is shown in Fig. 2a .
  • Four different arrangements of the sensors 12 are implemented by the tool. These arrangements are particularly suited for measuring fluid permittivity using electrodes 12, which may be used to determining a water flow regime of the well 10.
  • the term "water flow regime" should in this context be interpreted as a representation of the different fluids in the well, in particular at a common cross-section of the well.
  • Fig. 2a eight electrodes are distributed along the circumference of the tool.
  • two adjacent electrodes 12 are used.
  • two electrodes are used being spaced apart by one intermediate electrode.
  • two electrodes are used being spaced apart by two intermediate electrodes.
  • two electrodes are used being spaced apart by three intermediate electrodes.
  • the intermediate electrodes are not used.
  • the machine learning method 200 may be used to determine how these different types of measurements are to be processed, e.g. to outbalance the influences of noise.
  • the downhole fluid property detection tool 14 comprises an accelerometer 18 in order to measure the gravitational force acting on the downhole fluid property detection tool 14. The measured gravitational force is used to register 120 the angle of the electrodes 12. The angle of the electrodes 12 may be input to the machine learning method 200 to further improve the results.
  • the data processing unit 30 of Fig. 1 is further operatively connected to a display 16.
  • the data processing unit 30 may be configured to display 160 measured values of physical properties, determined downhole characteristics, or a combination of both on the display 16.
  • Fig. 3 schematically shows a method 100 for measuring downhole characteristics in a production well 10.
  • the method 100 comprises several steps 110-170. These steps 110-170 may be performed in any order, some may be skipped and others repeated, and different steps may be performed by different units of the data processing unit 30 and/or the downhole tool 14.
  • a measuring step 110 comprises measuring a physical property of the production well 10 using at least one sensor 12.
  • the sensor 12 may be any sensor suitable for measuring physical properties, such as an electrode, an ampere metre, a thermometer, or an optical sensor to mention a few. More than one sensor 12 may further be used, and more than one type of sensor 12 may be combined.
  • a registering step 120 comprises registering the angle of the at least one sensor 12 with respect to gravity, e.g. by using an accelerometer 18.
  • the accelerometer 18 may also be configured to register 120 the movement of the at least one sensor 12.
  • a pre-processing step 130 comprises pre-processing the measured values of the physical property using feature scaling and/or an automatic gain control technique to overcome data imbalance. This may also comprise using min-max normalisation, mean normalisation, Gaussian standardisation, amplifying and/or averaging measurements. The purpose of performing feature scaling and/or automatic gain control may be to balance highly varying magnitudes from the at least one sensor.
  • the method 100 may comprise an optional pre-determining step 125 before the processing step 140 and even before the optional pre-processing step 130.
  • the pre-determining step 125 comprises using linear or non-linear system identification.
  • the method may also comprise linear regression and/or fitting to known models.
  • the measured values of the physical property is determined to be linear or stepwise-linear (non-linear), and from such pre-determining step, the most suited feature scaling or automatic gain control can be chosen. From the test data, such linear or non-linear system identification may be unnecessary as such information may be known.
  • a determining step 150 comprises determining a downhole characteristic using a supervised machine learning method 200. This step is discussed further with reference to Fig. 4 .
  • An optional displaying step 160 comprises displaying the measured physical property and/or the determined downhole characteristic on a display 16.
  • the displaying step 160 may further comprise displaying the registered 120 angle or movement of the at least one sensor 12 on a display 16.
  • the displayed content may be useful for an operator to improve the operation of the production well 10.
  • the displaying step 160 may further comprise alerting an operator to a problem with the production well 10 based on the measurements made.
  • the alert may be visual or audial according to methods known by the skilled person.
  • An optional sending/transmitting step 170 comprises sending a control signal based on the determined downhole characteristic.
  • the signal may be sent to a downhole tool 14 in the well 10.
  • the control signal can e.g. stop a milling tool 14 because its teeth are too worn out or stop a suction tool 14 because its bailer is full.
  • the control signal can be used to control various operations downhole in order to improve the productivity of the well.
  • Fig. 4 shows a machine learning method 200 for determining a downhole characteristic.
  • the machine learning method 200 shown is a supervised machine learning method using a random forest algorithm.
  • a neural network, a Bayesian regression algorithm, or any supervised machine learning method may be used.
  • the machine learning method 200 starts by cross-validating 210 a predictive analytics algorithm with training data that are previously measured physical properties measured by the sensor of the downhole tool e.g. in another well or a testing facility. This comprises inputting a subset of previously measured physical properties with known corresponding downhole characteristics into the machine learning method 200 as training data, and the remaining subset of previously measured physical properties acts as test data to verify the estimation.
  • the set may have any number and range of data points sufficient to provide a reliable estimation.
  • the machine learning method 200 may use the training data to find a relationship between the measured physical property of the production well 10 and the corresponding downhole characteristics. Based on this, any new measurement 110 may apply 220 the found relationship to estimate downhole characteristic.
  • the machine learning method 200 further comprises applying 220 one or more algorithms, such as data mining algorithms, to the measured 110 values of the physical property to converge on an estimate for the determined downhole characteristic. This comprises using algorithms such as random forest to find a correlation between the measured 110 values of the physical property and the downhole characteristic using the training data. Once found, the relationship is applied to all new measured 110 values. Newly measured 110 values may also be fed back into the machine learning method 200 to further improve the algorithm used.
  • one or more algorithms such as data mining algorithms
  • the registered 120 acceleration data may be used instead of or in addition to the measured physical property, and a number of different physical properties may be used related to different downhole characteristics.
  • the method 200 may find them to be correlated or not, which may or may not improve the estimation of the downhole characteristics.

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  • Geology (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Mining & Mineral Resources (AREA)
  • Physics & Mathematics (AREA)
  • Environmental & Geological Engineering (AREA)
  • Fluid Mechanics (AREA)
  • General Life Sciences & Earth Sciences (AREA)
  • Geochemistry & Mineralogy (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Geophysics (AREA)
  • Investigating Or Analyzing Materials By The Use Of Electric Means (AREA)
  • Electrical Discharge Machining, Electrochemical Machining, And Combined Machining (AREA)
EP19156494.7A 2019-02-11 2019-02-11 Procédé pour déterminer les caractéristiques de fond de trou d'un puits de production Withdrawn EP3693538A1 (fr)

Priority Applications (3)

Application Number Priority Date Filing Date Title
EP19156494.7A EP3693538A1 (fr) 2019-02-11 2019-02-11 Procédé pour déterminer les caractéristiques de fond de trou d'un puits de production
PCT/EP2020/053341 WO2020165098A1 (fr) 2019-02-11 2020-02-10 Procédé de détermination de caractéristiques de fond de trou dans un puits de production
US16/786,095 US20200263538A1 (en) 2019-02-11 2020-02-10 Method for determining downhole characterics in a production well

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
EP19156494.7A EP3693538A1 (fr) 2019-02-11 2019-02-11 Procédé pour déterminer les caractéristiques de fond de trou d'un puits de production

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EP3693538A1 true EP3693538A1 (fr) 2020-08-12

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EP19156494.7A Withdrawn EP3693538A1 (fr) 2019-02-11 2019-02-11 Procédé pour déterminer les caractéristiques de fond de trou d'un puits de production

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EP (1) EP3693538A1 (fr)
WO (1) WO2020165098A1 (fr)

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2022216302A1 (fr) * 2021-04-05 2022-10-13 Landmark Graphics Corporation Modélisation de classement de trépan de forage en temps réel et technique de traitement

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20020178805A1 (en) * 2001-05-15 2002-12-05 Baker Hughes Inc. Method and apparatus for downhole fluid characterization using flexural mechanical resonators
US20080156486A1 (en) * 2006-12-27 2008-07-03 Schlumberger Oilfield Services Pump Control for Formation Testing
US20100126717A1 (en) * 2008-11-24 2010-05-27 Fikri Kuchuk Instrumented formation tester for injecting and monitoring of fluids
US20140121973A1 (en) * 2012-10-25 2014-05-01 Schlumberger Technology Corporation Prognostics And Health Management Methods And Apparatus To Predict Health Of Downhole Tools From Surface Check
US9022140B2 (en) * 2012-10-31 2015-05-05 Resource Energy Solutions Inc. Methods and systems for improved drilling operations using real-time and historical drilling data
US20170123097A1 (en) * 2015-10-28 2017-05-04 Baker Hughes Incorporated Real-time true resistivity estimation for logging-while-drilling tools
CN108005576A (zh) * 2017-11-30 2018-05-08 中建三局第建设工程有限责任公司 一种分离出渣的旋挖钻机及旋挖钻进施工方法

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20020178805A1 (en) * 2001-05-15 2002-12-05 Baker Hughes Inc. Method and apparatus for downhole fluid characterization using flexural mechanical resonators
US20080156486A1 (en) * 2006-12-27 2008-07-03 Schlumberger Oilfield Services Pump Control for Formation Testing
US20100126717A1 (en) * 2008-11-24 2010-05-27 Fikri Kuchuk Instrumented formation tester for injecting and monitoring of fluids
US20140121973A1 (en) * 2012-10-25 2014-05-01 Schlumberger Technology Corporation Prognostics And Health Management Methods And Apparatus To Predict Health Of Downhole Tools From Surface Check
US9022140B2 (en) * 2012-10-31 2015-05-05 Resource Energy Solutions Inc. Methods and systems for improved drilling operations using real-time and historical drilling data
US20170123097A1 (en) * 2015-10-28 2017-05-04 Baker Hughes Incorporated Real-time true resistivity estimation for logging-while-drilling tools
CN108005576A (zh) * 2017-11-30 2018-05-08 中建三局第建设工程有限责任公司 一种分离出渣的旋挖钻机及旋挖钻进施工方法

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US20200263538A1 (en) 2020-08-20
WO2020165098A1 (fr) 2020-08-20

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