WO2002066791A1 - Detection et reglage du debit fond de trou par reseaux neuronaux - Google Patents

Detection et reglage du debit fond de trou par reseaux neuronaux Download PDF

Info

Publication number
WO2002066791A1
WO2002066791A1 PCT/US2001/005123 US0105123W WO02066791A1 WO 2002066791 A1 WO2002066791 A1 WO 2002066791A1 US 0105123 W US0105123 W US 0105123W WO 02066791 A1 WO02066791 A1 WO 02066791A1
Authority
WO
WIPO (PCT)
Prior art keywords
sensor
neural network
outputs
output
downhole
Prior art date
Application number
PCT/US2001/005123
Other languages
English (en)
Other versions
WO2002066791A8 (fr
Inventor
Roger L. Schultz
Bruce H. Storm, Jr.
John Dennis
John M. Richardson
Original Assignee
Halliburton Energy Services, Inc.
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 Halliburton Energy Services, Inc. filed Critical Halliburton Energy Services, Inc.
Priority to PCT/US2001/005123 priority Critical patent/WO2002066791A1/fr
Priority to GB0230175A priority patent/GB2379513B/en
Priority to US10/076,960 priority patent/US6789620B2/en
Publication of WO2002066791A1 publication Critical patent/WO2002066791A1/fr
Publication of WO2002066791A8 publication Critical patent/WO2002066791A8/fr

Links

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
    • 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
    • E21B49/008—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 injection test; by analysing pressure variations in an injection or production test, e.g. for estimating the skin factor
    • 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
    • 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
    • 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
    • E21B47/10—Locating fluid leaks, intrusions or movements

Definitions

  • the present invention relates generally to operations performed in conjunction with a subterranean well and, in an embodiment described herein, more particularly provides a method of sensing a parameter in a well.
  • Such parameters may include pressure, temperature, resistivity, pH, dielectric, viscosity, flow rate, fluid composition, etc. This information enables a well operator to maintain efficient production from the well, plan future operations, comply with regulations, etc.
  • problems are encountered in sensing downhole parameters. Such problems include unavailability of a downhole sensor which senses the desired parameter, unavailability of a sensor which can withstand the well environment for an extended period of time, high cost of sensors which can withstand the well environment, short lifespan of downhole sensors, and unavailability of a high accuracy and/or resolution downhole sensor.
  • a suitable sensor for a desired parameter may be available for use at the surface, but it may not be designed for downhole use.
  • a sensor which otherwise meets all of the requirements for a downhole application may be prohibitively expensive.
  • Yet another example is given by the situation in which a high accuracy and/or resolution downhole sensor for the desired parameter is available, but the sensor has a limited lifespan in the well environment, thereby making it unsuitable for long term use in the well. Situations also arise in which a formerly operational downhole sensor becomes damaged, unable to communicate with the surface, or otherwise becomes unavailable for sensing the parameter in the well.
  • a method which solves the above problems in the art.
  • the method utilizes a neural network to determine at least one downhole parameter, even though a sensor for that parameter is not operational downhole at the time the parameter is determined.
  • a method in which parameters for individual zones of a well are determined without having operational sensors for those parameters downhole when the parameters are determined.
  • Training data sets are obtained using surface sensors, varied valve positions and temporary sensors.
  • the neural network is trained using this data.
  • the neural network is then used to determine the downhole parameters in response to inputting the surface sensors' outputs and the valve positions to the neural network.
  • a method in which a sensor's output is determined, even after the sensor has failed.
  • Training data sets from a time prior to the sensor's failure are obtained.
  • the training data sets include outputs of other downhole sensors, varied valve positions, etc.
  • the neural network is trained to output the failed sensors' output (before failure) in response to inputting the other sensor's outputs and the valve positions to the neural network.
  • a method in which a downhole parameter is determined, without using a permanent downhole sensor for that parameter. Training data sets are obtained using a temporary sensor for the desired parameter, and using other sensors for related parameters. The neural network is trained to produce the temporary sensor's outputs when the other sensors' outputs are input to the neural network. Thereafter, when the temporary sensor is no longer available for the desired parameter, the neural network will determine the temporary sensor's output in response to inputting the other sensors' outputs to the neural network.
  • a method is provided in which a high accuracy and/or resolution sensor is used to calibrate a lower accuracy and/ or resolution sensor. The calibration sensor is temporarily installed in the well along with the permanent downhole sensor.
  • Training data sets are obtained by recording outputs of both of the sensors in the well.
  • the neural network is trained using this data, so that the neural network outputs the calibration sensor outputs in response to inputting the downhole sensor's outputs to the neural network.
  • the downhole sensor's outputs are input to the neural network, which determines the corresponding outputs of the higher accuracy and/or resolution calibration sensor.
  • a "virtual" sensor is created downhole. That is, the output of a nonexistent downhole sensor is determined in response to inputting the outputs of other sensors, etc., to a trained neural network.
  • the neural network is trained using the outputs of a sensor temporarily in the well with the other sensors.
  • the sensor capable of sensing a desired parameter remains at the surface when training data is obtained.
  • the sensor for the desired parameter and the other sensors are at the surface when the training data is obtained.
  • a sensor is not used for the desired parameter, but known values for the desired parameter, along with the outputs of other sensors, are used to train the neural network.
  • a method wherein a combination of downhole sensors and surface sensors are used. These sensors may be used with a temporary sensor to obtain training data for a neural network, and for inputting to the neural network after training and after the temporary sensor is not available. Other pertinent information, such as valve positions, choke sizes, etc. may also be used. Downhole sensors may be advantageously positioned away from a harsh well environment where it is desired to sense a parameter, but sufficiently far from the surface that the sensors are not within a surface temperature affected zone of the well.
  • FIGS. 1-4 are schematic views of a first method embodying principles of the present invention.
  • FIGS. 5-7 are schematic views of a second method embodying principles of the present invention
  • FIGS. 8-11 are schematic views of a third method embodying principles of the present invention
  • FIGS. 12-14 are schematic views of a fourth method embodying principles of the present invention
  • FIGS. 15-17 are schematic views of a fifth method embodying principles of the present invention
  • FIGS. 18-20 are schematic views of a sixth method embodying principles of the present invention.
  • FIGS. 21-23 are schematic views of a seventh method embodying principles of the present invention.
  • FIGS. 24-27 are schematic views of an eighth method embodying principles of the present invention.
  • FIGS. 28-29 are schematic views of a ninth method embodying principles of the present invention.
  • FIGS. 1-4 Representatively illustrated in FIGS. 1-4 is a method 10 which embodies principles of the present invention.
  • directional terms such as “above”, “below”, “upper”, “lower”, etc., are used only for convenience in referring to the accompanying drawings.
  • the various embodiments of the present invention described herein maybe utilized in various orientations, such as inclined, inverted, horizontal, vertical, etc., in conjunction with various types of wells, including open hole, cased, production, injection, etc. wells, and in various configurations, without departing from the principles of the present invention.
  • one or more parameters for multiple zones 12, 14 intersected by a well are initially measured by one or more temporary downhole sensors DSi, DS2, DS3, DS4.
  • temporary as used to describe a sensor means that the sensor is only temporarily present or operable in the well, as opposed to a sensor which is intended for long term or permanent use in a well.
  • the sensors DSi, DS2, DS3, DS4 are relatively inexpensive sensors but have expected short lifespans in the well environment. The expected short lifespans of the sensors DSi, DS2, DS3, DS4 may be due to the effects of downhole temperatures, downhole pressures and/ or corrosive fluids, etc. on the sensors.
  • the sensors DSi, DS2, DS3, DS4 are used to obtain training data for a neural network 26 as described below.
  • Lines 16 (which may be any type of lines, such as electrical, fiber optic, hydraulic, etc.) are connected to each of the sensors DSi, DS2, DS3, DS4 and extend to the earth's surface for communication of the sensors' outputs to a conventional computer system (not shown) for training the neural network using techniques well known to those skilled in the neural network training art.
  • a conventional computer system not shown
  • other techniques such as acoustic or electromagnetic telemetry, etc., may be used to communicate the sensors' outputs, without departing from the principles of the present invention.
  • the sensors DSi, DS2, DS3, DS4 in the method 10 are each pressure and temperature sensors of the type well known to those skilled in the art.
  • Sensors DSi and DS3 sense pressure and temperature external to a production tubing string 18, and sensors DS2 and DS4 sense pressure and temperature internal to the tubing string.
  • Sensors DSi and DS2 sense these parameters proximate the zone 12, and sensors DS3 and DS4 sense these parameters proximate the zone 14.
  • sensors may be temporarily conveyed into the well suspended from a line 20, such as a wireline, electric line, slickline, etc. or coiled tubing, etc. as part of a logging tool 22.
  • the tool 22 depicted in FIGS. 1 & 2 is a conventional production logging tool which typically includes at least pressure, temperature and flow rate sensors. Resistivity, density, viscosity, acceleration, pH, dielectric, or any other type of sensor may be used in the method 10.
  • the tool 22 may be positioned in the tubing string 18 above the zone 14 as shown in FIG.
  • valves Vi, N2 are of the type which may be fully opened, fully closed or positioned therebetween to variably regulate fluid flow therethrough. Since the valves Ni, N2 may be used to variably regulate flow, rather than just permit or prevent flow, they may be considered downhole chokes. However, it is to be clearly understood that any type of valve or choke may be used in the method 10, without departing from the principles of the present invention.
  • valves Vi, N2 are also of the type for which the positions thereof may be known to an operator at the surface.
  • the valves Vi, N2 may include position sensors (not shown) connected to the lines 16, or a particular pressure applied to certain of the lines 16 may cause hydraulic actuators (not shown) of the valves to position the valves in a known manner, or a conventional shifting tool (not shown) may be used to position the valves in known positions, etc.
  • any technique may be used to actuate the valves Vi, N2 and to know the valves' positions.
  • Sensors SSi, SS2 are installed in a production flowline 24 at the surface.
  • the surface sensors SSi, SS2 are preferably permanent sensors, meaning that they are installed at the well for long term use. However, since the surface sensors SSi, SS2 are readily accessible, they may alternatively be temporary sensors, in keeping with the principles of the present invention.
  • the sensors SSi, SS2 maybe any type of sensors.
  • the surface sensor SSi may be a pressure and temperature sensor, and the surface sensor SS2 may be a flow rate sensor.
  • These sensors SSi, SS2 are also connected to the computer system (not shown) described above for training the neural network, and for long term monitoring of production from the zones 12, 14 after the neural network has been trained, as described below.
  • FIG. 3 a neural network training step of the method 10 is representatively illustrated.
  • the neural network 26 is trained using multiple training data sets 28 comprising outputs of the surface sensors SSi, SS2, outputs of the downhole sensors DSi, DS2, DS3, DS4 and positions of the valves Vi, N2.
  • the valves Vi, N2 are placed in various positions (fully open, fully closed, partially open, etc.) and the outputs of the various sensors SSi, SS2, DSi, DS2, DS3, DS4 are recorded.
  • the first training data set includes the first position of valve Ni (depicted as V ⁇ , ⁇ ), the first position of valve N2 (depicted as N2, ⁇ ), the corresponding outputs of surface sensors SSi, SS2 (depicted as SS ⁇ , ⁇ and SS2, ⁇ , respectively) and the corresponding outputs of the downhole sensors DSi, DS2, DS3, DS4 (depicted as DS ⁇ , ⁇ , DS2, ⁇ , DS3, ⁇ and DS4, ⁇ , respectively).
  • the first training data set includes the first positions of the valves Vi, V2 (positions V ⁇ , ⁇ and V2, ⁇ ) and the outputs of the sensors SSi, SS2, DSi, DS2, DS3, DS4 while the valves are in those positions (sensor outputs SS ⁇ , ⁇ , SS2, ⁇ , DSi.i, DS2, ⁇ , DS3, ⁇ and DS4, ⁇ ).
  • the surface sensor outputs SS ⁇ , ⁇ ...n, SS2, ⁇ ...n and the valve positions N ⁇ , ⁇ ...n, N2, ⁇ ...n are input to the neural network, and the neural network is trained to output the respective downhole sensor outputs DS ⁇ , ⁇ ...n, DS2, ⁇ ...n, DS3, ⁇ ...n, DS4, ⁇ ...n. That is, the neural network 26 when successfully trained outputs the downhole sensor outputs of a particular training data set (within an acceptable margin of error) when the surface sensor outputs and valve positions of that training data set are input to the neural network.
  • the neural network 26 may be any of the wide variety of neural networks known to those skilled in the art. Furthermore, any technique known to those skilled in the art for training the neural network 26 may be used.
  • the neural network 26 may be a perceptron network, Hopfield network, Kohonen network, etc., and the training technique may utilize a back propagation algorithm, or one of the special algorithms used to train Hopfield and Kohonen networks, etc.
  • the neural network 26 may take any form, for example, it may be "virtual" in that it exists in a computer memory or in computer readable form and may be manipulated using computer software, or the neural network may be a physical network of electronic components, etc.
  • any techniques may be used to refine or optimize the neural network 26 training, such as by using tapped delay lines (not shown), etc.
  • the trained neural network 26 is of significant value in monitoring production from the zones
  • the trained neural network 26 is capable of generating the downhole sensors' outputs given only the surface sensors' outputs and the valves' positions.
  • the neural network 26 is shown in operation in the method 10, after the neural network has been trained.
  • the surface sensor outputs (depicted in FIG. 4 as SSi and SS2) and the valve positions (depicted in FIG. 4 as Ni and N2) are input to the neural network 26.
  • the neural network 26 outputs the downhole sensor outputs (depicted in FIG. 4 as DSi, DS2, DS3 and DS4), which the neural network is able to determine based on its training.
  • the neural network 26 is still able to determine the output(s) of the inoperative sensor(s).
  • this permits the installation of inexpensive or less desirable short lived sensors as temporary sensors in a well for obtaining neural network training data, while more expensive permanent sensors are used at the surface for long term monitoring of the well, even after the downhole sensors have become inoperative or are no longer present in the well (such as after a wireline conveyed production logging tool has been removed from the well).
  • Another benefit of the method 10 is that it permits long term monitoring of the well using sensors installed at the surface, where they are readily accessible for maintenance, replacement, calibration, etc., after relatively inaccessible downhole sensors have become inoperative, or after the downhole sensors are no longer present in the well.
  • Yet another benefit of the method 10 is that it permits analysis of factors affecting production of the well. For example, after the neural network 26 is trained, prospective values for certain variables may be input to the neural network to determine their effect on the neural network outputs. The position of the valve Ni input to the neural network 26 may be changed, for example, to see how the change will affect the outputs of the downhole sensors DSi, DS2, DS3, DS4. The method 10, therefore, enables flow control in the well to be performed based on a predetermination of its effect on downhole parameters.
  • FIGS. 5-7 another method 30 embodying principles of the present invention is representatively illustrated.
  • the method 30 is similar to the method 10 described above in many respects, specifically, in that the output of a sensor is determined by a neural network after that sensor becomes inoperative, or is no longer present in a well.
  • the method 30 does not utilize temporary sensors as such. Instead, in the method 30, multiple sensors Si, S2, S3, S4, S5 are installed in the well, and all of the sensors may initially be intended to be installed permanently in the well. As depicted in FIG.
  • sensors Si and S4 are, for example, pressure and temperature sensors in communication with the interior of a tubing string 32
  • sensors S2 and S5 are, for example, pressure and temperature sensors in communication with the exterior of the tubing string
  • the sensor S3 is a position sensor for indicating a position of a valve N in the tubing string.
  • any types of sensors, any combination of sensor types, any number of sensors, etc. may be used in a method embodying principles of the present invention.
  • Outputs of the sensors Si, S2, S3, S4, S5 are transmitted to a computer system (not shown) via lines 34. Any type of lines may be used for the lines 34, and other communication means, such as acoustic telemetry, etc., may be used in place of the lines.
  • the method 30 permits the output of one or more of the sensors Si, S2, S3, S4, S5 to be determined, even after that sensor becomes inoperative or is no longer present in the well. For example, if the sensor S5 becomes inoperative, data obtained from when the sensor was operative may be used to train a neural network 36 to determine the sensor's output after it becomes inoperative.
  • training data sets 38 are obtained from a period of time in which the sensor was operative (see FIG. 6).
  • the training data sets 38 each include corresponding outputs of all of the sensors Si, S2, S3, S4, S5.
  • a first training data set includes corresponding outputs of the sensors Si, S2, S3, S4, S5 (depicted in FIG. 6 as S ⁇ , ⁇ , S2, ⁇ , S3, ⁇ , S4, ⁇ , S5, ⁇ )
  • a second training data set includes corresponding outputs of the sensors (depicted in FIG. 6 as S ⁇ ,2, S2,2, S3,2, S4,2, S5,2), etc., up to a total of n training data sets.
  • the neural network 36 is trained to output the sensor S5 outputs corresponding to outputs of the sensors Si, S2, S3, S4 input to the neural network. That is, the neural network 36 will, after training, produce the sensor S5 output of a particular training data set when the corresponding outputs of the other sensors Si, S2, S3, S4 in the training data set are input to the neural network (with an acceptable margin of error). Any type of neural network may be used for the neural network 36, and the neural network may be trained and optimized using any known methods.
  • the neural network 36 may be used to determine the sensor's output based on the outputs of the remaining sensors Si, S2, S3, S4. This result is accomplished by inputting the remaining sensor outputs (depicted in FIG. 7 as Si, S2, S3, S4) to the neural network 36, and the neural network in response determining the inoperative sensor's output (depicted in FIG. 7 as S5).
  • the method 30 permits the loss of a sensor to be compensated for in the situation where a history of the sensor's outputs, and outputs of other sensors, are available from a time prior to the sensor's loss. Use of the method 30 will typically be far more cost effective than retrieving and replacing the lost sensor. Note that the exclusive use of sensor outputs other than those of the sensor S5 to train the neural network 36 is not necessary, since other parameters such as valve positions known other than via a sensor (as in the method 10 described above), etc., may be used instead of, or in addition to, the other sensor outputs to train the neural network.
  • FIGS. 8-11 another method 40 embodying principles of the present invention is representatively illustrated.
  • the method 40 is similar in many respects to the methods 10, 30 described above, in that a neural network 42 is trained to determine the output of a sensor after that sensor is no longer present in a well.
  • the output of a flow rate sensor is determined after the sensor is retrieved from the well, but it is to be clearly understood that the method 40 maybe utilized for other types of sensors, other numbers of sensors, combinations of sensors, etc., without departing from the principles of the present invention.
  • Elements shown in FIGS. 8 & 9 which are similar to those shown in FIGS. 1 & 2 are indicated using the same reference numbers.
  • sensors DSi, DS2, DS3, DS4 are installed in the well as part of the tubing string 18.
  • Valves Ni, N2 permit fluid production from zones 12, 14 intersected by the well.
  • the positions of the valves Ni, V2 are known, either by use of a sensor, such as a position sensor (not shown), or by another method.
  • the production logging tool 22 is used as a temporary sensor to obtain multiple training data sets for training the neural network 42.
  • training data sets 44 are obtained with the valves Ni, V2 in various positions.
  • the training data sets 44 include corresponding outputs of the sensors DSi, DS2, DS3, DS4, positions of the valves Vi, N2, and outputs of the logging tool flow rate sensor (depicted in FIG. 10 as TS) for a total of n data sets.
  • the neural network 42 is trained to output corresponding outputs of the temporary flow rate sensor TS in response to inputting to the neural network the outputs of the sensors DSi, DS2, DS3, DS4 and positions of the valves Ni, V2. That is, the neural network 42 will, after training, produce the flow rate sensor TS output of a particular training data set when the corresponding outputs of the other sensors DSi, DS2, DS3, DS4 and positions of the valves Vi, V2 in the training data set are input to the neural network (with an acceptable margin of error). Any type of neural network may be used for the neural network 42, and the neural network maybe trained and optimized using any known methods.
  • the flow rate through the tubing string 18 above the valve Ni may be determined by inputting to the neural network the outputs of the sensors DSi, DS2, DS3, DS4 and positions of the valves Ni, N2. This step is representatively illustrated in FIG. 11.
  • the neural network 42 in response will determine what the output of the flow rate sensor TS would be if it were present in the tubing string 18 above the valve Vi as depicted in FIG. 8. It will be readily appreciated that the method 40 in a sense creates a
  • the neural network 42 determines the "virtual" flow rate sensor output based on the outputs of the other downhole sensors DSi, DS2, DS3, DS4 and the corresponding positions of the valves Ni, V2.
  • a similar neural network may be used for determining the output of the flow rate sensor TS positioned above the valve N2 as depicted in FIG. 9.
  • the neural network would be trained as described above for the neural network 42, using the flow rate sensor TS outputs at the position above the valve N2 in place of the flow rate sensor TS outputs at the position above the valve Vi.
  • the rate of fluid flow through the tubing string 18 above the valve N2 will include contributions from both of the zones 12, 14 if both of the valves Vi, N2 are open, however, conventional techniques may be used to calculate individual flow rates from the individual zones using the outputs of the neural networks.
  • the method 40 permits multiple "virtual" sensors to be created at various positions in the well.
  • FIGS. 12-14 another method 50 embodying principles of the present invention is representatively illustrated.
  • the method 50 is similar in many respects to the methods 10, 40 described above, in that a neural network 52 is used in conjunction with a temporary sensor and a permanent sensor.
  • the temporary sensor is used for calibration or enhancement of the output of the permanent sensor.
  • permanent sensors PSi, PS2, PS3, PS4 are installed in a well.
  • the permanent sensors PSi, PS2, PS3, PS4 may, for example, be pressure and temperature sensors.
  • any other type of sensors, any combination of sensors, etc. may be used a method incorporating principles of the present invention.
  • the permanent sensors PSi, PS2, PS3, PS4 may, when used alone, have less accuracy and/ or resolution than is desired. However, more desirable sensors may not be able to withstand the downhole environment for an extended period of time.
  • the method 50 resolves this problem by using more accurate and/ or higher resolution calibration sensors CSi, CS2, CS3, CS4 to calibrate the permanent sensors PSi, PS2, PS3, PS4 downhole while the calibration sensors remain operative in the well.
  • the outputs of the calibration and permanent sensors are used to train the neural network 52. After the calibration sensors CSi, CS2, CS3, CS4 become inoperative, the trained neural network 52 determines what the outputs of the higher accuracy and/or resolution calibration sensors would be, based on the outputs of the lower accuracy and/or resolution permanent sensors.
  • the neural network 52 is trained using multiple training data sets 54 obtained while the calibration sensors CSi, CS2, CS3, CS4 remain operative in the well.
  • the training data sets 54 each include corresponding outputs of the calibration sensors CSi, CS2, CS3, CS4 and outputs of the permanent sensors PSi, PS2, PS3, PS4.
  • the neural network 52 is trained so that it outputs the calibration sensor outputs of a particular training data set when corresponding permanent sensor outputs of the training data set are input to the neural network. Any type of neural network may be used for the neural network 52, and the neural network may be trained and optimized using any known methods.
  • the neural network 52 determines the corresponding outputs of the calibration sensors CSi, CS2, CS3, CS4.
  • the higher accuracy and/ or resolution calibration sensor outputs may be determined from the lower accuracy and/or resolution permanent sensor outputs, even after the calibration sensors CSi, CS2, CS3, CS4 are no longer operative in the well.
  • the method 60 differs from the above methods 10, 30, 40, 50 in at least one substantial aspect in that a temporary downhole sensor is not used in training a neural network. Instead, a reference sensor is used at the surface, in conjunction with outputs from sensors to be used downhole, to train the neural network.
  • the method 60 is especially useful in those situations where a downhole sensor for sensing a particular downhole parameter either does not exist, is not suitable for a particular application, is prohibitively expensive, etc.
  • a reference sensor RS exists for sensing the parameter at the surface
  • this reference sensor may be used in the method 60 to train a neural network 62.
  • the training data sets 64 include outputs of the reference sensor RS and corresponding outputs of the other sensors Si, S2, S3, S4.
  • the sensors Si, S2, S3, S4 sense parameters related to the downhole parameter which is sensed by the reference sensor RS.
  • the reference sensor RS is a flow rate sensor
  • the other sensors Si, S2, S3, S4 may be pressure and temperature sensors, viscosity sensors, etc.
  • any type of sensor may be used for the reference sensor RS
  • the reference sensor could be multiple sensors, and any type of sensors and combination of sensors may be used for the downhole sensors.
  • the neural network 62 is trained to output the corresponding output of the reference sensor RS (with an acceptable margin of error) when the outputs of the downhole sensors Si, S2, S3, S4 are input to the neural network. That is, the neural network 62 when trained outputs a reference sensor RS output of a particular training data set when the corresponding downhole sensor Si, S2, S3, S4 outputs of the training data set are input to the neural network.
  • Any type of neural network may be used for the neural network 62, and the neural network may be trained and optimized using any known methods.
  • the neural network 62 After the neural network 62 is trained, outputs of the downhole sensors Si, S2, S3, S4 are then input to the neural network 62.
  • the neural network 62 in response determines an output of the reference sensor RS as depicted in FIG. 17.
  • the method 60 permits the output of a reference sensor to be determined by a neural network, given the outputs of downhole sensors, even though the reference sensor has not been downhole to obtain training data sets for training the neural network.
  • the method 60 in a sense creates a "virtual" sensor for the particular downhole parameter which it is desired to sense.
  • FIGS. 18-20 another method 70 embodying principles of the present invention is representatively illustrated.
  • the method 70 is similar in many respects to the method 60 described above, but differs significantly in at least one respect in that a reference sensor at the surface is not used to obtain training data sets. Instead, the method 70 utilizes a temporary sensor TS which is only temporarily present in the well.
  • the temporary sensor TS may be conveyed into the well by wireline, electric line, slickline, coiled tubing, or any other conveyance. While the temporary sensor TS is present in the well, a particular downhole parameter is sensed by the temporary sensor. Other downhole sensors Si, S3, S4 are installed in the well and preferably sense parameters which are related to the parameter sensed by the temporary sensor TS. Multiple training data sets 74 are obtained by recording outputs of the temporary sensor TS and corresponding outputs of the downhole sensors Si, S3, S4. The training data sets 74 are obtained with the sensors TS, Si, S3, S4 downhole.
  • the neural network 72 is then trained to output the temporary sensor TS output when outputs of the downhole sensors Si, S3, S4 are input to the neural network, as depicted in FIG. 19. That is, the trained neural network 72 will output an output of the temporary sensor TS of a particular training data set when the corresponding outputs of the downhole sensors Si, S3, S4 are input to the neural network.
  • Any type of neural network may be used for the neural network 62, and the neural network may be trained and optimized using any known methods.
  • the neural network 72 determines the output of the temporary sensor TS, even though the temporary sensor may no longer be present in the well.
  • the method 70 in a sense creates a "virtual" sensor downhole to take the place of the temporary sensor TS.
  • the method 80 provides another means by which a "virtual" sensor may be created.
  • sensors which sense the desired or related parameters of interest cannot withstand the downhole environment at the location where sensing is desired for a long period of time.
  • the pressure and temperature at a producing zone may be desired, but sensors which can withstand the pressure and temperature at the producing zone may not be available for long term use in the well, such sensors may be prohibitively expensive, etc.
  • the well intersects a zone 82 and a valve N is used to control flow between the zone and the interior of a production tubing string 84.
  • the valve N may have a position sensor, or its position may be otherwise known.
  • Sensors Pi, Ti are temporarily conveyed into the well, for example, as part of a wireline, slickline or coiled tubing conveyed tool.
  • the sensors Pi, Ti may be positioned proximate the zone 82 for only so long as it takes to record a sufficient number of training data sets, as described below.
  • the sensors Pi, Ti may be permanently installed in the tubing string 84 proximate the zone 82, but may only be able to withstand the well environment at that position for a limited period of time.
  • sensors P2, T2 are installed in the well, but they are not proximate the zone 82. Instead, the sensors P2, T2 are positioned sufficiently far uphole that they are in a less severe environment, for example, at a lower temperature and pressure. In this manner, the sensors P2,
  • T2 are able to remain functioning downhole for a long period of time.
  • the sensors P2, T2 are, however, positioned sufficiently far downhole that their outputs are not affected by the surface temperature.
  • a surface temperature affected zone Z exists near the surface of each well, in which the temperature in the well is reduced due to the close proximity of the much lower temperature surface.
  • Other sensors may be installed at the surface.
  • another set of pressure and temperature sensors P3, T3 may be installed upstream of a surface choke C, whose size is known.
  • Another pressure sensor P4 may be installed downstream of the choke C, so that the pressure differential across the choke may be known.
  • the training data sets 86 include outputs of the pressure and temperature sensors Pi, Ti, P2, T2, P3, T3, P4, the size of the surface choke C and the corresponding position of the valve V.
  • the valve N position and/ or the choke C size may be varied to produce the training data sets 86.
  • the temporary sensors Pi, Ti may be retrieved from the well.
  • a neural network 88 is trained to output the temporary sensors' Pi, Ti outputs (with an acceptable margin of error) when the outputs of the other sensors P2, T2, P3, T3, P4, position of the valve V and size of the surface choke C are input to the neural network.
  • the trained neural network 88 will output the outputs of the pressure and temperature sensors Pi, Ti of a particular training data set in response to the corresponding sensors' P2, T2, P3, T3, P4 outputs, valve N position and choke C size of that training data set being input to the neural network.
  • the neural network 88 determines the outputs of the temporary sensors Pi, Ti when outputs of the other sensors P2, T2, P3, T3, P4, a position of the valve N and a size of the choke C are input to the neural network, as illustrated in FIG. 23. In this manner, the temperature and pressure proximate the zone 82 may be determined, even though sensors for these parameters are not present proximate the zone 82.
  • the method 90 provides another means whereby a "virtual" sensor may be created downhole.
  • the method 90 is similar in many respects to the method 60 described above.
  • a reference sensor RS capable of sensing a particular parameter, but unsuitable for extended downhole operation, is used in conjunction with downhole sensors Si, S2, S3, S4, which sense related parameters, in obtaining training data sets 92 for training a neural network 94.
  • the method 90 differs in at least one substantial respect in that the downhole sensors Si, S2, S3, S4 are located at the surface when the training data sets 92 are obtained.
  • the reference sensor RS is a fluid composition sensor
  • the downhole sensors Si, S2, S3, S4 could, for example, sense related parameters such as resistivity, temperature, pressure and pH.
  • the sensors RS, Si, S2, S3, S4 may sense any parameters, and any combination of parameters, without departing from the principles of the present invention.
  • the reference sensor RS and the other downhole sensors Si, S2, S3, S4 are all exposed to various fluid compositions F at the surface, and the corresponding outputs of all of the sensors are recorded.
  • the neural network 94 is then trained, as depicted in FIG. 26, to output the reference sensor RS outputs when the corresponding other sensors' outputs Si, S2, S3, S4 are input to the neural network. That is, the trained neural network 94 will output the output of the reference sensor RS of a particular training data set in response to the other sensors' Si, S2, S3, S4 outputs of the training data set being input to the neural network.
  • Any type of neural network may be used for the neural network 94, and the neural network may be trained and optimized using any known methods.
  • the downhole sensors Si, S2, S3, S4 are installed in the well as depicted in
  • FIG. 25 Thereafter, outputs of the downhole sensors Si, S2, S3, S4 are input to the neural network 94 as depicted in FIG. 27.
  • the neural network 94 determines the output of the reference sensor RS, even though the reference sensor is not downhole and has not been downhole.
  • the method 90 permits fluid composition downhole to be determined, without the need of actually positioning a fluid composition sensor downhole.
  • the method 90 may be used to sense any parameter downhole, even though a sensor capable of sensing that parameter directly downhole is not available, is incapable of withstanding the well environment, is prohibitively expensive, etc.
  • FIGS. 28 & 29 another method 100 embodying principles of the present invention is representatively illustrated.
  • the method 100 is similar in many respects to the method 90 described above.
  • actual known values for the desired parameter are used.
  • the desired parameter is fluid composition
  • known fluid compositions F are used when outputs of the downhole sensors Si, S2, S3, S4 are obtained for training data sets 102.
  • desired parameters other than fluid composition may be used, without departing from the principles of the present invention.
  • the sensors Si, S2, S3, S4 are all exposed to various fluid compositions F as depicted in FIG. 24, except that no reference sensor RS is used in the method 100.
  • the outputs of the sensors Si, S2, S3, S4 are recorded along with the corresponding known fluid compositions. These sensor outputs and known compositions make up the training data sets 102.
  • a neural network 104 is trained using the training data sets 102.
  • the neural network 104 is trained to output the known fluid compositions F when the sensors' Si, S2, S3, S4 outputs are input to the neural network. That is, the trained neural network 104 will output a known fluid composition F of a particular training data set when the sensors' Si, S2, S3, S4 outputs for that particular training data set are input to the neural network.
  • the downhole sensors Si, S2, S3, S4 are then installed in the well as depicted in FIG. 25. Thereafter, the sensors' Si, S2, S3, S4 outputs are input to the neural network 104, as depicted in FIG. 29, and in response the neural network determines the downhole fluid composition F.

Landscapes

  • Geology (AREA)
  • Mining & Mineral Resources (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Fluid Mechanics (AREA)
  • Environmental & Geological Engineering (AREA)
  • General Life Sciences & Earth Sciences (AREA)
  • Geochemistry & Mineralogy (AREA)
  • Geophysics (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Feedback Control In General (AREA)
  • Control Of Non-Electrical Variables (AREA)
  • Testing Or Calibration Of Command Recording Devices (AREA)

Abstract

L'invention concerne des procédés de détection et de régulation du débit fond de trou utilisant des réseaux neuronaux. Dans un mode de réalisation de la présente invention, un capteur provisoire (DSn) est placé au fond du trou et un capteur permanent (SSn) est placé à la surface. Les données générées par les deux capteurs sont enregistrées comme ensembles de données d'entraînement. Ces ensembles de données permettent d'entraîner un réseau neuronal. Lorsque le capteur provisoire n'est plus présent ou n'est plus opérationnel dans le puits, le réseau neuronal est capable de déterminer les données générées par le capteur provisoire en réponse aux données transmises au réseau neuronal par le capteur permanent.
PCT/US2001/005123 2001-02-16 2001-02-16 Detection et reglage du debit fond de trou par reseaux neuronaux WO2002066791A1 (fr)

Priority Applications (3)

Application Number Priority Date Filing Date Title
PCT/US2001/005123 WO2002066791A1 (fr) 2001-02-16 2001-02-16 Detection et reglage du debit fond de trou par reseaux neuronaux
GB0230175A GB2379513B (en) 2001-02-16 2001-02-16 Downhole sensing and flow control utilizing neural networks
US10/076,960 US6789620B2 (en) 2001-02-16 2002-02-15 Downhole sensing and flow control utilizing neural networks

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/US2001/005123 WO2002066791A1 (fr) 2001-02-16 2001-02-16 Detection et reglage du debit fond de trou par reseaux neuronaux

Publications (2)

Publication Number Publication Date
WO2002066791A1 true WO2002066791A1 (fr) 2002-08-29
WO2002066791A8 WO2002066791A8 (fr) 2003-11-13

Family

ID=21742345

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2001/005123 WO2002066791A1 (fr) 2001-02-16 2001-02-16 Detection et reglage du debit fond de trou par reseaux neuronaux

Country Status (2)

Country Link
GB (1) GB2379513B (fr)
WO (1) WO2002066791A1 (fr)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10495778B2 (en) 2015-11-19 2019-12-03 Halliburton Energy Services, Inc. System and methods for cross-tool optical fluid model validation and real-time application

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO1998024010A1 (fr) * 1996-11-27 1998-06-04 Siemens Aktiengesellschaft Procede de modelisation neuronique des formes d'asservissement d'un systeme technique a des parametres
US5828981A (en) * 1995-05-11 1998-10-27 Texaco Inc. Generating pore types and synthetic capillary pressure curves from wireline logs using neural networks
EP0877309A1 (fr) * 1997-05-07 1998-11-11 Ford Global Technologies, Inc. Capteurs virtuels pour vehicules basés sur des réseaux neuronaux entrainés à partir de données générées par des modèles de simulation
EP0881357A2 (fr) * 1997-05-06 1998-12-02 Halliburton Energy Services, Inc. Procédé pour contrÔler le développement d'un gisement de gaz ou d'huile
US6041856A (en) * 1998-01-29 2000-03-28 Patton Enterprises, Inc. Real-time pump optimization system
US6044325A (en) * 1998-03-17 2000-03-28 Western Atlas International, Inc. Conductivity anisotropy estimation method for inversion processing of measurements made by a transverse electromagnetic induction logging instrument
WO2000031654A2 (fr) * 1998-11-19 2000-06-02 Intelligent Inspection Corporation Dispositif et procede de gestion d'un puits

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5828981A (en) * 1995-05-11 1998-10-27 Texaco Inc. Generating pore types and synthetic capillary pressure curves from wireline logs using neural networks
WO1998024010A1 (fr) * 1996-11-27 1998-06-04 Siemens Aktiengesellschaft Procede de modelisation neuronique des formes d'asservissement d'un systeme technique a des parametres
EP0881357A2 (fr) * 1997-05-06 1998-12-02 Halliburton Energy Services, Inc. Procédé pour contrÔler le développement d'un gisement de gaz ou d'huile
EP0877309A1 (fr) * 1997-05-07 1998-11-11 Ford Global Technologies, Inc. Capteurs virtuels pour vehicules basés sur des réseaux neuronaux entrainés à partir de données générées par des modèles de simulation
US6041856A (en) * 1998-01-29 2000-03-28 Patton Enterprises, Inc. Real-time pump optimization system
US6044325A (en) * 1998-03-17 2000-03-28 Western Atlas International, Inc. Conductivity anisotropy estimation method for inversion processing of measurements made by a transverse electromagnetic induction logging instrument
WO2000031654A2 (fr) * 1998-11-19 2000-06-02 Intelligent Inspection Corporation Dispositif et procede de gestion d'un puits

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
A. MURRAY, M. NEUROTH, A. STRONACH, P. MACCONNELL: "The Applications of Advanced Computing Techniques to Oil and Gas Facility Optimisation", SOCIETY OF PETROLEUM ENGINEERS, no. 56904, 7 September 1999 (1999-09-07) - 9 September 1999 (1999-09-09), Aberdeen, pages 1 - 8, XP002177254 *
D. DASHEVSKIY, D. DUBINSKY, J. MACPHERSON: "Application of Neural Networks for Predictive Control in Drilling Dynamics", SOCIETY OF PETROLEUM ENGINEERS, no. 56442, 3 October 1999 (1999-10-03) - 6 October 1999 (1999-10-06), Houston, pages 1 - 8, XP002177253 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10495778B2 (en) 2015-11-19 2019-12-03 Halliburton Energy Services, Inc. System and methods for cross-tool optical fluid model validation and real-time application

Also Published As

Publication number Publication date
WO2002066791A8 (fr) 2003-11-13
GB0230175D0 (en) 2003-02-05
GB2379513B (en) 2005-06-29
GB2379513A (en) 2003-03-12

Similar Documents

Publication Publication Date Title
US6789620B2 (en) Downhole sensing and flow control utilizing neural networks
US6801135B2 (en) Webserver-based well instrumentation, logging, monitoring and control
CA2405775C (fr) Debitmetre de fond de trou
US8412458B2 (en) Determining fluid rate and phase information for a hydrocarbon well using predictive models
GB2417560A (en) Downhole measurement system for verifying a function of a downhole tool
EP1046885B1 (fr) Méthode de calibration de capteurs de pression et temperature
NO20180899A1 (en) Downhole cement strain gauge
US11293278B2 (en) Valve position sensing using electric and magnetic coupling
US20220307349A1 (en) System and method for restoration of safety integrity level (sil) capability in a subsea installation
Asthana et al. Downhole Flow Control Technologies: From Simple Starts to Wireless Smarts
Cayeux et al. Real-Time Optimization of the Drilling Process—Challenges in Industrialization
WO2002066791A1 (fr) Detection et reglage du debit fond de trou par reseaux neuronaux
Konopczynski et al. Intelligent well completion: status and opportunities for developing marginal reserves
Shaker et al. Unique Real-Time Production Optimization Methodology for a Multilateral Well with Hydraulic and Electrical Inflow Control Valves
Hasanov et al. Modelling and Verification of Gravel Pack Treatments in ACG Field to Improve Sand Control Reliability
Rostami et al. Integrated hydraulics modeling for managed pressure drilling
Zhu et al. Optimizing oil and gas production by intelligent technology
van der Geest et al. Reliability through data reconciliation
RU2819019C1 (ru) Определение положения клапана с применением электрического и магнитного соединения
Buchanan Completion System Enabling Simultaneous Injection and Production from Alternating Transverse Fractures in a Horizontal Well
Feder Data Exchange and Collaboration Realize Automated Drilling Control Potential
WO2001092686A1 (fr) Commande, surveillance, diagraphie et instruments de puits bases sur un serveur web
Latini et al. Gravel Pack Optimization in Frade Field Through Bottomhole Data Analysis
Ghoniem et al. Dynamic Production Optimization in Khafji Offshore Field
Nielsen et al. Case study: First intelligent completion system installed in the Gulf of Mexico

Legal Events

Date Code Title Description
AK Designated states

Kind code of ref document: A1

Designated state(s): AE AG AL AM AT AU AZ BA BB BG BR BY BZ CA CH CN CR CU CZ DE DK DM DZ EE ES FI GB GD GE GH GM HR HU ID IL IN IS JP KE KG KP KR KZ LC LK LR LS LT LU LV MA MD MG MK MN MW MX MZ NO NZ PL PT RO RU SD SE SG SI SK SL TJ TM TR TT TZ UA UG US UZ VN YU ZA ZW

AL Designated countries for regional patents

Kind code of ref document: A1

Designated state(s): GH GM KE LS MW MZ SD SL SZ TZ UG ZW AM AZ BY KG KZ MD RU TJ TM AT BE CH CY DE DK ES FI FR GB GR IE IT LU MC NL PT SE TR BF BJ CF CG CI CM GA GN GW ML MR NE SN TD TG

DFPE Request for preliminary examination filed prior to expiration of 19th month from priority date (pct application filed before 20040101)
121 Ep: the epo has been informed by wipo that ep was designated in this application
ENP Entry into the national phase

Ref country code: GB

Ref document number: 200230175

Kind code of ref document: A

Format of ref document f/p: F

CFP Corrected version of a pamphlet front page
CR1 Correction of entry in section i

Free format text: IN PCT GAZETTE 35/2002 DUE TO A TECHNICAL PROBLEM AT THE TIME OF INTERNATIONAL PUBLICATION, SOME INFORMATION WAS MISSING (81). THE MISSING INFORMATION NOW APPEARS IN THE CORRECTED VERSION.

Free format text: IN PCT GAZETTE 35/2002 DUE TO A TECHNICAL PROBLEM AT THE TIME OF INTERNATIONAL PUBLICATION, SOME INFORMATION WAS MISSING (81). THE MISSING INFORMATION NOW APPEARS IN THE CORRECTED VERSION.

REG Reference to national code

Ref country code: DE

Ref legal event code: 8642

122 Ep: pct application non-entry in european phase
NENP Non-entry into the national phase

Ref country code: JP