WO2023067391A1 - Système et procédé de prédiction et d'optimisation de paramètres de forage - Google Patents

Système et procédé de prédiction et d'optimisation de paramètres de forage Download PDF

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Publication number
WO2023067391A1
WO2023067391A1 PCT/IB2022/000621 IB2022000621W WO2023067391A1 WO 2023067391 A1 WO2023067391 A1 WO 2023067391A1 IB 2022000621 W IB2022000621 W IB 2022000621W WO 2023067391 A1 WO2023067391 A1 WO 2023067391A1
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Prior art keywords
data
time
penetration
rate
predicted
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PCT/IB2022/000621
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English (en)
Inventor
Tim Robinson
Dalila Gomes
Alexander Chekushev
Olav Revheim
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Exebenus AS
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Publication of WO2023067391A1 publication Critical patent/WO2023067391A1/fr

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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
    • E21B44/00—Automatic control systems specially adapted for drilling operations, i.e. self-operating systems which function to carry out or modify a drilling operation without intervention of a human operator, e.g. computer-controlled drilling systems; Systems specially adapted for monitoring a plurality of 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
    • 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 disclosure relates generally to oil and gas well drilling and well operations. More specifically, this disclosure relates to a method and a system using machine learning models to optimize a rate of penetration (ROP) of the drilling operation.
  • ROP rate of penetration
  • Drilling and well operations in oil and gas wells are expensive operations. The cost is typically several tens to several hundred thousand dollars per day. Optimizing the rate of penetration reduces the time spent drilling the well, and consequently reducing the operational cost. Attempts to optimize performance include the following.
  • U.S. Patent No. 10,539,001 uses drilling parameters and lithologies from offset wells to generate ROP as function of depth combined with the lithologies as a function of depth in a planning phase - rather than in real time.
  • U.S. Patent No. 10,275,715 describes a methodology for using historical and real time data to generate a set of machine learning models that compares the actual ROP with predicted and optimum ROP within a ROP swimlane determined by statistical model.
  • the method disclosed in U.S. Patent No. 10,275,715 relies on relevant historical data, presents the output as a swimlane - rather than specific recommended values.
  • U.S. Patent No. 9,995, 129 provides a method for how to control the drilling rig, and not for finding the optimum operational parameters.
  • U.S. Patent No. 7,730,967 provides a method to correct operational anomalies from drillstring sensors.
  • U.S. Patent No. 8,121,971 describes rule-based agents using conventional physical algorithms to optimize drilling operations.
  • U.S. Patent No. 9,424,667 describes using sensor values to calculate entropy and energy consumption and as a result determining whether the operation is RPM, weight on bit (WOB) or flowrate dominant, and using this to optimize the drilling parameters.
  • U.S. Patent No. 6,382,331 describes storing relevant sensor data and ROP, performing a linear regression with ROP as the response variable and sensor data as explanatory variables to create a correlation between the ROP and one of the sensors to define an optimum value for the sensor and for then to try to follow the optimum sensor value.
  • U.S. Patent No. 7,357,196 describes adjusting drilling parameters based on models of lithology, rock strength, shale plasticity, mechanical efficiency.
  • U.S. Patent No. 10,316,653 describes a method and system for predicting drilling performance per depth based on a geology model giving a bit selection and predicted drilling mechanics such as bit wear, mechanical efficiency, and operating parameters.
  • U.S. Patent No. 7,899,658 describes a method for simulating the performance of a drilling BHA in engineering and evaluating the performance post-run.
  • U.S. Patent No. 7,412,331 describes using an equation based on rock properties for the formations to be drilled combined with string friction factor, mud weight and mechanical efficiency factor.
  • U.S. Patent No. 9,970,266 describes using a neural network for real time lithology predictions and base drilling optimization recommendations based on the lithology prediction.
  • U.S. Patent No. 9,022,140 describes methods and systems for prediction a range of drilling parameters based on Neural Network lithology predictions.
  • U.S. Patent No. 9,057,245 describes calculating mechanical specific energy using published methods for a range of WOB and RPMs, optimum drilling operations are reached when standard deviation of MSE is low.
  • U.S. Patent No. 10,591,625 describes automatic comparison and adjustment of drilling parameters towards a pre-defined setpoint.
  • U.S. Patent No. 10,657,441 describes using a neural network to predict ROP by receiving real time drilling data and giving advice on parameters to change. Also requires use of static data indicative on the type of drilling data.
  • U.S. Patent No. 10,577,914 discloses multi-variable modelling of drilling parameters to optimize drilling performance using a physics model.
  • U.S. Patent No. 7,172,037 describes an integrated system of bottomhole assembly (BHA), toolstring sensors and “controller” that predict behavior of the drilling systems to give advice on parameter changes to optimize drilling performance, using neural networks.
  • BHA bottomhole assembly
  • U.S. Patent No. 8,527,249 describes readings to calculate equivalent circulating density (ECD) for different drilling parameters and then propose drilling parameters to be as close to max allowable ECD as possible.
  • ECD equivalent circulating density
  • the disclosure provides a methodology for remote cloud-based optimization of ROP by recommending values in real-time for rotary speeds, weight-on-bit and mud flow rates that optimize ROP, solely using readily available surface measurements.
  • the disclosure provides a methodology for on-premise optimization of ROP by recommending values in real-time for rotary speeds, weight-on-bit and mud flow rates that optimize ROP, solely using readily available surface measurements.
  • the disclosure provides a methodology for near wellbore (e.g., rig server) optimization of ROP by recommending values in real-time for rotary speeds, weighton-bit and mud flow rates that optimize ROP, solely using readily available surface measurements.
  • near wellbore e.g., rig server
  • the disclosure provides a method for predicting drilling parameters for drilling operation.
  • the method comprises: receiving time-based data from a real-time data system including a sensor; filtering the time-based data from the system; and generating, using a machine learning model, predictions based on the filtered time-based data from the sensor.
  • the predictions include a predicted rate of penetration.
  • the method further includes selecting drilling parameters that yield the highest predicted rate of penetration.
  • the predictions include one or more of weight on bit, revolutions per minute and mud flow.
  • the time-based data includes one or more of rate of penetration, weight on bit, revolutions per minute and mud flow.
  • the time-based data is received at a processor remote from the oil well.
  • the processor calculates the average values for one or more sensor values over a time interval or a depth interval.
  • one or more machine learning models predict values of one or more of rate of penetration, weight on bit, revolutions per minute, and mud flow based on the measured time-based data or averaged time-based data.
  • one or more machine learning model predicts values of one or more of rate of penetration, weight on bit, revolutions per minute, and mud flow based the logarithmic values of the time-based data.
  • an algorithm stepwise modifies the measured sensor values and a machine learning model makes a new prediction for each modification.
  • predictions are repeated one or more times during the operational sequence.
  • one or more of the predicted weight on bit, revolutions per minute and mud flow yielding the highest rate of penetration is selected.
  • the predicted weight on bit, revolutions per minute and mud flow yielding the highest rate of penetration is compared with threshold values of said parameters.
  • a different rate of penetration and associated parameters is selected if one or more of the parameters exceeds the threshold values.
  • the predicted data values are converted to time or depth series data and visualized in a computer user interface.
  • the predicted data values are converted to time or depth series data is stored in a database.
  • drilling operations are identified by filtering two or more sensors.
  • two or more machine learning models utilize the same filtered and normalized data sets and a selection algorithm selects a single preferred prediction data series.
  • the disclosure provides a system for predicting rate of penetration in oilfield operations comprising: a real time data system associated with at least one oil well; an electronic processor; and a memory.
  • the memory storing instructions that when executed by the electronic processor configure the electronic processor to: receive data from the real time data system; filtering the time-based data from the system; and generating, using a machine learning model, predictions based on the filtered time-based data from the sensor.
  • the predictions include a predicted rate of penetration.
  • the memory storing instructions configure the electronic processor to selecting drilling parameters that yield the highest predicted rate of penetration.
  • the real time data system comprises one or more sensors associated with an oil well.
  • the processor configured to receive data from the real time data system is remote from the oil well.
  • the time measured, predicted drilling parameters, and rate of penetration are visualized in a user interface.
  • the user interface is located remote from the oil well.
  • the predicted drilling parameters and rate of penetration are converted to time or depth series data and stored in a database.
  • receiving data from the real time data system includes data from two or more sensors.
  • two or more machine learning models are using the same filtered and normalized data sets and a selection algorithm selects a single preferred prediction data series.
  • filtering results from one sensor data series are used as input to the filtering of another sensor data series.
  • the instructions executed by the electronic processor is containerized and deployed to a virtual machine in a data center.
  • the containerized instructions are deployed on a physical server.
  • FIG. l is a schematic diagram of a drilling rig being monitored by sensors.
  • FIG. 2 is schematic diagram showing a drillbit in a well.
  • FIG. 3 is a block diagram of steps of a method of the instant invention.
  • FIG. 4A is an example of user interfaces of the instant invention.
  • FIG. 4B is an example of user interfaces of the instant invention.
  • FIG. 5 is a block diagram demonstrating use of sensor data.
  • FIG. 6 is an example of a display in a data viewer.
  • FIG. 7 is a block diagram depicting preferred systems of the present invention.
  • the electronic-based aspects may be implemented in software (e.g., stored on non-transitory computer-readable medium) executable by one or more processing units, such as a microprocessor and/or application specific integrated circuits (“ASICs”).
  • processing units such as a microprocessor and/or application specific integrated circuits (“ASICs”).
  • ASICs application specific integrated circuits
  • servers and “computing devices” described in the specification can include one or more processing units, one or more computer-readable medium modules, one or more input/output interface, and various connections (e.g., a system bus) connecting the components.
  • the software-based components can be containerized and deployed on Virtual Machines (Windows, Linux or similar) in a Data Center that are either cloud based ( provided by Azure, AWS or similar) or by the user organization itself.
  • the software-based components can be deployed on a physical server.
  • the invention provides a method and system for capturing sensor data in real time from a drilling rig in the oil and gas field.
  • the method and system filters and normalizes the real time data and feed them into to several predictive machine learning models to predict the drilling parameters based on the current drilling performance.
  • the ROP is then calculated for each of the predicted parameters, and an algorithm selects the optimum ROP based on these predictions.
  • the recommended drilling parameters and expected ROP from these parameters are stored in a database and displayed in a computer user interface.
  • a drilling rig 100 or other well operating units includes rig equipment 104 (e.g., a block), a mud pump system 108A, 108B, 108C, a drill string 112 (i.e., downhole equipment), and a drill bit 116 for drilling rock 118.
  • rig equipment 104 e.g., a block
  • mud pump system 108A, 108B, 108C e.g., a drill string 112 (i.e., downhole equipment)
  • drill string 112 i.e., downhole equipment
  • drill bit 116 for drilling rock 118.
  • the operation of the drilling rig 100 is monitored by a sensor 120 coupled to a portion of the rig 100.
  • An output 124 of the sensor 120 is provided to a computer system 150.
  • the output 124 of the sensor 120 includes data that can provide insights in the operations of the rig 100 and the various types of rock 118 being drilled through.
  • the sensor 120 is a hook load sensor, a torque sensor, a mud density input sensor, a mud flow input sensor, a pressure sensor, a rotations per minute (RPM) sensor, an equivalent circulating density (ECD) sensor, a rate of penetration (ROP) sensor, a drill depth sensor, or any other suitable sensor.
  • the output 124 from the sensor 120 (i.e., sensor output data) is captured as part of a real-time data system 128 that then stores the output 124 in a drill site computer 154.
  • the drill site computer 154 is typically located on the premises of the drilling rig 100.
  • the drill site computer 154 includes a memory storage 158 and a display 162.
  • the output 124 from the sensor 120 is shown on the display 162 and can be monitored by qualified personnel Pl to verify the quality of operations and to identify deviations or early warnings for undesired events.
  • the output 124 from the sensor 120 captured and stored in the computer storage 1158 is also distributed via a network 166.
  • the network is internet-based.
  • the sensor data 124 is distributed by the network 166 to one or more remote locations 170 outside from the drilling rig 10 (e.g., a remote office buildings).
  • the sensor data is stored as time or depth series of data at the remote locations 170 on computer storage 174 and shown on displays 178 to other qualified personnel P2.
  • the network 166 is, for example, a wide area network (“WAN”) (e.g., a TCP/IP based network), a local area network (“LAN”), a neighborhood area network (“NAN”), a home area network (“HAN”), or personal area network (“PAN”) employing any of a variety of communications protocols, such as Wi-Fi, Bluetooth, ZigBee, etc.
  • WAN wide area network
  • LAN local area network
  • NAN neighborhood area network
  • HAN home area network
  • PAN personal area network
  • the network 166 is a cellular network, such as, for example, a Global System for Mobile Communications (“GSM”) network, a General Packet Radio Service (“GPRS”) network, an Evolution-Data Optimized (“EV-DO”) network, an Enhanced Data Rates for GSM Evolution (“EDGE”) network, a 3GSM network, a 4GSM network, a 5GNew Radio, a Digital Enhanced Cordless Telecommunications (“DECT”) network, a digital AMPS (“IS-136/TDMA”) network, an Integrated Digital Enhanced Network (“iDEN”) network, a satellite network, or radiolink network etc.
  • GSM Global System for Mobile Communications
  • GPRS General Packet Radio Service
  • EV-DO Evolution-Data Optimized
  • EDGE Enhanced Data Rates for GSM Evolution
  • 3GSM Third Generation
  • 4GSM Third Generation
  • 5GNew Radio a Digital Enhanced Cordless Telecommunications
  • DECT Digital Enhanced Cordless Telecommunications
  • IS-136/TDMA digital AMPS
  • the ROP is a function of the drilling resistance of the various rock formations 203 and the forces applied from the drillbit 201 to the rock formations 203 through weight 204 set down onto the drillbit 201, commonly referred to as weight on bit (WOB); the jetting and cutting removal effect created by the flow of drilling mud 205; and the grinding effect caused by rotation 206.
  • WOB weight on bit
  • the optimal ROP is achieved by selecting an optimum combination of WOB 204, rotation 206 and flow 205. These three parameters: WOB, rotation and flow together are commonly referred to as drilling parameters.
  • the optimum combination of the drilling parameters is dynamic and is a function of rock strength of the different formations 203 as together with the depth, size and path of the well. For the practitioners in the field, that ability to use only rig sensor data in real time has significant advantages.
  • Data 124 from the sensor 120 may be pulled via any conventional data transfer protocol from the real time data acquisition, storage and distribution computer 182 (e.g., the drill site computer 154) to a processing computer 184, where predictive methods are performed. Mapping and configuration of data series mnemonics are performed on a configuration user interface via a web interface 186.
  • the processing computer 184 may be cloud hosted. The actual predictive modelling can be done on a separate, preferably cloud hosted system 188, such as one provided by Microsoft Azure, Amazon Web Services or other commercial cloud provider.
  • the term “computers” includes a plurality of electrical and electronic components that provide power, operational control, and protection to the components and modules within the computers and/or the system.
  • the processing computer 184 includes, among other things, a processing unit (e.g., a microprocessor, a microcontroller, or other suitable programmable device), and is implemented using a known computer architecture (e.g., a modified Harvard architecture, a von Neumann architecture, etc.).
  • a processing unit e.g., a microprocessor, a microcontroller, or other suitable programmable device
  • a known computer architecture e.g., a modified Harvard architecture, a von Neumann architecture, etc.
  • the memory storage of the computers is a non-transitory computer readable medium and includes, for example, a program storage area and the data storage area.
  • the program storage area and the data storage area can include combinations of different types of memory, such as a ROM, a RAM (e.g., DRAM, SDRAM, etc ), EEPROM, flash memory, a hard disk, a SD card, or other suitable magnetic, optical, physical, or electronic memory devices.
  • the processing unit is connected to the memory and executes software instructions that are capable of being stored in a RAM of the memory (e.g., during execution), a ROM of the memory (e.g., on a generally permanent bases), or another non-transitory computer readable medium such as another memory or a disc.
  • Software included in the implementation of the methods disclosed herein can be stored in the memory.
  • the software includes, for example, firmware, one or more applications, program data, filters, rules, one or more program modules, and other executable instructions.
  • the processing computer 184 is configured to retrieve from the memory and execute, among other things, instructions related to the processes and methods described herein.
  • rig equipment and downhole tools are often equipment with sensors 301 (e.g., sensor 120). These sensors describe the operation of the rig, including but not limited to hook load (e.g., the weight of the string 112), block position (the position of 104), torque (e.g., the force used to rotate 112), RPM (e.g., the number of rotations per minute applied to 112), pump pressure and flow rate (e.g., the output values from the pump 108C).
  • hook load e.g., the weight of the string 112
  • block position the position of 104
  • torque e.g., the force used to rotate 112
  • RPM e.g., the number of rotations per minute applied to 112
  • pump pressure and flow rate e.g., the output values from the pump 108C.
  • the sensors may also be sensors connected to the downhole string 112, either providing measurements on the rig operations, such as load, torque, RPM, flowrate, pump pressure or sensors measuring the properties of the downhole formation 118 including Gamma Ray (GR), Neutron Density data, Sonic response data and others.
  • GR Gamma Ray
  • Neutron Density data Neutron Density data
  • Sonic response data Sonic response data and others.
  • the method described herein uses sensor data to optimize the ROP.
  • the method of the invention utilizes sensor data 301 stored in a computer storage facility at STEP 302.
  • the invention involves reading such data series in a data receptor module at STEP 303 that connects to the data source and retrieves the data of the relevant input data series.
  • the invention contains a user interface FIG 4A for setting up and to configure data mnemonics FIG 4B for the relevant sensor data series.
  • Different sources of such data can be WITSML data, WITS0 data, OPC data or other data formats.
  • the data receptor module is an integration service that is tailored to the source, using known system integration methodologies, in the case of WITSML, the receptor module uses XML-queries via a SOAP interface, for OPC-UA a RestAPI is used.
  • the invention allows users to set up the data receptor to retrieve data from a specific server 401, also supporting log-in security requirements 402.
  • the invention further has a user interface that allows the users to configure the wellbore names 403, used by the receptor module to retrieve the correct data, and to select the appropriate data sources 404 for the invention.
  • the sensor data series used by the invention is bit depth, hole depth, mud flow in (MFI), mud density in (MDI), weight-on-bit (WOB), rotary speed (RPM), torque (TRQ) and drillpipe pressure (DPP).
  • the sensor data handled by the data receptor module at STEP 303 is used by the activity recognition module at STEP 304 that uses known methods to recognize the drilling activity. Only when drilling activity is recognized, the data received by the data receptor module are forwarded to the Filtering and Data preparation module at STEP 305.
  • the applicable sensor data 501 are gathered for two configurable times (tl an t2) or depth intervals (dl or d2) by the data aggregation service 502.
  • the aggregated data is then averaged over the tl, t2, dl, and d2 intervals at data averaging service 503.
  • a series of calculations are made for each calculation time (Tn) or depth (Dn) stamp. In one instance of the invention, these calculations involve EQN. 1 and EQN. 2.
  • INPUT Input 1, log(Inputl), In(inputl), input2, log(Input2), ln(input2), MFI, MDI, WOB, RPM. TRQ and DPP, collectively termed INPUT.
  • the machine learning models predict the drilling parameters and ROP a depth increment, D(n+1) or Time, T(n+1) ahead in time. For each time, Tn or depth Dn stamp, the predictions may be repeated a configurable number of times (N) for one or more of the INPUT parameters. For each time, the input values are variable according to EQN. 3.
  • INPUT(1 to N) INPUT +/- (1 to N)* Delta(value) [3] [0082]
  • the Delta(value) as well as the N value may be configured individually for each of the parameters that are part of the INPUT.
  • the output from the machine learning modules is a series of N+l set of predicted drilling parameters (WOBN, RPMN and 121own) with the corresponding predicted ROPN value. Such output is generated for each of the time(Tn) or Depth(Dn) stamp where data is gathered.
  • the selection algorithm For each time (Tn) or Depth (Dn) stamp, in one instance of the invention, the selection algorithm identifies the maximum predicted ROP value at STEP 307 from the set of N+l possible ROP value and select the corresponding drilling parameters from this value as the recommended drilling parameter. In another instance of the invention, the selection algorithm at STEP 307 compares the selected drilling parameters for the maximum ROP to their pre-set or calculated using known methods, maximum allowable threshold values. If one or more of the drilling parameters exceeds their maximum value, a new set of drilling parameter corresponding to a new maximum ROP are selected and checked against the threshold values. This is repeated until the maximum ROP with allowable drilling parameters are identified.
  • the selected maximum drilling parameters and the corresponding predicted ROP value are converted to a time-series data be the real Time data converter at STEP 308.
  • the time series data are stored in a data storage at STEP 309 and displayed in a data viewer at STEP 310.
  • the method of the invention may be used in both real time during a drilling operation, and by replaying historical data.
  • FIG. 6 shows an example of a display in a data viewer (at STEP 310), as an example of the method applied to historical data as a replay.
  • Data sensor values 601 indicating the status of the operation is shown for reference.
  • the drilling parameters (WOB, RPM and Flow) are displayed in separate swimlanes 602. The individual components within each swimlane are a shown with curve 603, which shows the actual drilling parameters as they were during the operation.
  • the recommend drilling parameter is shown as curve 604, based on the prediction method detailed herein.
  • a gap, or delta, between the recommended drilling parameters based on the predictions 604, and the gap between the actual and the recommended values 603 is shown as 605.
  • FIG. 7 provides a depiction of preferred systems of the present invention.
  • Sensor data may be pulled via any conventional data transfer protocol from the Real Time Data Acquisition, storage and distribution computer 182 to the computer of the invention 184, where the different modules of the invention, 303, 304, 305, 306, 307 and 308 are installed.
  • Mapping and configuration of data series mnemonics are performed on a configuration user interface on a user interface with a web interface 186.
  • the output of the invention are stored in a time and depth based storage and distribution computer 188, which may be the same computers from which data are pulled 182.
  • the data are viewed on a web-based user interface 190 as shown in FIGS. 4A, 4B, and 6.
  • the systems and methods described herein can be implemented in hardware, software, firmware, or combinations of hardware, software and/or firmware.
  • the systems and methods described in this specification may be implemented using a non-transitory computer readable medium storing computer executable instructions that when executed by one or more processors of a computer cause the computer to perform operations.

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  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Geology (AREA)
  • Mining & Mineral Resources (AREA)
  • Physics & Mathematics (AREA)
  • Environmental & Geological Engineering (AREA)
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Abstract

L'invention concerne un procédé et un système d'utilisation de technologies d'apprentissage automatique pour prédire la valeur et la synchronisation de paramètres de fonctionnement. Ces prédictions sont ensuite utilisées pour optimiser le taux de pénétration (ROP) d'une opération de forage.
PCT/IB2022/000621 2021-10-22 2022-10-20 Système et procédé de prédiction et d'optimisation de paramètres de forage WO2023067391A1 (fr)

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