WO2023028207A1 - Système sur puce perfectionné à utiliser dans un environnement de traitement de fond de trou pour la commande et la navigation relatives au forage - Google Patents

Système sur puce perfectionné à utiliser dans un environnement de traitement de fond de trou pour la commande et la navigation relatives au forage Download PDF

Info

Publication number
WO2023028207A1
WO2023028207A1 PCT/US2022/041489 US2022041489W WO2023028207A1 WO 2023028207 A1 WO2023028207 A1 WO 2023028207A1 US 2022041489 W US2022041489 W US 2022041489W WO 2023028207 A1 WO2023028207 A1 WO 2023028207A1
Authority
WO
WIPO (PCT)
Prior art keywords
drilling
chip
well
data
measure
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.)
Ceased
Application number
PCT/US2022/041489
Other languages
English (en)
Inventor
Enrique Zarate LOSOYA
Narendra VISHNUMOLAKALA
Eduardo Gildin
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.)
Texas A&M University System
Original Assignee
Texas A&M University System
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 Texas A&M University System filed Critical Texas A&M University System
Publication of WO2023028207A1 publication Critical patent/WO2023028207A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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
    • 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
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]

Definitions

  • the present disclosure relates generally to well drilling and more particularly, but not by way of limitation, to a System on Chip (SoC) compatible with Measurement While Drilling (MWD) and Rotary Steerable Systems (RSS) to identify, correct, and optimize well drilling operations.
  • SoC System on Chip
  • MWD Measurement While Drilling
  • RSS Rotary Steerable Systems
  • the present disclosure relates to an SoC that is compatible with MWD systems and RSSs that are capable of acquiring, processing, and executing advanced artificial intelligence agents to identify, correct, and optimize well drilling operations directly at or near a bit of a down hole assembly.
  • the bottom hole assembly is operated without human interaction and is instead controlled by the SoC.
  • the SoC system is composed of two main components; the first component is a SoC with processing speeds and capacity that is higher than current MWD and RSS downhole tools (e.g., an order of magnitude greater processing capabilities).
  • the second component is a firmware specifically developed for downhole tools that is compatible with common communication protocols, sensors, and signal conditioning algorithms.
  • the combination of both components allows for implementation of pre-trained advanced artificial intelligence agents or models that expedite, automate, and eliminate several loops of decision-making cycles usually performed by experienced engineers on site.
  • decision making for well drilling can be made by the SoC.
  • data from the system is relayed to the surface so that an operator can review the operation of the system and adjust operating parameters of the system as desired.
  • a system for drilling a well includes one of a measure while drilling system or a rotary steerable system, and a SoC for use with a downhole tool of the one of a measure while drilling system or a rotary steerable system.
  • the SoC includes a processor and memory operable to acquire, process, and execute advanced artificial intelligent agents to identify, correct, and optimize well drilling operations down hole.
  • the SoC is configured with processing speeds an order of magnitude greater than current measure while drilling and rotary steerable systems.
  • the processor of the SoC is operable to execute firmware that is compatible with communication protocols, sensors, and signal conditioning algorithms of the one of a measure while drilling system or rotary steerable system.
  • the SoC collects real-time measurements relating to at least one drilling parameter.
  • the at least one drilling parameter is one or more of directional orientation, azimuth, inclination, RPM, toolface, gamma correction, and survey estimation.
  • the SoC is rated to withstand temperatures of at least 180 C.
  • the SoC is rated to withstand vibrations of at least 20 g RMS and 10 to 1 kHz.
  • the SoC is rated to withstand shock loads of at least 100g for at least 11 ms.
  • the SoC is operable to receive data from one or more of tools, sensors, and surface equipment associated with the system to direct drilling of the well.
  • the received data comprises one or more of gamma, magnetic field, continuous gamma, acceleration, pressure, temperature, well plan, metadata, surface measurements, survey points, electromagnetic telemetry, mud pulse telemetry, qMIX/qBUS, RSS tools, and other interdevice communication protocols.
  • the processor of the SoC is operable to provide advisory messages relating to drilling of the well.
  • the processor of the SoC is operable to log data relating to drilling of the well.
  • the processor of the SoC is operable to communicate with the one of the measure while drilling system or the rotary steerable system and to control one or more of directional orientation, azimuth, inclination, RPM, toolface, gamma correction, and survey estimation.
  • the one of the measure while drilling system or the rotary steerable system comprise a surface system.
  • the downhole tool comprises a power source, a pressure switch, and a controller.
  • the controller comprises the SoC, a sensor package, and an Al-models based switch.
  • the sensor package comprises one or more of an accelerometer, a magnetometer, a gyroscope, a gamma ray counter, a thermometer, a strain-gauge, and a pressure transducer.
  • the surface system comprises a pressure transducer for receiving mudpulse based communications from the controller.
  • a method of drilling a well includes preparing data for use with an algorithm to drill the well, developing, via the algorithm, information regarding a formation being drilled, identifying a drilling state, a formation state, and an efficiency of the system, and generating, using the drilling state, the formation state, and the efficiency of the system, geosteering control instructions, control system response, and advisory system output data.
  • FIG. 1 depicts an illustrative side-view of a generic drilling site according to aspects of the disclosure
  • FIG. 2 is a schematic diagram of a system on a chip according to aspects of the disclosure
  • FIG. 3 is a schematic diagram of a MWD system according to aspects of the disclosure.
  • FIG. 4 is a flow chart illustrating a method of automated lithology identification and mechanical specific energy in real-time while drilling according to aspects of the disclosure.
  • FIG. 5 is a schematic of a system for drilling a well according to aspects of the disclosure.
  • Some systems advise or provide indications to the skilled driller to change a drilling parameter while monitoring large amount of incoming data. These currently used techniques are not able to generate a step change in drilling efficiency because these approaches rely upon direct input from the driller. Therefore, it would be desirable to have a drilling control system that can provide a step change to drilling efficiency, which further allows for an improved completion design of the well.
  • FIG. 1 depicts a land-based drilling site 10, however, FIG. 1 is primarily provided to give some context in which the drilling control systems and methods disclosed herein may be employed. Thus, the drilling control systems and methods are not limited to land-based drilling sites, but may also be employed in offshore or other types of drilling environments.
  • drilling site 10 includes a drilling rig 12 that is disposed above a well 14 in a formation 22.
  • Drilling rig 12 includes a drill string 16 that has a drill bit 18 towards an end thereof.
  • Drilling rig 12 includes a parameter control and uphole sensor unit 20 that is configured to control one or more drilling parameters including, without limitation, weight-on-bit (WOB), rate-of-penetration (ROP), rotation-per-minute (RPM), and drilling fluid pressure.
  • parameter control and uphole sensor unit 20 may include a pressure control system and may contain sensors for detecting certain operating parameters and controlling the actuation of the pressure of the drilling fluid that is impregnated in well 14.
  • parameter control and uphole sensor unit 20 can also be configured to control the WOB, which impacts the ROP of drill bit 18.
  • Parameter control and uphole sensor unit 20 may include sensors to measure uphole drilling rig 12 data, such as uphole WOB, RPM, casing pressure, depth of drill bit 18, and the drill bit type. In some aspects, measurements of the drilling fluid (or mud) are also captured at the rig.
  • Parameter control and uphole sensor unit 20 is described above to perform several different functions; however, in practical implementation, these functions may be performed by a combination of one or more control and sensor units. [0026] It may be desirable while drilling to gather data related to the drilling process and the formations through which drill bit 18 penetrates.
  • Downhole sensors 26 may include different sets of sensors, one set capturing data while the drill bit 18 is drilling, and this type of measurement is sometimes referred to as logging- while-drilling (LWD). LWD measurements may be related to the formation and contents of the rock drill bit 18 is drilling through. The other set of sensors may capture data related to drill string 16 and drill bit 18, and this type of measurement is sometimes referred to as measurement-while-drilling (MWD).
  • LWD logging- while-drilling
  • MWD measurement-while-drilling
  • the sensors that capture LWD data may include a gamma ray sensor, a resistivity sensor, a neutron sensor, an NMR sensor, a formation-sampling sensor, a sonic sensor, and other relevant sensors. These sensors are employed to gather formation data, such as the porosity, density, lithology, dielectric constant, layer interfaces, type, pressure, and fluid permeability.
  • the sensors capturing MWD data may measure the downhole weight and torque on drill bit 18, and may also capture data related to the bending movements of drill bit 18.
  • the uphole and downhole sensors are configured to communicate the collected data to a location at drilling rig 12 using telemetry tools and methods. Telemetry methods, in one example, include mud-pulse telemetry, which is generally not instantaneous, and thus, the data received by the location at the drilling rig 12 will be delayed.
  • FIG. 1 further depicts a drilling rig control center 24, which monitors data received from the uphole and downhole sensors to optimize drilling parameters, which further results in an optimally-drilled well.
  • the monitored data is received by a trained system that is developed off-line (e.g., in a lab or a factory) and is then implemented on-line at the drilling rig for on-site drilling optimization. This trained system may be continually re-trained on-site using the real-time data received from the uphole and downhole sensors.
  • the trained system derives formation state data from the real-time data received from the uphole and downhole sensors.
  • formation state data includes information pertaining to the rock being drilled.
  • the formation state data includes the probability of the formation state data being correct.
  • the formation state data may further be classified by the trained system. For example, the formation state data could be classified into more general rock types such as sandstone, shale, or limestone. The formation state data could also be classified into more refined categories such as sandy-shale or carbonate-rich shale. The formation state data could be classified into specific rock formations for a given area.
  • formation state data includes rock petrophysical and other reservoir-related data such as porosity, pore pressure, and the percentage of rock types in a particular interval.
  • a potential use of such data and properties is geosteering; for example, determining if the drilling bottom hole assembly (BHA) and bit are within the targeted pay zone.
  • the trained system also derives drilling state data from the real-time data received from the uphole and downhole sensors.
  • drilling state data includes information pertaining to the drill string 14 (FIG. 1) and drill bit 18 (FIG. 1). For example, displacement, rate-of-penetration, mechanical specific energy, etc.
  • the drilling state data may further be classified by the trained system.
  • the trained system is trained to indicate whether there is vibrational dysfunction downhole using the instant set of data that is received from the uphole and downhole sensors.
  • the trained system may also provide more information about the formation and its contents. For example, the formation lithology, compressive strength, shear strength, abrasiveness, and conductivity. In some examples, even more measurements may be gathered by the trained system. For example, temperature, density, and gas content may also provide data related to the formation and its contents.
  • FIG. 2 is a schematic diagram of an SoC 50, according to aspects of the disclosure.
  • SoC 50 provides considerably more processing power (in some instances, on the order of an order of magnitude more) than is typically found in downhole systems to enable complex real-time measurements and analysis to be performed by SoC 50 while it is positioned downhole.
  • SoC 50 enables real-time measurements of various drilling parameters such as: directional orientation, azimuth, inclination, RPM, toolface, gamma correction, survey estimation, and the like.
  • SoC 50 is configured to implement the methods disclosed herein and includes a central processing unit (CPU) 51 (e.g., an ARM® Cortex® M7), a graphics processing unit (GPU) 52, a tensor processing unit application specific integrated circuit (TPU-ASIC) 53, cache 54, random access memory (RAM) 55, storage memory 56, and a universal asynchronous receiver transmitter (UART) 57.
  • CPU central processing unit
  • GPU graphics processing unit
  • TPU-ASIC 53 tensor processing unit application specific integrated circuit
  • cache 54 random access memory
  • RAM 55 random access memory
  • storage memory 56 storage memory 56
  • UART universal asynchronous receiver transmitter
  • GPU 52 and TPU-ASIC 53 are used to perform machine learning tasks (e.g., to provide neural network machine learning using data acquired during the drilling process and/or historical data).
  • Cache 54, RAM 55, and storage memory 56 are temporary and long-term memory storage to carry out the instant method.
  • SoC 50 further includes inputs 58 (e.g., general purpose input/output, analog digital converter, pulse width modulation), neural processing unit (NPU) 59, power management 60, embedded multimedia card (eMMC) 61, real time clock (RTC) 62, analog to digital converters (ADCs) 63, digital signal processor (DSP) 64, inertial measurement unit (IMU) 65, machine learning accelerator (ML ACC) 66, temperature sensor 67, and (digital signal processor) DSP 68.
  • inputs 58 e.g., general purpose input/output, analog digital converter, pulse width modulation
  • NPU neural processing unit
  • eMMC embedded multimedia card
  • RTC real time clock
  • ADCs analog to digital converters
  • DSP digital signal processor
  • IMU inertial measurement unit
  • ML ACC machine learning accelerator
  • temperature sensor 67 e.g., temperature sensor 67
  • DSP 68 digital signal processor
  • Inputs 58 connect SoC 50 with, for example, sensors (e.g., downhole sensors 26) and other system modules to collect data about various drilling parameters and to send instructions to system components to control the drilling operation.
  • NPU 59 implements logic and executes machine learning algorithms and works in conjunction with CPU 51, GPU 52, and TPU-ASIC 53.
  • Power management 60 monitors and control power usage of SoC 50 to optimize power usage of SoC 50.
  • eMMC 61 provides memory storage for SoC 50.
  • eMMC 61 may store routines, programs, settings, operating system, and the like. Additionally, eMMC 61 may be used to store data collected during drilling. In some aspects, eMMC 61 and storage memory 56 may be combined into one component.
  • RTC 62 provides date/time information to SoC 50. This information may be used by SoC 50 for logging and other computing purposes.
  • ADCs 63 and DSP 64/68 work with and convert analog signals (e.g., signals from one or more sensors that monitor the drilling operation) to digital signals that can be stored and/or used by CPU 51, GPU 52, TPU-ASIC 53, NPU 59 etc.
  • ML ACC 66 performs inference computations for machine learning models.
  • IMU 65 provides orientation information of the BHA, and typically includes one or more of an accelerometer, magnetometer, and pressure sensor. Temperature sensor 67 measures and provides downhole temperature information to SoC 50.
  • Table 1 below sets forth exemplary specifications of SoC 50 for use in the system of the present disclosure.
  • SoC 50 is capable of withstanding the impacts, vibrations, and elevated temperatures experienced down hole.
  • the SoC of the present disclosure is rated for temperatures up to 180C, vibrations of 20 g RMS and 10 to 1 kHz, and shock loads of 100g for 11 ms.
  • FIG. 3 is a schematic diagram of a MWD system 100 according to aspects of the disclosure.
  • System 100 includes tools and sensors 102, well information 104, surface equipment 106, integrated measurement while drilling software development environment 108, and MWD SoC 110.
  • SoC 50 is representative of MWD SoC 110.
  • MWD SoC 110 is configured to communicate with software environment 108.
  • MWD SoC 110 receives one or more of data, instructions, and compiled code from software environment 108.
  • Information from tools and sensors 102 can include gamma 112, magnetic field 114, continuous gamma 116, acceleration 118, pressure 120, temperature 122, and the like.
  • Well information 104 can include well plan 124, metadata 126, surface measurements 128, survey points 130, and the like.
  • Additional information from surface equipment 106 can include electromagnetic telemetry 132, mud pulse telemetry 134, qMIX/qBUS 136, RSS Tools 138, other inter-device communication protocols 140, and the like.
  • FIG. 4 is an example of an algorithm for use with the instant disclosure and is discussed in more detail below.
  • the custom designed algorithm controls the MWD assembly and can additionally perform advisory and data logging functions. Control of the MWD assembly includes directional orientation, azimuth, inclination, RPM, toolface, gamma correction, survey estimation, and the like.
  • the advisory function provides information regarding possible dysfunctions (e.g., operating parameters of the MWD assembly are out of a desired range or exceed a desired threshold).
  • MWD SoC 110 sends communications 142 to components of the drilling system to, for example, monitor and/or control directional orientation 144, azimuth 146, inclination 148, RPM 150, toolface 152, 154, gamma correction 156, and survey estimation 158.
  • Communications 142 may be commands and/or data.
  • communications 142 enable dysfunction detection, automated geosteering, data logging, and lithology identification utilizing pre-trained artificial intelligence agents.
  • FIG. 4 is a flow chart illustrating a method 200 of automated lithology identification and mechanical specific energy in real-time while drilling.
  • Method 200 is an exemplary algorithm to be implemented by, for example, MWD SoC 110 and system 100 and system 300 of FIG. 5 (discussed below).
  • Method 200 begins at step 202, where data to be used with the algorithm is prepared (e.g., formatted, defined, etc.).
  • the algorithm uses the data from step 202 in real-time to validate/develop updated formation information and drilling state detection.
  • drilling state, efficiency, and formation state are identified.
  • step 206 relies upon data 207 from training, either or both of existing and historical drilling/formation state detection, and prior real-time training results.
  • Step 206 outputs lithology and petrophysical data to step 208. Data from step 206 and step 208 are merged at 209.
  • Step 210 Data from step 209 is used in step 210 to determine geosteering control and advisory system output (e.g., provide an indication to a user if parameters are outside of a desired range). Data from step 209 is used in step 212 for control system response and advisory system output (e.g., provide an indication to a user if parameters for control are outside of a desired range). Step 210, using the data from step 209, outputs improved geosteering commands 215 to system 100/300 to improve the drilling of the well (e.g., more accurate and more timely commands to optimize drilling). Commands 215 may contain information geosteering commands, lithology information, petrophysical information, and other data are sent to a control system at step 216.
  • the control system communicates with components of system 100/300 to orchestrate the drilling operation.
  • the control system also collects this data in a database for reference in future drilling operations to improve the machine learning process.
  • Step 212 using the data from step 209, outputs improved drilling performance commands 219 to system 100/300 that result in improved drilling performance 213 (e.g., optimization of toque output/power usage to minimize mechanical specific energy).
  • Commands 215 and 219 are used by system 100/300 at step 214 to refine the operation of the system 100/300.
  • Method 200 then returns to step 202 and repeats in an iterative fashion to continue to optimize the operation of system 100/300.
  • U.S. Patent Publication 2019/0277130 the entire disclosure of which is incorporated herein by reference, discloses methods for real-time optimizations of drilling operations.
  • FIG. 5 is a schematic of a MWD system 300 for drilling a well according to aspects of the disclosure.
  • System 300 is an exemplary implementation of system 100.
  • System 300 includes a downhole tool 302 and a surface system 330.
  • Downhole tool 302 includes a power source 304, a pressure-based switch 306, and a controller 308.
  • Controller 308 includes a MWD SoC 310, similar to SoC 50 and MWD SoC 110, a sensor package 312, and an Al models- based switch 314.
  • Sensor package 312 may include one or more of the following sensors for obtaining measurements: accelerometer, magnetometer, gyroscope, gamma ray counters, temperature, strain-gauges, pressure transducer, and the like.
  • Al models-based switch 314 controls drilling states of the MWD system depending on the outputs of a trained Al agent, for instance, the information acquired from directional gamma-ray in conjunction with sensor fusion algorithms, and method 200 can be used to determine the target zone boundaries to maintain, advise or correct the current drilling and trajectory states.
  • MWD SoC 310 receives outputs from sensor package 312 (e.g., measurements that describe the state of the well being drilled) and Al models-based switch 314 (e.g., instructions controlling the MWD system) and performs various functions, such as signal processing, filtering, data analysis, and compression.
  • MWD SoC 310 includes signal processing 311 (e.g., performed by one or more of the CPU, GPU, TPU-ASIC, NPU, etc.
  • Drilling control 316 includes commands controlling drilling parameters such as the drill bit orientation, azimuth, inclination, rotational speed, effective weight on bit, depth of cut, jet velocities, etc.
  • Data 318 may include electromagnetic communications for the surface or mud-pulse based communications that are sent to surface system 330.
  • Surface system 330 includes a pressure transducer 332 that receives mud-pulse based communications from data 318. Communications received by pressure transducer 332 are processed by a signal processor 334. The processed data is digitized by a pulse digitalization and decoder 336 and logged by a drilling electronic data recorder 338. The logged data can be viewed by a user on screen 340. Upon review of the logged data, alterations to the operation parameters of downhole tool 302 may be made if desired.

Landscapes

  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Geology (AREA)
  • Mining & Mineral Resources (AREA)
  • Physics & Mathematics (AREA)
  • Environmental & Geological Engineering (AREA)
  • Fluid Mechanics (AREA)
  • General Life Sciences & Earth Sciences (AREA)
  • Geochemistry & Mineralogy (AREA)
  • Earth Drilling (AREA)

Abstract

L'invention concerne un système de forage de puits qui comprend un système de mesure pendant le forage ou un système orientable rotatif et un système sur puce à utiliser avec un outil de fond de trou du système de mesure pendant le forage ou du système orientable rotatif. Le système sur puce comprend un processeur et une mémoire pouvant fonctionner pour acquérir, traiter et exécuter des agents d'intelligence artificielle perfectionnés pour identifier, corriger et optimiser des opérations de forage de puits en fond de trou.
PCT/US2022/041489 2021-08-27 2022-08-25 Système sur puce perfectionné à utiliser dans un environnement de traitement de fond de trou pour la commande et la navigation relatives au forage Ceased WO2023028207A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US202163237947P 2021-08-27 2021-08-27
US63/237,947 2021-08-27

Publications (1)

Publication Number Publication Date
WO2023028207A1 true WO2023028207A1 (fr) 2023-03-02

Family

ID=85323451

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2022/041489 Ceased WO2023028207A1 (fr) 2021-08-27 2022-08-25 Système sur puce perfectionné à utiliser dans un environnement de traitement de fond de trou pour la commande et la navigation relatives au forage

Country Status (1)

Country Link
WO (1) WO2023028207A1 (fr)

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116398111A (zh) * 2023-06-07 2023-07-07 四川众恒精诚地质勘测有限公司 一种面向地质勘测的岩土层钻进系统及方法
US20230228898A1 (en) * 2022-01-19 2023-07-20 Halliburton Energy Services, Inc. Utilizing resistivity distribution curves for geological or borehole correlations
US12204065B2 (en) 2022-01-19 2025-01-21 Halliburton Energy Services, Inc. Utilizing resistivity data for multiple view perspectives for geo-steering
US12313808B2 (en) 2022-01-19 2025-05-27 Halliburton Energy Services, Inc. Correlating true vertical depths for a measured depth
US12473812B2 (en) 2024-01-12 2025-11-18 Saudi Arabian Oil Company Methods and systems for logging while drilling and optimized telemetry using artifical intelligence

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6026913A (en) * 1997-09-30 2000-02-22 Halliburton Energy Services, Inc. Acoustic method of connecting boreholes for multi-lateral completion
US20080223579A1 (en) * 2007-03-14 2008-09-18 Schlumberger Technology Corporation Cooling Systems for Downhole Tools
WO2020154399A1 (fr) * 2019-01-23 2020-07-30 Schlumberger Technology Corporation Caractérisation d'écho d'impulsion ultrasonore et de formation d'étrier
US20210192712A1 (en) * 2019-12-18 2021-06-24 Schlumberger Technology Corporation Methods for transmitting data acquired downhole by a downhole tool
US20210189868A1 (en) * 2019-12-19 2021-06-24 Saudi Arabian Oil Company Systems and Methods for Actuating Downhole Devices and Enabling Drilling Workflows from the Surface
US20210189872A1 (en) * 2021-02-04 2021-06-24 Erdos Miller, Inc. Optimization of automated telemetry for a downhole device

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6026913A (en) * 1997-09-30 2000-02-22 Halliburton Energy Services, Inc. Acoustic method of connecting boreholes for multi-lateral completion
US20080223579A1 (en) * 2007-03-14 2008-09-18 Schlumberger Technology Corporation Cooling Systems for Downhole Tools
WO2020154399A1 (fr) * 2019-01-23 2020-07-30 Schlumberger Technology Corporation Caractérisation d'écho d'impulsion ultrasonore et de formation d'étrier
US20210192712A1 (en) * 2019-12-18 2021-06-24 Schlumberger Technology Corporation Methods for transmitting data acquired downhole by a downhole tool
US20210189868A1 (en) * 2019-12-19 2021-06-24 Saudi Arabian Oil Company Systems and Methods for Actuating Downhole Devices and Enabling Drilling Workflows from the Surface
US20210189872A1 (en) * 2021-02-04 2021-06-24 Erdos Miller, Inc. Optimization of automated telemetry for a downhole device

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20230228898A1 (en) * 2022-01-19 2023-07-20 Halliburton Energy Services, Inc. Utilizing resistivity distribution curves for geological or borehole correlations
US12204065B2 (en) 2022-01-19 2025-01-21 Halliburton Energy Services, Inc. Utilizing resistivity data for multiple view perspectives for geo-steering
US12313808B2 (en) 2022-01-19 2025-05-27 Halliburton Energy Services, Inc. Correlating true vertical depths for a measured depth
US12320946B2 (en) * 2022-01-19 2025-06-03 Halliburton Energy Services, Inc. Utilizing resistivity distribution curves for geological or borehole correlations
CN116398111A (zh) * 2023-06-07 2023-07-07 四川众恒精诚地质勘测有限公司 一种面向地质勘测的岩土层钻进系统及方法
CN116398111B (zh) * 2023-06-07 2023-09-22 四川众恒精诚地质勘测有限公司 一种面向地质勘测的岩土层钻进系统及方法
US12473812B2 (en) 2024-01-12 2025-11-18 Saudi Arabian Oil Company Methods and systems for logging while drilling and optimized telemetry using artifical intelligence

Similar Documents

Publication Publication Date Title
WO2023028207A1 (fr) Système sur puce perfectionné à utiliser dans un environnement de traitement de fond de trou pour la commande et la navigation relatives au forage
CN111989456B (zh) 用于井筒操作中定向钻井的基于模型的参数估计
EP2929141B1 (fr) Fonction de pondération pour calcul d'inclinaison et d'azimut
US11015424B2 (en) Geosteering based on automated well performance prediction
US6386297B1 (en) Method and apparatus for determining potential abrasivity in a wellbore
US20180334897A1 (en) Drilling control based on brittleness index correlation
US11549354B2 (en) Methods for real-time optimization of drilling operations
US20130341093A1 (en) Drilling risk avoidance
WO2022066742A1 (fr) Procédé et système de traitement de données diagraphiques de puits provenant de multiples puits à l'aide d'un apprentissage automatique
CA2794094A1 (fr) Procedes et systemes pour operations de forage ameliorees utilisant des donnees de forage historiques et en temps reel
NO341156B1 (no) System, fremgangsmåte og datamaskinlesbart medium for å gjennomføre en boreoperasjon for et oljefelt
US11952880B2 (en) Method and system for rate of penetration optimization using artificial intelligence techniques
US20240218791A1 (en) Utilizing dynamics data and transfer function for formation evaluation
GB2458356A (en) Oilfield well planning and operation
EP3545168B1 (fr) Pixélisation de solution d'inversion de limite de distance au lit
CA3165992C (fr) Planification automatisee de capteurs pour forage directionnel
NO20250196A1 (en) Orchestration framework to determine composite well construction recommendations
WO2000050737A1 (fr) Procede et appareil permettant de determiner la severite d'interface potentielle pour une formation
US20240426178A1 (en) Orchestration framework to compute dynamic risk and impact maps
US20250189690A1 (en) Using trust model to compute dynamic risk and impact maps
US20240035367A1 (en) Method and system for increasing effective data rate of telemetry for wellbore construction
Zarate Losoya et al. Methods For Real-time Optimization Of Drilling Operations
WO2025221273A1 (fr) Calcul de volume de foudroyage pour opérations de forage

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 22862066

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 22862066

Country of ref document: EP

Kind code of ref document: A1