WO2021041254A1 - Plateforme informatique répartie de gestion de réservoirs à base d'ai/ml et de chaînes de blocs - Google Patents

Plateforme informatique répartie de gestion de réservoirs à base d'ai/ml et de chaînes de blocs Download PDF

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WO2021041254A1
WO2021041254A1 PCT/US2020/047502 US2020047502W WO2021041254A1 WO 2021041254 A1 WO2021041254 A1 WO 2021041254A1 US 2020047502 W US2020047502 W US 2020047502W WO 2021041254 A1 WO2021041254 A1 WO 2021041254A1
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variables
engine
machine learning
algorithm
earth model
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PCT/US2020/047502
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English (en)
Inventor
Keshava Prasad RANGARAJAN
Raja Vikram R. PANDYA
Srinath MADASU
Shashi DANDE
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Landmark Graphics Corporation
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Priority claimed from PCT/US2019/064655 external-priority patent/WO2021040764A1/fr
Application filed by Landmark Graphics Corporation filed Critical Landmark Graphics Corporation
Priority to GB2200669.6A priority Critical patent/GB2600294A/en
Publication of WO2021041254A1 publication Critical patent/WO2021041254A1/fr

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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
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    • G06Q10/00—Administration; Management
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
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    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
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    • G06Q10/00—Administration; Management
    • G06Q10/10—Office automation; Time management
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/02—Agriculture; Fishing; Forestry; Mining
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
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    • H—ELECTRICITY
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    • H04L9/32—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system or for message authentication, e.g. authorization, entity authentication, data integrity or data verification, non-repudiation, key authentication or verification of credentials
    • H04L9/3236—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system or for message authentication, e.g. authorization, entity authentication, data integrity or data verification, non-repudiation, key authentication or verification of credentials using cryptographic hash functions
    • H04L9/3239—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system or for message authentication, e.g. authorization, entity authentication, data integrity or data verification, non-repudiation, key authentication or verification of credentials using cryptographic hash functions involving non-keyed hash functions, e.g. modification detection codes [MDCs], MD5, SHA or RIPEMD
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
    • H04L9/32—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system or for message authentication, e.g. authorization, entity authentication, data integrity or data verification, non-repudiation, key authentication or verification of credentials
    • H04L9/3297—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system or for message authentication, e.g. authorization, entity authentication, data integrity or data verification, non-repudiation, key authentication or verification of credentials involving time stamps, e.g. generation of time stamps
    • 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
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/40—Seismology; Seismic or acoustic prospecting or detecting specially adapted for well-logging
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR 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]
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    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/12—Computing arrangements based on biological models using genetic models
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q2220/00—Business processing using cryptography
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
    • H04L9/50—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols using hash chains, e.g. blockchains or hash trees

Definitions

  • FIG. 2 is an illustration a flow diagram of an algorithm for creating blocks in the blockchain, in accordance with certain example embodiments
  • FIG. 6 is an illustration of a computing machine and a system applications module, in accordance with certain example embodiments.
  • the system can further include at least one partition.
  • Each partition comprises the at least one node system stack, at least one selected from a group comprising the least one predictive engine and at least one process of the at least one predictive engine, and at least one selected from a group comprising the least one machine learning engine and at least one process of the at least one machine learning engine.
  • the at least one node system stack can include a middleware controller.
  • the middleware controller is communicable coupled to each partition, each node system stack, each predictive engine, each process of the predictive engine, each machine learning engine, and each process of the machine learning engine.
  • the system can also include an optimization engine.
  • the optimization engine optimizes the generated earth model variables by sampling the generated earth model variables based on at least one drilling model and an optimization tool.
  • the system can also include a deep particle filter to clean the well log data variables and seismic data variables.
  • the system can also include a forward modeling component to compare predicted variables in the generated earth model to the cleaned or uncleaned well log data variables and seismic data variables.
  • the method includes generating at least one trained data model generated using at least one machine learning algorithm, at least one algorithmically generated earth model, at least one control variable.
  • the method also includes training an artificial intelligence algorithm using an artificial intelligence algorithm, the at least one trained data model, the trained artificial intelligence algorithm having a parameter set less than the artificial intelligence algorithm.
  • the method further includes generating earth model variables using the trained artificial intelligence algorithm.
  • the method also includes communicable coupling at least one node system stack to the at least one predictive engine, a distributed network, a plurality of sensors, and at least one machine controller.
  • the method also includes creating at least one chained block in a distributed network, the distributed network comprising a genesis block and a plurality of subsequent blocks, each subsequent block comprising a well site entry and a cryptographic hash value of a previous well site entry, wherein the well site entry comprises at least one transacted operation control variable.
  • the method further includes controlling at least one well site operation using the at least one transacted operation control variable.
  • the at least one transacted operation control variable is, at least in part, based on at least one of the generated earth model variables.
  • Predictor variables are variables used to predict an outcome.
  • Outcome variables are variables in which their value is dependent on a predictor variable or predictor variables.
  • Feature selection means an algorithm that can identify and select variables within a data source that contribute to the predictor variables and outcome variables.
  • Variable interaction means that the contribution of one predictor variable is modified by one or many other predictor variables, so that the combined contribution of all variables involved in the interaction is greater than the simple sum over the individual contributions attributable to each variable.
  • An earth model defines the spatial distribution of sub-surface properties such as permeability, porosity, faults, salt bodies, etc.
  • Typical variables in a drilling model can include, and without limitation, weight on bit, rotations per minute of the drill bit, mud flow rate, differential pressure of the mud-motor, stand pipe pressure.
  • the well site automation module manages communication and processing of the blockchain 12 and service provider administration systems 18.
  • the well site operations module manages communication and processing for cloud service 16, and well site operator systems 20.
  • the sensor bank and controller module manages communication and processing for sensor bank module and controller 22 and sensors and controllers 24. The module together work to perform the functions described herein.
  • well site operator can manage operations of well site equipment based on a plurality of variables and variable types procured from various sources, data models, Machine Learning (ML) and Artificial Intelligence (AI) algorithmic models using operation control variables procured therefrom. Provenance and security of the variables, such as the operation control variables, are preserved in the blockchain 12. Other variables can also be stored in the blockchain 12. The integrity of the data can be further maintained by relying on multiple cloud service providers to maintain and provide the Public Key Infrastructure (PKI) between consumer and the blockchain Service Provider (SP).
  • PKI Public Key Infrastructure
  • SP blockchain Service Provider
  • a first block is created in the blockchain 12 called a genesis block and provides base components, such as a nonce, public key, and signature.
  • the genesis block is owned by an entity providing the blockchain application services, that is to say the blockchain SP.
  • the public key and signature are from the PKI and can be provided by one or more cloud service providers.
  • the infrastructure is from independent sources and, therefore, the blockchain 12 can comprise of truly disparate blocks that are far less likely to be compromised.
  • proper management of the infrastructure under a single service provider scenario can also be as secure providing the provider properly secures the PKI keys and signatures.
  • a well-known technique of creating blocks within the blockchain 12 is to configure the base blockchain applications (Base BC1 - Base BCN) to compete to solve a complex problem.
  • the base blockchain applications (Base BC1 - Base BCN) can compete to solve for a particular nonce.
  • the application that solves the problem first is awarded the next block in the blockchain and, therefore, can create a transacted well site entry in the awarded block.
  • the blocks 26 can include a well site entry, a nonce, a hash of a previous block or portion of the previous block, and public key and signature from a PKI.
  • the ROS nodes 34 can process work orders from multiple well site operators and create entries in the blockchain in real-time or near real time.
  • a well site operator 20 or, in the event of a genesis block, the blockchain service provider 18 receive a PKI keyset, i.e. the public and private key, and digital signature from the cloud service provider 16.
  • the blockchain service provider 18 and the well site operator 20 securely store the private key locally and the public key is stored in the blockchain 12 or stored in a place managed by the base blockchain applications (Base BC1 - Base BCN) and associated well site automation applications (WS Auto Appi - WS Auto AppN).
  • the public key is obviously used to create the hash of a previous well site entry or block and the private key used to recreate the entry from the hash.
  • the sensor variables can include, and without limitation, production equipment measurements, telemetry data for various instruments, rig sensing data, surface measurements, fluid and additive measurements, downhole measurements LWD/LIWD, cementing measurements, wireline and perforations data, and earth model data.
  • the operation control variables can comprises variables that, and without limitation, control production equipment, such as cementing equipment, rigs, wireline and perforation equipment, and a rotary steerable electronic bit.
  • the control variables are forecast variables that are generated based on predictive analysis, which will be described in more detail later.
  • control variables may be immediately routed to the sensor bank and controller 22 to control equipment operations or delayed until an appropriate time that can be based on decisions from well site operator personnel and/or at time deemed necessary based on the forecast and instantaneous or updated sensor variables.
  • select variables can come from service provider administration system 18, well site operator system 20, and/or the sensor bank and controller 22.
  • variables are computed.
  • the computed variables can include an earth model and equipment operation control variables. The computation of the computed variables is discussed in greater detail in reference to Figs. 3-5.
  • a well site entry is generated and communicated to the distributed network 14.
  • the well site entry can include, and without limitation, a well site identifier, computed variables, and a public key of the PKI 38. What is exactly included in the well site entry can also be determined by the well site operator.
  • the well site entry can include.
  • Computed variables can include earth model variables, equipment operation control variables, and algorithmic models.
  • an entry is created in a block or blocks 26 of the blockchain 12.
  • the transacted entry can include, and without limitation, well site identifier, a time stamp, computed variables, a public key, and a hash value of a previous block.
  • operation of well site equipment is controlled using the control variables. Operation of the equipment can be controlled in real-time or based on a timer or a trigger, such as a decision from well site personnel or subsequent computations.
  • the earth model can be used to determine drilling regions through which drilling can progress from a starting location to a target location, while going through various waypoints. The starting location, the waypoints, and the target location can be provided by a user.
  • Each node system stack 102 comprises at least one Operating System (OS) and a middleware controller.
  • OS Operating System
  • the middleware controller is fitted with code to perform a particular process of, e.g., the predictive engine 104.
  • the middleware controller is a software component that is operable on a node, e.g. ROS nodes 34, between a physical layer and user space applications and/or certain OS kernel applications.
  • the physical layer being a software and hardware layer that provides necessary details of how to interface data through a transmission medium.
  • Common middleware components include the MAC (Medium Access Control) layer, network layer, and transport layer of the OSI (Open Systems Interconnection) model.
  • An example of the middleware controller is the open source Robot Operating System (ROS).
  • ROS Robot Operating System
  • the middleware controller of the ROS based node system stack 102 and the node(s) and partitions are configured to share messages between executing entities to accomplish an analytics goal and provide control variables for managing well site operations.
  • nodes and partitions can be set up and designated as preforming analytics operations, such as a node and partition configured as the predictive engine 104 or sub-process of such, or system control operations, such as controlling drilling operations of a particular machine for a particular site.
  • the well log data variables and seismic data variables can include current drilling coordinates, production equipment measurements, rig sensing and control, fluids and additive measurements, cementing measurements and controls, wireline and perforations sense and control, telemetry, surface measurements, downhole measurements, rotary steerable electronic bit, and earth physical properties data.
  • the other data can include initial realizations from well planning variables and Subject Matter Expert (SME) variables.
  • SME Subject Matter Expert
  • the accuracy of the generated earth model is assessed using a simulator 134. For example, the accuracy of the generated model can be assessed based on a reservoir fracking or reservoir production simulation. In essence, historical data and trained data can be used to assess whether the generated model can be determined to be reliable based on past historical models and operations.
  • the optimization engine 136 can generate a drill path or production control variable or variables using statistics based pattern recognition, such as using stochastic modeling techniques, and the generated earth model.
  • the drill path can be a set of variables defining sub-surface, earth coordinates.
  • actions can also be generated describing what corrective actions or manipulations of equipment are needed in order to create an optimal drill path.
  • the production control variables can be a set of variables used to control a valve or a pump.
  • Machine learning algorithms such as MultiVariate Regression model (MVR), Artificial Neural Network (ANN), or a decision tree-based algorithm, or any combination thereof, can be used to generate the training and validation datasets and the trained algorithmic models used to create the data models.
  • the decision tree-based algorithm can be an Extreme Gradient Boosting (XGB) algorithm.
  • the ML engine 140 is used to store drill path variables, production control variables, earth model (predictor variables), outcome variable(s), and an objective function or functions in the storage devices 12 and/or 16 as training data. Over time, the ML engine 140 can create trained data models, e.g. trained earth models, have a reduced parameter space than those of the stored variables. The ML engine 140 generates many possible drill paths from drill bit current location to target location while passing through various waypoints and avoiding various hazardous regions and or regions which need to be avoided during drilling, for example region with water, drilling along the fault, salt region, region where mud loss possibility is higher, etc.
  • the algorithm 200 uses a machine learning algorithm to train data and create data models, such as earth models having a reduced parameter set.
  • the algorithm 200 can store the earth model, the at least one drill path, the plurality of variables, and/or the drilling models, i.e. trained earth models, in a distributed storage network, e.g. as entries in a blockchain, 12. Obviously, many of the same variables can be stored in various databases in cloud service 16.
  • the drill path for a downhole drill bit is adjusted based on the difference between the current drill path and the at least one predicted drill path. In embodiment, the drill path can be adjusted when the difference between the current drill path and the at least one predicted drill path exceeds a predetermined threshold.
  • the algorithm 200 can updated the generated visualization.
  • the algorithm 200 sends commands to the sensor bank and controller 22 and/or the systems 18, 20.
  • the processor 310 can be designed to execute code instructions in order to perform the operations and functionality described herein, manage request flow and address mappings, and to perform calculations and generate commands.
  • the processor 310 can be configured to monitor and control the operation of the components in the computing machines.
  • the processor 310 can be a general purpose processor, a processor core, a multiprocessor, a reconfigurable processor, a microcontroller, a digital signal processor (“DSP”), an application specific integrated circuit (“ASIC”), a controller, a state machine, gated logic, discrete hardware components, any other processing unit, or any combination or multiplicity thereof.
  • the I/O interface 320 can couple the computing machine to various input devices including mice, touch-screens, scanners, electronic digitizers, sensors, receivers, touchpads, trackballs, cameras, microphones, keyboards, any other pointing devices, or any combinations thereof.
  • the I/O interface 320 can couple the computing machine to various output devices including video displays, speakers, printers, projectors, tactile feedback devices, automation control, robotic components, actuators, motors, fans, solenoids, valves, pumps, transmitters, signal emitters, lights, and so forth.
  • the sensors 380 and controllers 390 can be components of field device components 18, i.e. surface and sub-surface sensors, configured to sense various physical properties, i.e. mechanical, chemical, and electrical properties, of surface sub-surface downhole machines and surrounding environment and communicate sensed data to the sensor hub 20.
  • software can include one or more lines of code or other suitable software structures operating in a general purpose software application, such as an operating system, and one or more lines of code or other suitable software structures operating in a specific purpose software application.
  • the term “couple” and its cognate terms, such as “couples” and “coupled,” can include a physical connection (such as a copper conductor), a virtual connection (such as through randomly assigned memory locations of a data memory device), a logical connection (such as through logical gates of a semiconducting device), other suitable connections, or a suitable combination of such connections.
  • data can refer to a suitable structure for using, conveying or storing data, such as a data field, a data buffer, a data message having the data value and sender/receiver address data, a control message having the data value and one or more operators that cause the receiving system or component to perform a function using the data, or other suitable hardware or software components for the electronic processing of data.
  • a software system is a system that operates on a processor to perform predetermined functions in response to predetermined data fields.
  • a system can be defined by the function it performs and the data fields that it performs the function on.
  • a NAME system where NAME is typically the name of the general function that is performed by the system, refers to a software system that is configured to operate on a processor and to perform the disclosed function on the disclosed data fields. Unless a specific algorithm is disclosed, then any suitable algorithm that would be known to one of skill in the art for performing the function using the associated data fields is contemplated as falling within the scope of the disclosure.
  • each partition comprises the at least one node system stack, at least one selected from a group comprising the least one predictive engine and at least one process of the at least one predictive engine, and at least one selected from a group comprising the least one machine learning engine and at least one process of the at least one machine learning engine;
  • Clause 5 the system of clause 1, further comprising an optimization engine, the optimization engine optimizes the generated earth model variables by sampling the generated earth model variables based on at least one drilling model and an optimization tool;
  • each partition comprises the at least one node system stack, at least one selected from a group comprising the least one predictive engine and at least one process of the at least one predictive engine, and at least one selected from a group comprising the least one machine learning engine and at least one process of the at least one machine learning engine;
  • Clause 17 the method of clause 16, communicable coupling a middleware controller to each partition, each node system stack, each predictive engine, each process of the predictive engine, each machine learning engine, and each process of the machine learning engine;
  • Clause 19 the method of clause 15, further comprising optimizing the generated earth model variables by sampling the generated earth model variables based on at least one drilling model and one of a Bayesian optimization, genetic algorithm optimization, and particle swarm optimization; and
  • Clause 20 the method of clause 15, further comprising cleaning the well log data variables and seismic data variables using a deep particle filter; and comparing predicted variables in the generated earth model to the cleaned well log data variables and seismic data variables using a forward modeling component.

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Abstract

La présente invention concerne un système, permettant de commander des opérations de sites de puits, comprenant un moteur d'apprentissage machine, un moteur prédictif, un empilement de systèmes de nœuds et une chaîne de blocs. Le moteur d'apprentissage comprend un algorithme d'apprentissage machine, un modèle terrestre généré de manière algorithmique et des variables de commande. L'algorithme d'apprentissage génère un modèle de données entraîné à l'aide du modèle terrestre généré de manière algorithmique. Le moteur prédictif comprend un algorithme d'intelligence artificielle (AI). L'algorithme d'AI génère un algorithme d'AI entraîné à l'aide du modèle de données entraîné et des variables de modèle terrestre à l'aide de l'algorithme d'AI entraîné. L'empilement de systèmes est couplé en communication avec le moteur prédictif, le moteur d'apprentissage, la chaîne de blocs, les capteurs et un dispositif de commande de machine. La chaîne de blocs comprend un bloc de genèse et une pluralité de blocs suivants. Chaque bloc suivant comprend une entrée de site de puits et un hachage d'une entrée précédente. L'entrée de site de puits comprend des variables de commande de fonctionnement soumises à une transaction. Les variables soumises à une transaction sont basées sur les variables de modèle terrestre générées.
PCT/US2020/047502 2019-08-23 2020-08-21 Plateforme informatique répartie de gestion de réservoirs à base d'ai/ml et de chaînes de blocs WO2021041254A1 (fr)

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US201962891223P 2019-08-23 2019-08-23
US62/891,223 2019-08-23
PCT/US2019/064655 WO2021040764A1 (fr) 2019-08-23 2019-12-05 Plateforme de production et de forage se basant sur ai/ml
USPCT/US2019/064655 2019-12-05
US202016651859A 2020-03-27 2020-03-27
US16/651,859 2020-03-27

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PCT/US2020/047498 WO2021041251A1 (fr) 2019-08-23 2020-08-21 Plate-forme de gestion automatisée de réservoir basée sur l'ia/aa et une chaîne de blocs
PCT/US2020/047502 WO2021041254A1 (fr) 2019-08-23 2020-08-21 Plateforme informatique répartie de gestion de réservoirs à base d'ai/ml et de chaînes de blocs

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PCT/US2020/047498 WO2021041251A1 (fr) 2019-08-23 2020-08-21 Plate-forme de gestion automatisée de réservoir basée sur l'ia/aa et une chaîne de blocs

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WO2021041252A1 (fr) 2021-03-04
GB2600294A (en) 2022-04-27
NO20220092A1 (en) 2022-01-21
WO2021041251A1 (fr) 2021-03-04
GB2600293A (en) 2022-04-27
GB2600293B (en) 2023-03-22
GB2600296A (en) 2022-04-27
NO20220097A1 (en) 2022-01-21

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