A method for generating a merged survey dataset, that includes obtaining a plurality of survey datasets, calculating edges between survey datasets of the plurality of survey datasets, modifying the plurality of survey datasets, based on the edges, to obtain a plurality of modified survey datasets, and generating the merged survey dataset using the plurality of modified survey datasets.
A method for generating a merged survey dataset, that includes obtaining a plurality of survey datasets, calculating edges between survey datasets of the plurality of survey datasets, modifying the plurality of survey datasets, based on the edges, to obtain a plurality of modified survey datasets, and generating the merged survey dataset using the plurality of modified survey datasets.
A method comprises obtaining geology data of a subsurface formation and generating a subsurface model of the subsurface formation, the subsurface model including one or more age-attributed geometries of a first age scheme. The method comprises obtaining a first contextual information dataset of a target age scheme and converting each of the one or more age-attributed geometries to a target age-attributed geometry based on the target age scheme. The method comprises integrating the first contextual information dataset into the subsurface model, via the one or more target age-attributed geometries, to generate a context volume. The method comprises performing a subsurface operation based on the context volume.
A method comprises obtaining geology data of a subsurface formation and generating a subsurface model of the subsurface formation, the subsurface model including one or more age-attributed geometries of a first age scheme. The method comprises obtaining a first contextual information dataset of a target age scheme and converting each of the one or more age-attributed geometries to a target age-attributed geometry based on the target age scheme. The method comprises integrating the first contextual information dataset into the subsurface model, via the one or more target age-attributed geometries, to generate a context volume. The method comprises performing a subsurface operation based on the context volume.
A method for determining an emissions associated with hydrocarbon recovery of a hydrocarbon site within a geographic region, the method comprises selecting the hydrocarbon site for which to determine the emissions. The method comprises determining current values of hydrocarbon related attributes that affect emissions at the hydrocarbon site for a current time frame. The method comprises inputting the current values of the hydrocarbon related attributes related to emissions at the hydrocarbon site into a learning machine to generate an emissions factor for each of the hydrocarbon related attributes that affect the emissions at the hydrocarbon site.
A method for determining an emissions associated with hydrocarbon recovery of a hydrocarbon site within a geographic region, the method comprises selecting the hydrocarbon site for which to determine the emissions. The method comprises determining current values of hydrocarbon related attributes that affect emissions at the hydrocarbon site for a current time frame. The method comprises inputting the current values of the hydrocarbon related attributes related to emissions at the hydrocarbon site into a learning machine to generate an emissions factor for each of the hydrocarbon related attributes that affect the emissions at the hydrocarbon site.
In some embodiments, a method comprises quantifying a condition of a wellbore into which a string is to be deployed, wherein quantifying the condition of the wellbore comprises, determining at least one geometrical parameter of the wellbore; and determining at least one mechanical parameter of the string that is caused by deploying the string downhole into the wellbore. The method also comprises determining a string runnability index for running the string into the wellbore based on the quantified condition of the wellbore.
E21B 44/00 - Systèmes de commande automatique spécialement adaptés aux opérations de forage, c. à d. systèmes à fonctionnement autonome ayant pour rôle d'exécuter ou de modifier une opération de forage sans l'intervention d'un opérateur humain, p.ex. systèmes de ; Systèmes spécialement adaptés à la surveillance de plusieurs variables ou conditions de forage
E21B 47/022 - Détermination de l'inclinaison ou de la direction du trou de forage, p.ex. à l'aide de géomagnétisme
8.
WELLBORE STRING RUNNABILITY FOR DOWNHOLE OPERATIONS
In some embodiments, a method comprises quantifying a condition of a wellbore into which a string is to be deployed, wherein quantifying the condition of the wellbore comprises, determining at least one geometrical parameter of the wellbore; and determining at least one mechanical parameter of the string that is caused by deploying the string downhole into the wellbore. The method also comprises determining a string runnability index for running the string into the wellbore based on the quantified condition of the wellbore.
E21B 43/10 - Mise en place de tubages, filtres ou crépines dans les puits
E21B 23/04 - Appareils pour déplacer, mettre en place, verrouiller, libérer ou retirer, les outils, les packers ou autres éléments dans les trous de forage mis en œuvre à l'aide de moyens fluides, p.ex. actionnés par explosion
E21B 23/02 - Appareils pour déplacer, mettre en place, verrouiller, libérer ou retirer, les outils, les packers ou autres éléments dans les trous de forage pour verrouiller les outils ou autres éléments sur des supports ou dans des retraits entre sections adjacentes du tubage
9.
DIFFUSION MODELING BASED SUBSURFACE FORMATION EVALUATION
Some implementations include a method for controlling a computer to geologically characterize a space relative to a borehole. The method may include configuring a diffusion process applied to information and data about samples of reservoir parameters. The method also may include determining, via the diffusion process, a probability distribution of the reservoir parameters in the space relative to the borehole.
Some implementations include a method for controlling a computer to geologically characterize a space relative to a borehole. The method may include configuring a diffusion process applied to information and data about samples of reservoir parameters. The method also may include determining, via the diffusion process, a probability distribution of the reservoir parameters in the space relative to the borehole.
A method comprising obtaining a thickness for each of one or more sediment packages of a subsurface formation. The method comprises generating a thickness profile of each of the one or more sediment packages based on the thickness. The method comprises obtaining one or more properties of each of the one or more sediment packages based on the thickness profile. The method comprises generating, via a learning machine, one or more sediment package classifications based on the one or more properties. The method comprises and performing a subsurface operation based on the one or more sediment package classifications.
G06F 18/2411 - Techniques de classification relatives au modèle de classification, p.ex. approches paramétriques ou non paramétriques basées sur la proximité d’une surface de décision, p.ex. machines à vecteurs de support
A method comprising obtaining a thickness for each of one or more sediment packages of a subsurface formation. The method comprises generating a thickness profile of each of the one or more sediment packages based on the thickness. The method comprises obtaining one or more properties of each of the one or more sediment packages based on the thickness profile. The method comprises generating, via a learning machine, one or more sediment package classifications based on the one or more properties. The method comprises and performing a subsurface operation based on the one or more sediment package classifications.
A system for drilling a borehole at a wellsite, the system including a BHA with a drill bit and operable to drill the borehole as part of a borehole drilling operation using a modeled operational parameter. The system also includes a sensor located in the borehole and operable to measure an actual operational parameter in real-time. A controller including a processor performs operations that include: determining a modeled result for the borehole drilling operation using a well engineering model; receiving the measurement of the actual operational parameter from the sensor; determining an actual result for the borehole drilling operation using the well engineering model; automatically calibrating the well engineering model using the modeled result and the actual result to produce a calibrated well engineering model; and adjusting the modeled operational parameter of the drilling operation based on the calibrated well engineering model.
E21B 44/02 - Commande automatique de l'avance de l'outil
E21B 47/01 - Dispositifs pour supporter des instruments de mesure sur des trépans, des tubes, des tiges ou des câbles de forage; Protection des instruments de mesure dans les trous de forage contre la chaleur, les chocs, la pression ou similaire
A system for drilling a borehole at a wellsite, the system including a BHA with a drill bit and operable to drill the borehole as part of a borehole drilling operation using a modeled operational parameter. The system also includes a sensor located in the borehole and operable to measure an actual operational parameter in real-time. A controller including a processor performs operations that include: determining a modeled result for the borehole drilling operation using a well engineering model; receiving the measurement of the actual operational parameter from the sensor; determining an actual result for the borehole drilling operation using the well engineering model; automatically calibrating the well engineering model using the modeled result and the actual result to produce a calibrated well engineering model; and adjusting the modeled operational parameter of the drilling operation based on the calibrated well engineering model.
E21B 44/00 - Systèmes de commande automatique spécialement adaptés aux opérations de forage, c. à d. systèmes à fonctionnement autonome ayant pour rôle d'exécuter ou de modifier une opération de forage sans l'intervention d'un opérateur humain, p.ex. systèmes de ; Systèmes spécialement adaptés à la surveillance de plusieurs variables ou conditions de forage
E21B 47/00 - Relevés dans les trous de forage ou dans les puits
G06F 30/13 - Conception architecturale, p.ex. conception architecturale assistée par ordinateur [CAAO] relative à la conception de bâtiments, de ponts, de paysages, d’usines ou de routes
15.
BRITTLE-BURST STRENGTH FOR WELL SYSTEM TUBULAR INTEGRITY
A system can receive data relating to a tubular of a well system. The system can execute a first module to determine first outputs. The system can execute a second module to determine second outputs based on the first outputs. The system can execute a third module to determine third outputs based on the first outputs. The second outputs can include a crack-initiation fracture pressure, and the third outputs can include a crack-propagation fracture pressure. The system can identify a brittle-burst strength of the tubular from among the second outputs, the third outputs, and a standard burst strength of the tubular. The system can provide the brittle-burst strength of the tubular to facilitate an adjustment to the tubular to optimize a wellbore operation associated with the well system.
A fluid injection process for injecting fluid into a wellbore can be adjusted based on relative permeability. An actual trapped gas saturation of a formation can be determined from a maximum gas saturation, a maximum trapped gas saturation, and an actual gas saturation. A pseudo-maximum gas saturation can be determined from the actual trapped gas saturation, the maximum trapped gas saturation, and actual gas saturation. A relative permeability of the formation can be determined by mapping the pseudo-maximum gas saturation along a drainage curve. The fluid injection process can be adjusted based on the relative permeability.
A fluid injection process for injecting fluid into a wellbore can be adjusted based on relative permeability. An actual trapped gas saturation of a formation can be determined from a maximum gas saturation, a maximum trapped gas saturation, and an actual gas saturation. A pseudo-maximum gas saturation can be determined from the actual trapped gas saturation, the maximum trapped gas saturation, and actual gas saturation. A relative permeability of the formation can be determined by mapping the pseudo-maximum gas saturation along a drainage curve. The fluid injection process can be adjusted based on the relative permeability.
In some embodiments, a method for computing, by a volume data processor, volumetrics of a subsurface region without gridlines associated with the subsurface region comprises creating, in the volume data processor, a geometry representing the subsurface region and first bounding box about the geometry, computing a first probability that a group of sampled points inside the first bounding box are inside the geometry, and computing a gross rock volume (GRV) of the geometry by multiplying the first probability by a volume of the first bounding box.
E21B 47/12 - Moyens pour la transmission de signaux de mesure ou signaux de commande du puits vers la surface, ou de la surface vers le puits, p.ex. pour la diagraphie pendant le forage
In some embodiments, a method for computing, by a volume data processor, volumetrics of a subsurface region without gridlines associated with the subsurface region comprises creating, in the volume data processor, a geometry representing the subsurface region and first bounding box about the geometry, computing a first probability that a group of sampled points inside the first bounding box are inside the geometry, and computing a gross rock volume (GRV) of the geometry by multiplying the first probability by a volume of the first bounding box.
G01V 99/00 - Matière non prévue dans les autres groupes de la présente sous-classe
E21B 49/00 - Test pour déterminer la nature des parois des trous de forage; Essais de couches; Procédés ou appareils pour prélever des échantillons du terrain ou de fluides en provenance des puits, spécialement adaptés au forage du sol ou aux puits
A system can be used to incorporate historical geological data into machine learning techniques. The system can receive historical geological data. The system can pre-process the historical geological data by applying a selected, relative-time pre-processing technique to the historical geological data with respect to time-attributed geological phenomena. The system can train a machine-learning model using the pre-processed historical geological data. The system can apply the trained machine-learning model to generate predictions of geological phenomena. The system can provide a user interface to provide a visualization of the predictions of geological phenomena.
A system can be used to incorporate historical geological data into machine learning techniques. The system can receive historical geological data. The system can pre-process the historical geological data by applying a selected, relative-time pre-processing technique to the historical geological data with respect to time-attributed geological phenomena. The system can train a machine-learning model using the pre-processed historical geological data. The system can apply the trained machine-learning model to generate predictions of geological phenomena. The system can provide a user interface to provide a visualization of the predictions of geological phenomena.
E21B 47/12 - Moyens pour la transmission de signaux de mesure ou signaux de commande du puits vers la surface, ou de la surface vers le puits, p.ex. pour la diagraphie pendant le forage
09 - Appareils et instruments scientifiques et électriques
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
downloadable and recorded computer software for use in the exploration and production of hydrocarbons in the oil and gas industry providing online non-downloadable computer software for use in the exploration and production of hydrocarbons in the oil and gas industry
23.
BRITTLE-BURST STRENGTH FOR WELL SYSTEM TUBULAR INTEGRITY
A system can receive data relating to a tubular of a well system. The system can execute a first module to determine first outputs. The system can execute a second module to determine second outputs based on the first outputs. The system can execute a third module to determine third outputs based on the first outputs. The second outputs can include a crack-initiation fracture pressure, and the third outputs can include a crack-propagation fracture pressure. The system can identify a brittle-burst strength of the tubular from among the second outputs, the third outputs, and a standard burst strength of the tubular. The system can provide the brittle-burst strength of the tubular to facilitate an adjustment to the tubular to optimize a wellbore operation associated with the well system.
E21B 47/06 - Mesure de la température ou de la pression
E21B 47/12 - Moyens pour la transmission de signaux de mesure ou signaux de commande du puits vers la surface, ou de la surface vers le puits, p.ex. pour la diagraphie pendant le forage
Disclosed herein are embodiments of a method, a non-transitory computer readable medium, and an apparatus for faulted seismic horizon mapping. In one example, a method comprises: obtaining seismic data for a seismic volume that corresponds to a subsurface formation; generating a map of at least one horizon in the subsurface formation based on the seismic volume; identifying at least one fault intersecting the at least one horizon; determining a throw of the at least one fault; and updating the map of the at least one horizon to incorporate the at least one fault based on the throw of the at least one fault.
G01V 1/34 - Représentation des enregistrements sismiques
E21B 49/00 - Test pour déterminer la nature des parois des trous de forage; Essais de couches; Procédés ou appareils pour prélever des échantillons du terrain ou de fluides en provenance des puits, spécialement adaptés au forage du sol ou aux puits
A system can generate, via a software model, downhole pressure estimations and downhole debris estimations using caving parameters. Additionally, the system can generate, via the software model, settled caving volume percent estimations using the caving parameters. The system can determine a pack off volume percent using the downhole pressure estimations, the downhole debris estimations, and the settled caving volume percent estimations. The system can output, via a user interface, the pack off indicator and a subset of the caving parameters for use in adjusting a wellbore operation. The user interface can provide a plot of the pack off volume percent horizontally offset with respect to a plot of the subset of the caving parameters and a depth of the wellbore.
A method for processing seismic data by a seismic data system. The method comprises acquiring a plurality of first traces each corresponding to a respective first trace location. The method comprises expressing the first traces as first vertices in a first graph in which first edges connect the first vertices, wherein the first edges indicate positioning of the first vertices.
A system can generate, via a software model, downhole pressure estimations and downhole debris estimations using caving parameters. Additionally, the system can generate, via the software model, settled caving volume percent estimations using the caving parameters. The system can determine a pack off volume percent using the downhole pressure estimations, the downhole debris estimations, and the settled caving volume percent estimations. The system can output, via a user interface, the pack off indicator and a subset of the caving parameters for use in adjusting a wellbore operation. The user interface can provide a plot of the pack off volume percent horizontally offset with respect to a plot of the subset of the caving parameters and a depth of the wellbore.
E21B 44/00 - Systèmes de commande automatique spécialement adaptés aux opérations de forage, c. à d. systèmes à fonctionnement autonome ayant pour rôle d'exécuter ou de modifier une opération de forage sans l'intervention d'un opérateur humain, p.ex. systèmes de ; Systèmes spécialement adaptés à la surveillance de plusieurs variables ou conditions de forage
E21B 47/04 - Mesure de la profondeur ou du niveau du liquide
G01V 99/00 - Matière non prévue dans les autres groupes de la présente sous-classe
Disclosed herein are embodiments of a method, a non-transitory computer readable medium, and an apparatus for faulted seismic horizon mapping. In one example, a method comprises: obtaining seismic data for a seismic volume that corresponds to a subsurface formation; generating a map of at least one horizon in the subsurface formation based on the seismic volume; identifying at least one fault intersecting the at least one horizon; determining a throw of the at least one fault; and updating the map of the at least one horizon to incorporate the at least one fault based on the throw of the at least one fault.
A method for processing seismic data by a seismic data system. The method comprises acquiring a plurality of first traces each corresponding to a respective first trace location. The method comprises expressing the first traces as first vertices in a first graph in which first edges connect the first vertices, wherein the first edges indicate positioning of the first vertices.
G01V 1/36 - Exécution de corrections statiques ou dynamiques sur des enregistrements, p.ex. correction de l'étalement; Etablissement d'une corrélation entre signaux sismiques; Elimination des effets produits par un excès d'énergie
30.
METHOD AND SYSTEM FOR PREDICTION AND CLASSIFICATION OF INTEGRATED VIRTUAL AND PHYSICAL SENSOR DATA
The present disclosure is related to improvements in methods for evaluating and predicting responses of virtual sensors to determine formation and fluid properties as well as classifying the predicted as plausible or outlier responses that can indicate the need for maintenance of downhole physical sensors. In one aspect, a method includes detecting a change to a system of operating a wellbore to yield a determination, the system including a virtual sensor, the virtual sensor including a physical sensor placed in the wellbore for collecting one or more physical properties inside the wellbore; and based on the determination, performing one of retraining a machine learning model for predicting an output of the virtual sensor or predicting an output of the virtual sensor using the machine learning mode, the predicted output being indicative of at least one of sub-surface formation or fluid properties inside the wellbore.
A method for controlling computerized operations related to a wellbore comprises drilling the wellbore in a subsurface formation with a drill string including a drill bit. The method comprises acquiring a plurality of drilling parameters while drilling the wellbore. The method comprises determining, based on the plurality of drilling parameters, solids properties for solids forming a cutting plug up hole of the drill bit. The method comprises determining a length of the cutting plug based on the solids properties. The method comprises determining a cutting plug friction force based on the cutting plug length and a pressure differential across the cutting plug. The method comprises performing a drilling operation based on the cutting plug friction force.
E21B 44/02 - Commande automatique de l'avance de l'outil
E21B 44/00 - Systèmes de commande automatique spécialement adaptés aux opérations de forage, c. à d. systèmes à fonctionnement autonome ayant pour rôle d'exécuter ou de modifier une opération de forage sans l'intervention d'un opérateur humain, p.ex. systèmes de ; Systèmes spécialement adaptés à la surveillance de plusieurs variables ou conditions de forage
E21B 29/00 - Découpage ou destruction de tubes, packers, bouchons ou câbles, situés dans les trous de forage ou dans les puits, p.ex. découpage de tubes endommagés, de fenêtres; Déformation des tubes dans les trous de forage; Remise en état des tubages de puits sans les retirer du sol
A method for controlling computerized operations related to a wellbore comprises drilling the wellbore in a subsurface formation with a drill string including a drill bit. The method comprises acquiring a plurality of drilling parameters while drilling the wellbore. The method comprises determining, based on the plurality of drilling parameters, solids properties for solids forming a cutting plug up hole of the drill bit. The method comprises determining a length of the cutting plug based on the solids properties. The method comprises determining a cutting plug friction force based on the cutting plug length and a pressure differential across the cutting plug. The method comprises performing a drilling operation based on the cutting plug friction force.
The disclosure relates to determining rock properties of subterranean formations and learning the distribution of hydrocarbons in the formations. A geometrical element spread function is disclosed that quantifies distortion of the geology as seen by the geophysicists who process seismic images of the subterranean formations. A method of determining the rock properties using the seismic images and synthetic images is provided. In one example, the method includes: (1) obtaining seismic data from a subterranean formation using a seismic acquisition system, (2) generating one or more seismic images of the subterranean formation using the seismic data, (3) creating one or more synthetic images from the one or more seismic images, and (4) determining rock properties of the subterranean formation based on the one or more seismic images and the one or more synthetic images.
E21B 49/00 - Test pour déterminer la nature des parois des trous de forage; Essais de couches; Procédés ou appareils pour prélever des échantillons du terrain ou de fluides en provenance des puits, spécialement adaptés au forage du sol ou aux puits
G01V 1/34 - Représentation des enregistrements sismiques
34.
INFERRING SUBSURFACE KNOWLEDGE FROM SUBSURFACE INFORMATION
A geoscience knowledge system can be obtained, where the geoscience knowledge system can include one or more of publicly available information, industry information, proprietary information, or task specific information. The geoscience knowledge system can be represented as a graph, graph data, network nodes, image data, tokenized data, or textualized data. Subsurface information can be obtained such as from seismic images or other types of sensor data. The subsurface information can be transformed or pre-processed, such as denoising, to make it suitable for use by the geoscience knowledge system. Then subsurface knowledge can be inferred from the subsurface information using the geoscience knowledge system. The subsurface knowledge can provided estimates, approximations, or value of the subterranean formation of interest in order to calculate an economic model parameter, such as a hydrocarbon distribution proximate the subterranean formation of interest.
A geoscience knowledge system can be obtained, where the geoscience knowledge system can include one or more of publicly available information, industry information, proprietary information, or task specific information. The geoscience knowledge system can be represented as a graph, graph data, network nodes, image data, tokenized data, or textualized data. Subsurface information can be obtained such as from seismic images or other types of sensor data. The subsurface information can be transformed or pre-processed, such as denoising, to make it suitable for use by the geoscience knowledge system. Then subsurface knowledge can be inferred from the subsurface information using the geoscience knowledge system. The subsurface knowledge can provided estimates, approximations, or value of the subterranean formation of interest in order to calculate an economic model parameter, such as a hydrocarbon distribution proximate the subterranean formation of interest.
A system can be used for optimizing a wellbore operation via a metaverse space that can include one or more avatars. The system can provide access to the metaverse space for an entity. The metaverse space can be a computer-generated representation of a location relating to a wellbore operation. The system can receive, via an avatar in the metaverse space, a query from the entity relating to the wellbore operation. The avatar can include software applications for performing tasks in the metaverse space. The system can execute, via the avatar, a request to a micro-service for at least one solution parameter based on the query. The request can cause the micro-service to generate the at least one solution parameter. The system can receive the at least one solution parameter from the micro-service. The system can output the at least one solution parameter for adjusting the wellbore operation.
Processes to receive user input parameters and system input parameters associated with a borehole undergoing active drilling operations to continually update drilling directions with wholistically applied optimizations to bring the actual borehole trajectory closer to the planned borehole trajectory. The processes can project ahead of the drilling assembly to determine the actual trajectory of the borehole and generate corrections to reduce the gap between the actual and planned trajectory paths. Various optimizations can be applied to the corrections to avoid overstressing systems or reducing the borehole productivity. Conflicts between optimizations can be resolved using a weighting or ranking system. More than one set of corrections can be determined and a user or a machine learning system can be used to select the one set of corrections to use as the results to be communicated and applied to the drilling operation plan or a borehole system, such as a geo-steering system.
E21B 44/00 - Systèmes de commande automatique spécialement adaptés aux opérations de forage, c. à d. systèmes à fonctionnement autonome ayant pour rôle d'exécuter ou de modifier une opération de forage sans l'intervention d'un opérateur humain, p.ex. systèmes de ; Systèmes spécialement adaptés à la surveillance de plusieurs variables ou conditions de forage
Processes to receive user input parameters and system input parameters associated with a borehole undergoing active drilling operations to continually update drilling directions with wholistically applied optimizations to bring the actual borehole trajectory closer to the planned borehole trajectory. The processes can project ahead of the drilling assembly to determine the actual trajectory of the borehole and generate corrections to reduce the gap between the actual and planned trajectory paths. Various optimizations can be applied to the corrections to avoid overstressing systems or reducing the borehole productivity. Conflicts between optimizations can be resolved using a weighting or ranking system. More than one set of corrections can be determined and a user or a machine learning system can be used to select the one set of corrections to use as the results to be communicated and applied to the drilling operation plan or a borehole system, such as a geo-steering system.
E21B 47/09 - Localisation ou détermination de la position d'objets dans les trous de forage ou dans les puits; Identification des parties libres ou bloquées des tubes
E21B 47/04 - Mesure de la profondeur ou du niveau du liquide
E21B 41/00 - Matériel ou accessoires non couverts par les groupes
E21B 44/00 - Systèmes de commande automatique spécialement adaptés aux opérations de forage, c. à d. systèmes à fonctionnement autonome ayant pour rôle d'exécuter ou de modifier une opération de forage sans l'intervention d'un opérateur humain, p.ex. systèmes de ; Systèmes spécialement adaptés à la surveillance de plusieurs variables ou conditions de forage
39.
LEARNING HYDROCARBON DISTRIBUTION FROM SEISMIC IMAGE
The disclosure relates to determining rock properties of subterranean formations and learning the distribution of hydrocarbons in the formations. A geometrical element spread function is disclosed that quantifies distortion of the geology as seen by the geophysicists who process seismic images of the subterranean formations. A method of determining the rock properties using the seismic images and synthetic images is provided. In one example, the method includes: (1) obtaining seismic data from a subterranean formation using a seismic acquisition system, (2) generating one or more seismic images of the subterranean formation using the seismic data, (3) creating one or more synthetic images from the one or more seismic images, and (4) determining rock properties of the subterranean formation based on the one or more seismic images and the one or more synthetic images.
E21B 47/12 - Moyens pour la transmission de signaux de mesure ou signaux de commande du puits vers la surface, ou de la surface vers le puits, p.ex. pour la diagraphie pendant le forage
40.
REAL-TIME DRILLING OPTIMIZATION IN A METAVERSE SPACE
A system can be used for optimizing a wellbore operation via a metaverse space that can include one or more avatars. The system can provide access to the metaverse space for an entity. The metaverse space can be a computer-generated representation of a location relating to a wellbore operation. The system can receive, via an avatar in the metaverse space, a query from the entity relating to the wellbore operation. The avatar can include software applications for performing tasks in the metaverse space. The system can execute, via the avatar, a request to a micro-service for at least one solution parameter based on the query. The request can cause the micro-service to generate the at least one solution parameter. The system can receive the at least one solution parameter from the micro-service. The system can output the at least one solution parameter for adjusting the wellbore operation.
G06Q 10/04 - Prévision ou optimisation spécialement adaptées à des fins administratives ou de gestion, p. ex. programmation linéaire ou "problème d’optimisation des stocks"
G06T 13/40 - Animation tridimensionnelle [3D] de personnages, p.ex. d’êtres humains, d’animaux ou d’êtres virtuels
G06T 19/00 - Transformation de modèles ou d'images tridimensionnels [3D] pour infographie
G06F 21/30 - Authentification, c. à d. détermination de l’identité ou de l’habilitation des responsables de la sécurité
E21B 41/00 - Matériel ou accessoires non couverts par les groupes
41.
TRIP MAP FOR ADJUSTING A TRIPPING OPERATION IN A WELLBORE
A system can generate a trip map for adjusting a tripping operation in a wellbore. The system can receive input data from a downhole tool in a wellbore. The system can determine parameters for the tripping operation. The system can determine an overall condition for an interval of the wellbore based on the parameters. The system can determine a status for the parameters and for the overall condition based on a difference between the parameters or the overall condition and a corresponding optimized value. The system can generate a trip map using the parameters and the overall condition. The trip map can include a background shape and a polygon that can be positioned on the background shape. The polygon can include corners corresponding to the parameters and overall condition that are positioned angularly around the background. The trip map can be output to adjust the tripping operation.
G01V 99/00 - Matière non prévue dans les autres groupes de la présente sous-classe
G01V 3/18 - Prospection ou détection électrique ou magnétique; Mesure des caractéristiques du champ magnétique de la terre, p.ex. de la déclinaison ou de la déviation spécialement adaptée au carottage
E21B 41/00 - Matériel ou accessoires non couverts par les groupes
42.
ANALYZING BOREHOLE PATHS USING STRATIGRAPHIC TURNING POINTS
The disclosure presents processes to determine turning points in stratigraphy (TPS) which can be used to improve the representation of the borehole path in relation to layers of the subterranean formation. The TPS can be determined by analyzing each directional survey point in relation to the nearest layer of the subterranean formation. In determining which layer is the nearest layer, the process can analyze the layer type, such as conformable or unconformable, whether a fault intersects the borehole, the angle of the layer in relation to the borehole path, or whether the true stratigraphic thickness (TST) changes from one of a positive parameter or negative parameter to the other. The generated TPS can be used by a system as input or can be displayed for a user where the segmented borehole path can be aligned using the calculated TST to improve the ability of the user to analyze the representation.
E21B 44/00 - Systèmes de commande automatique spécialement adaptés aux opérations de forage, c. à d. systèmes à fonctionnement autonome ayant pour rôle d'exécuter ou de modifier une opération de forage sans l'intervention d'un opérateur humain, p.ex. systèmes de ; Systèmes spécialement adaptés à la surveillance de plusieurs variables ou conditions de forage
E21B 47/04 - Mesure de la profondeur ou du niveau du liquide
43.
Trip map for adjusting a tripping operation in a wellbore
A system can generate a trip map for adjusting a tripping operation in a wellbore. The system can receive input data from a downhole tool in a wellbore. The system can determine parameters for the tripping operation. The system can determine an overall condition for an interval of the wellbore based on the parameters. The system can determine a status for the parameters and for the overall condition based on a difference between the parameters or the overall condition and a corresponding optimized value. The system can generate a trip map using the parameters and the overall condition. The trip map can include a background shape and a polygon that can be positioned on the background shape. The polygon can include corners corresponding to the parameters and overall condition that are positioned angularly around the background. The trip map can be output to adjust the tripping operation.
E21B 44/00 - Systèmes de commande automatique spécialement adaptés aux opérations de forage, c. à d. systèmes à fonctionnement autonome ayant pour rôle d'exécuter ou de modifier une opération de forage sans l'intervention d'un opérateur humain, p.ex. systèmes de ; Systèmes spécialement adaptés à la surveillance de plusieurs variables ou conditions de forage
44.
OPTIMIZING DRILLING PARAMETERS FOR CONTROLLING A WELLBORE DRILLING OPERATION
A system can receive input data indicating a current state of a wellbore drilling operation. The system can determine, by a set of software applications, constraints associated with the wellbore drilling operation. The system can optimize, by an optimization model and using the input data, a drilling parameter subject to the constraints associated with the wellbore drilling operation. The system can output the optimized drilling parameter for controlling the wellbore drilling operation.
E21B 49/00 - Test pour déterminer la nature des parois des trous de forage; Essais de couches; Procédés ou appareils pour prélever des échantillons du terrain ou de fluides en provenance des puits, spécialement adaptés au forage du sol ou aux puits
E21B 43/30 - Disposition particulière des puits, p.ex. disposition rendant optimum l'espacement des puits
E21B 47/12 - Moyens pour la transmission de signaux de mesure ou signaux de commande du puits vers la surface, ou de la surface vers le puits, p.ex. pour la diagraphie pendant le forage
E21B 44/00 - Systèmes de commande automatique spécialement adaptés aux opérations de forage, c. à d. systèmes à fonctionnement autonome ayant pour rôle d'exécuter ou de modifier une opération de forage sans l'intervention d'un opérateur humain, p.ex. systèmes de ; Systèmes spécialement adaptés à la surveillance de plusieurs variables ou conditions de forage
45.
DETERMINING CELL PROPERTIES FOR A GRID GENERATED FROM A GRID-LESS MODEL OF A RESERVOIR OF AN OILFIELD
A system can receive a grid-less point cloud model of a geological formation, the grid-less cloud point model that includes data points. The system can determine, by a machine-learning model for clustering data points, clusters for the data points according to a heterogeneity index. The system can determine an outline for each cluster. The system can generate a grid corresponding to the geological formation, the grid comprising a plurality of cells for each cluster of the plurality of clusters, each cluster having cell properties. The system can output the grid for the geological formation to a graphical user interface, the grid usable for executing a flow simulation at the graphical user interface.
E21B 47/12 - Moyens pour la transmission de signaux de mesure ou signaux de commande du puits vers la surface, ou de la surface vers le puits, p.ex. pour la diagraphie pendant le forage
46.
DETERMINING CELL PROPERTIES FOR A GRID GENERATED FROM A GRID-LESS MODEL OF A RESERVOIR OF AN OILFIELD
A system can receive a grid-less point cloud model of a geological formation, the grid-less cloud point model that includes data points. The system can determine, by a machine-learning model for clustering data points, clusters for the data points according to a heterogeneity index. The system can determine an outline for each cluster. The system can generate a grid corresponding to the geological formation, the grid comprising a plurality of cells for each cluster of the plurality of clusters, each cluster having cell properties. The system can output the grid for the geological formation to a graphical user interface, the grid usable for executing a flow simulation at the graphical user interface.
G06F 30/28 - Optimisation, vérification ou simulation de l’objet conçu utilisant la dynamique des fluides, p.ex. les équations de Navier-Stokes ou la dynamique des fluides numérique [DFN]
47.
OPTIMIZING DRILLING PARAMETERS FOR CONTROLLING A WELLBORE DRILLING OPERATION
A system can receive input data indicating a current state of a wellbore drilling operation. The system can determine, by a set of software applications, constraints associated with the wellbore drilling operation. The system can optimize, by an optimization model and using the input data, a drilling parameter subject to the constraints associated with the wellbore drilling operation. The system can output the optimized drilling parameter for controlling the wellbore drilling operation.
E21B 44/00 - Systèmes de commande automatique spécialement adaptés aux opérations de forage, c. à d. systèmes à fonctionnement autonome ayant pour rôle d'exécuter ou de modifier une opération de forage sans l'intervention d'un opérateur humain, p.ex. systèmes de ; Systèmes spécialement adaptés à la surveillance de plusieurs variables ou conditions de forage
48.
EMISSIONS ESTIMATIONS AT A HYDROCARBON OPERATION LOCATION USING A DATA-DRIVEN APPROACH
A system can collect a first set of equipment data and emissions data from a first hydrocarbon operation location. The system can train at least one machine-learning model to estimate an emission factor of at least one equipment component of the first hydrocarbon operation location using the first set of equipment data and the emissions data of the first hydrocarbon operation location. The system can then apply the at least one machine-learning model to a second set of equipment data to estimate total emissions over a predetermined amount of time at a second hydrocarbon operation location.
G06Q 50/26 - Services gouvernementaux ou services publics
G06Q 10/04 - Prévision ou optimisation spécialement adaptées à des fins administratives ou de gestion, p. ex. programmation linéaire ou "problème d’optimisation des stocks"
A system can collect a first set of equipment data and emissions data from a first hydrocarbon operation location. The system can train at least one machine-learning model to estimate an emission factor of at least one equipment component of the first hydrocarbon operation location using the first set of equipment data and the emissions data of the first hydrocarbon operation location. The system can then apply the at least one machine-learning model to a second set of equipment data to estimate total emissions over a predetermined amount of time at a second hydrocarbon operation location.
A method of predicting rock properties at a selectable scale is provided, including receiving coordinates of locations of respective sample points, receiving measurement data associated with measurements or measurement interpretations for each sample point, receiving for each sample point a scale that indicates the scale used to obtain the measurements and/or measurement interpretations, wherein different scales are received for different sample points. A deep neural network (DNN) is trained by applying the received coordinates, measurement data, and scale associated with each sample point and associating the sample point with a rock property as a function of the coordinates, measurement data, and scale applied for the sample point. The DNN is configured to generate rock property data for a received request point having coordinates and a selectable scale, wherein the rock property data is determined for the request point as a function of the coordinates and the selectable scale.
The disclosure presents processes to automatically generate one or more set of fault segments from a fault plane pointset. The processes can identify a predominant direction and derive a set of fault segments from the fault plane pointset, where the fault segments are generated by using slices of data from the fault plane pointset that are perpendicular to the predominant direction. For each slice of data, the fault segments can be analyzed with neighboring fault segments to determine if they are overlapping. Fault segments that block or overlap other fault segments can be assigned to a different subset of fault segments from the underlying fault segments. Gaps in the fault plane pointset, and the resulting set of fault segments, can be filled in by merging neighboring fault segments above and below the gap if the neighboring fault segments satisfy a criteria for filling the gap.
E21B 44/00 - Systèmes de commande automatique spécialement adaptés aux opérations de forage, c. à d. systèmes à fonctionnement autonome ayant pour rôle d'exécuter ou de modifier une opération de forage sans l'intervention d'un opérateur humain, p.ex. systèmes de ; Systèmes spécialement adaptés à la surveillance de plusieurs variables ou conditions de forage
E21B 49/00 - Test pour déterminer la nature des parois des trous de forage; Essais de couches; Procédés ou appareils pour prélever des échantillons du terrain ou de fluides en provenance des puits, spécialement adaptés au forage du sol ou aux puits
The disclosure presents processes to automatically generate one or more set of fault segments from a fault plane pointset. The processes can identify a predominant direction and derive a set of fault segments from the fault plane pointset, where the fault segments are generated by using slices of data from the fault plane pointset that are perpendicular to the predominant direction. For each slice of data, the fault segments can be analyzed with neighboring fault segments to determine if they are overlapping. Fault segments that block or overlap other fault segments can be assigned to a different subset of fault segments from the underlying fault segments. Gaps in the fault plane pointset, and the resulting set of fault segments, can be filled in by merging neighboring fault segments above and below the gap if the neighboring fault segments satisfy a criteria for filling the gap.
A system can receive a sustainability target for a level of assessment for a hydrocarbon operation. The system can receive actual data for an activity associated with the hydrocarbon operation. The system can generate a sustainability metric based on the actual data and one or more parameters of the activity. The system can generate, by at least one algorithm, a predicted sustainability state for the level of assessment at a subsequent point in time based on the sustainability metric, the actual data, and the one or more parameters of the activity. The system can generate a recommendation for at least one action based on the predicted sustainability state and the sustainability target for the hydrocarbon operation. The system can output the recommendation for the at least one action for adjusting the activity of the hydrocarbon operation.
The disclosure provides an automated process for determining the wear condition of a downhole tool that removes the subjectivity associated with manual observation. The automated process can advantageously evaluate a wear condition of a downhole tool using visual analytics and real-time analysis after the downhole tool has been extracted from the wellbore. An example of a method includes: (1) securing a downhole tool in a rig assembly, (2) obtaining, using sensors, surround tool data of the downhole tool in the rig assembly, wherein the surround tool data includes a first set of surround tool data obtained before a downhole operation by the downhole tool and a second set of surround tool data obtained after the downhole operation, and (3) automatically determining a wear condition of the downhole tool in real time by comparing the second set of surround tool data to the first set of surround tool data.
E21B 47/12 - Moyens pour la transmission de signaux de mesure ou signaux de commande du puits vers la surface, ou de la surface vers le puits, p.ex. pour la diagraphie pendant le forage
Frequency-dependent machine-learning (ML) models can be used to interpret seismic data. A system can apply spectral decomposition to pre-processed training data to generate frequency-dependent training data of two or more frequencies. The system can train two or more ML models using the frequency-dependent training data. Subsequent to training the two or more ML models, the system can apply the two or more ML models to seismic data to generate two or more subterranean feature probability maps. The system can perform an analysis of aleatoric uncertainty on the two or more subterranean feature probability maps to create an uncertainty map for aleatoric uncertainty. Additionally, the system can generate a filtered subterranean feature probability map based on the uncertainty map for aleatoric uncertainty.
Frequency-dependent machine-learning (ML) models can be used to interpret seismic data. A system can apply spectral decomposition to pre-processed training data to generate frequency-dependent training data of two or more frequencies. The system can train two or more ML models using the frequency-dependent training data. Subsequent to training the two or more ML models, the system can apply the two or more ML models to seismic data to generate two or more subterranean feature probability maps. The system can perform an analysis of aleatoric uncertainty on the two or more subterranean feature probability maps to create an uncertainty map for aleatoric uncertainty. Additionally, the system can generate a filtered subterranean feature probability map based on the uncertainty map for aleatoric uncertainty.
A system can determine a heterogeneity and a score for a reservoir for optimizing a drilling location. The system can receive a wireline log associated with a well that is positioned in a subterranean formation that includes a reservoir. The system can determine, using the wireline log, at least one statistical parameter for an interval of the well. The system can determine, using the at least one statistical parameter, a vertical heterogeneity of the reservoir. The system can determine, using the vertical heterogeneity, a score associated with the reservoir. The score can indicate an extraction difficulty and a carbon intensity of the reservoir. The system can output the score for optimizing a drilling location.
E21B 43/30 - Disposition particulière des puits, p.ex. disposition rendant optimum l'espacement des puits
E21B 47/12 - Moyens pour la transmission de signaux de mesure ou signaux de commande du puits vers la surface, ou de la surface vers le puits, p.ex. pour la diagraphie pendant le forage
E21B 47/26 - Stockage des données en fond de puits, p.ex. dans une mémoire ou sur un support d'enregistrement
E21B 41/00 - Matériel ou accessoires non couverts par les groupes
58.
Determining reservoir heterogeneity for optimized drilling location
A system can determine a heterogeneity and a score for a reservoir for optimizing a drilling location. The system can receive a wireline log associated with a well that is positioned in a subterranean formation that includes a reservoir. The system can determine, using the wireline log, at least one statistical parameter for an interval of the well. The system can determine, using the at least one statistical parameter, a vertical heterogeneity of the reservoir. The system can determine, using the vertical heterogeneity, a score associated with the reservoir. The score can indicate an extraction difficulty and a carbon intensity of the reservoir. The system can output the score for optimizing a drilling location.
E21B 43/16 - Procédés de récupération assistée pour l'extraction d'hydrocarbures
E21B 47/003 - Détermination des volumes du puits ou trou de forage
E21B 47/0224 - Détermination de l'inclinaison ou de la direction du trou de forage, p.ex. à l'aide de géomagnétisme utilisant des moyens sismiques ou acoustiques
E21B 49/00 - Test pour déterminer la nature des parois des trous de forage; Essais de couches; Procédés ou appareils pour prélever des échantillons du terrain ou de fluides en provenance des puits, spécialement adaptés au forage du sol ou aux puits
59.
MODELING A KARST FORMATION FOR A WELLBORE OPERATION
A system can model a karst formation for controlling a wellbore operation. The system can receive first input data that includes a set of fracture properties in a fracture network of a subterranean formation. The system can receive second input data that includes a set of point sets from a fracture geometry of the fracture network. The system can generate a set of fracture skeletons from the first input data and the second input data. The system can model a karst feature based on the plurality of fracture skeletons. The system can output the karst feature for controlling a wellbore operation.
A system can model a karst formation for controlling a wellbore operation. The system can receive first input data that includes a set of fracture properties in a fracture network of a subterranean formation. The system can receive second input data that includes a set of point sets from a fracture geometry of the fracture network. The system can generate a set of fracture skeletons from the first input data and the second input data. The system can model a karst feature based on the plurality of fracture skeletons. The system can output the karst feature for controlling a wellbore operation.
The disclosure addresses the existing gap in tubular designs and monitoring of tubulars in wellbores by considering high temperature, cyclic thermal loading effects. An example method of designing tubular for use in a well is provided that includes: (1) receiving a well configuration for a well and at least one type of well operation for the well, (2) receiving a selection of a tubular for use in the well, (3) generating a temperature history and a pressure history for the well using the well configuration, the selection of the tubular, the at least one type of well operation, and one or more simulators, and (4) determining, using the temperature history and the pressure history, a derated strength of the tubular based on one or more effects of high temperature, cyclic thermal loadings on the tubular.
G06F 30/18 - Conception de réseaux, p.ex. conception basée sur les aspects topologiques ou d’interconnexion des systèmes d’approvisionnement en eau, électricité ou gaz, de tuyauterie, de chauffage, ventilation et climatisation [CVC], ou de systèmes de câblage
The disclosure addresses the existing gap in tubular designs and monitoring of tubulars in wellbores by considering high temperature, cyclic thermal loading effects. An example method of designing tubular for use in a well is provided that includes: (1) receiving a well configuration for a well and at least one type of well operation for the well, (2) receiving a selection of a tubular for use in the well, (3) generating a temperature history and a pressure history for the well using the well configuration, the selection of the tubular, the at least one type of well operation, and one or more simulators, and (4) determining, using the temperature history and the pressure history, a derated strength of the tubular based on one or more effects of high temperature, cyclic thermal loadings on the tubular.
Some implementations relate to a method for parallelizing, by a geological data system, operations of a geostatistical simulation for a well data set via a plurality of processing elements (PEs). The method may include determining a reservoir area for the well data set. The method may include determining a set of turning band lines for the reservoir area. The method may include dividing the reservoir area into a plurality of tiles, each tile including a respective subset of the set of turning band lines. The method may include assigning at least one of the tiles to each of the PEs. The method may include determining, in parallel for each tile, intermediate results with respect to each respective subset of turning band lines. The method may include aggregating the intermediate results to form a final result of the geostatistical simulation.
Some implementations relate to a method for parallelizing, by a geological data system, operations of a geostatistical simulation for a well data set via a plurality of processing elements (PEs). The method may include determining a reservoir area for the well data set. The method may include determining a set of turning band lines for the reservoir area. The method may include dividing the reservoir area into a plurality of tiles, each tile including a respective subset of the set of turning band lines. The method may include assigning at least one of the tiles to each of the PEs. The method may include determining, in parallel for each tile, intermediate results with respect to each respective subset of turning band lines. The method may include aggregating the intermediate results to form a final result of the geostatistical simulation.
Systems and methods for machine learning (ML) assisted parameter matching are disclosed. Wellsite data is acquired for one or more existing production wells in a hydrocarbon producing field. The wellsite data is transformed into one or more model data sets for predictive modeling. A first ML model is trained to predict well logs for the existing production well(s), based on the model data set(s). A first well model is generated to estimate production of the existing production well(s) based on the predicted well logs. Parameters of the first well model are tuned based on a comparison between the estimated and an actual production of the existing production well(s). A second ML model is trained to predict parameters of a second well model for a new production well, based on the tuned parameters of the first well model. The new well's production is forecasted using the second ML model.
E21B 47/12 - Moyens pour la transmission de signaux de mesure ou signaux de commande du puits vers la surface, ou de la surface vers le puits, p.ex. pour la diagraphie pendant le forage
E21B 41/00 - Matériel ou accessoires non couverts par les groupes
G06N 20/20 - Techniques d’ensemble en apprentissage automatique
66.
MACHINE LEARNING ASSISTED COMPLETION DESIGN FOR NEW WELLS
Systems and methods for completion design are disclosed. Wellsite data is acquired for one or more existing production wells. The wellsite data is transformed into model data sets for training a first machine learning (ML) model to predict well logs. A first well model uses the well logs to estimate production of the existing well(s). Parameters of the first well model are tuned based on a comparison between the estimated and actual production of the existing well(s). A second ML model is trained to predict parameters of a second well model for a new well, based on the tuned parameters of the first well model. The new well's production is forecasted using the second ML model. Completion costs for the new well are estimated based on the well's completion design parameters and the forecasted production. Completion design parameters are adjusted, based on the estimated completion costs and the forecasted production.
G06F 30/27 - Optimisation, vérification ou simulation de l’objet conçu utilisant l’apprentissage automatique, p.ex. l’intelligence artificielle, les réseaux neuronaux, les machines à support de vecteur [MSV] ou l’apprentissage d’un modèle
G06F 30/13 - Conception architecturale, p.ex. conception architecturale assistée par ordinateur [CAAO] relative à la conception de bâtiments, de ponts, de paysages, d’usines ou de routes
E21B 47/00 - Relevés dans les trous de forage ou dans les puits
E21B 49/00 - Test pour déterminer la nature des parois des trous de forage; Essais de couches; Procédés ou appareils pour prélever des échantillons du terrain ou de fluides en provenance des puits, spécialement adaptés au forage du sol ou aux puits
67.
RECOMMENDATION ENGINE FOR AUTOMATED SEISMIC PROCESSING
System and methods for automated seismic processing are provided. Historical seismic project data associated with one or more historical seismic projects is obtained from a data store. The historical seismic project data is transformed into seismic workflow model data. At least one seismic workflow model is generated using the seismic workflow model data. Responsive to receiving seismic data for a new seismic project, an optimized workflow for processing the received seismic data is determined based on the at least one generated seismic workflow model. Geophysical parameters for processing the seismic data with the optimized workflow are selected. The seismic data for the new seismic project is processed using the optimized workflow and the selected geophysical parameters.
Systems and methods for completion design are disclosed. Wellsite data is acquired for one or more existing production wells. The wellsite data is transformed into model data sets for training a first machine learning (ML) model to predict well logs. A first well model uses the well logs to estimate production of the existing well(s). Parameters of the first well model are tuned based on a comparison between the estimated and actual production of the existing well(s). A second ML model is trained to predict parameters of a second well model for a new well, based on the tuned parameters of the first well model. The new well's production is forecasted using the second ML model. Completion costs for the new well are estimated based on the well's completion design parameters and the forecasted production. Completion design parameters are adjusted, based on the estimated completion costs and the forecasted production.
E21B 47/12 - Moyens pour la transmission de signaux de mesure ou signaux de commande du puits vers la surface, ou de la surface vers le puits, p.ex. pour la diagraphie pendant le forage
E21B 47/008 - Surveillance des systèmes de pompe de fond de trou, p.ex. pour la détection de conditions appelées "cognement sur le fluide"
E21B 43/16 - Procédés de récupération assistée pour l'extraction d'hydrocarbures
The disclosure presents processes to select cartographic reference system (CRS) recommendations from a CRS model where the CRS recommendations are matched to received seismic data. A learning mode can be used to build the CRS model where seismic data is matched to CRS. The learning mode can be automated using natural language processing system to parse the meta data for the seismic data, such as the name, area, or code, or label. The CRS model can be updated using an output from a user system, such as when a user manually matches a CRS to seismic data. The matched seismic data to CRS, e.g., seismic data-CRS match, can be used as input to a user system or a computing system, such as a borehole operation system.
E21B 49/00 - Test pour déterminer la nature des parois des trous de forage; Essais de couches; Procédés ou appareils pour prélever des échantillons du terrain ou de fluides en provenance des puits, spécialement adaptés au forage du sol ou aux puits
70.
DETERMINING PARAMETERS FOR A WELLBORE OPERATION BASED ON RESONANCE SPEEDS OF DRILLING EQUIPMENT
Drilling parameters for a wellbore operation can be determined based on resonance speeds. For example, a system can receive real-time data for a drilling operation that is concurrently occurring with receiving the real-time data. The system can determine, for a drilling depth, a rotations-per-minute (RPM) value corresponding to a resonance speed based on a weight-on-bit (WOB) value and the real-time data. The system can generate a plot of the WOB value and the RPM value corresponding to the resonance speed. The system can determine drilling parameters for the drilling operation based on the plot. The drilling parameters can exclude, for the WOB value, the RPM value corresponding to the resonance speed.
E21B 47/12 - Moyens pour la transmission de signaux de mesure ou signaux de commande du puits vers la surface, ou de la surface vers le puits, p.ex. pour la diagraphie pendant le forage
E21B 45/00 - Mesure du temps de forage ou de la vitesse de pénétration
71.
Recommendation engine for automated seismic processing
System and methods for automated seismic processing are provided. Historical seismic project data associated with one or more historical seismic projects is obtained from a data store. The historical seismic project data is transformed into seismic workflow model data. At least one seismic workflow model is generated using the seismic workflow model data. Responsive to receiving seismic data for a new seismic project, an optimized workflow for processing the received seismic data is determined based on the at least one generated seismic workflow model. Geophysical parameters for processing the seismic data with the optimized workflow are selected. The seismic data for the new seismic project is processed using the optimized workflow and the selected geophysical parameters.
Drilling parameters for a wellbore operation can be determined based on resonance speeds. For example, a system can receive real-time data for a drilling operation that is concurrently occurring with receiving the real-time data. The system can determine, for a drilling depth, a rotations-per-minute (RPM) value corresponding to a resonance speed based on a weight-on-bit (WOB) value and the real-time data. The system can generate a plot of the WOB value and the RPM value corresponding to the resonance speed. The system can determine drilling parameters for the drilling operation based on the plot. The drilling parameters can exclude, for the WOB value, the RPM value corresponding to the resonance speed.
E21B 47/04 - Mesure de la profondeur ou du niveau du liquide
E21B 47/26 - Stockage des données en fond de puits, p.ex. dans une mémoire ou sur un support d'enregistrement
E21B 44/04 - Commande automatique de l'avance de l'outil en réponse au couple fourni par le moyen d'entraînement
E21B 49/00 - Test pour déterminer la nature des parois des trous de forage; Essais de couches; Procédés ou appareils pour prélever des échantillons du terrain ou de fluides en provenance des puits, spécialement adaptés au forage du sol ou aux puits
The disclosure presents processes to select cartographic reference system (CRS) recommendations from a CRS model where the CRS recommendations are matched to received seismic data. A learning mode can be used to build the CRS model where seismic data is matched to CRS. The learning mode can be automated using natural language processing system to parse the meta data for the seismic data, such as the name, area, or code, or label. The CRS model can be updated using an output from a user system, such as when a user manually matches a CRS to seismic data. The matched seismic data to CRS, e.g., seismic data-CRS match, can be used as input to a user system or a computing system, such as a borehole operation system.
The disclosure presents processes for evaluating a borehole design against one or more identified risks. The processes can determine borehole design concepts for the borehole design. Each borehole design concept can have multiple risks assigned, which can be selected from a library of risks, a risk matrix or template, a risk model, or user entered risks. The risks can be scored using one or more statistics-based algorithms, such as a sum, an average, a mean, or other algorithms. The risks can be grouped by a risk level, forming a sub-risk score for each risk level for each borehole design concept. A final risk score can be generated using the sub-risk scores for the borehole design. More than one borehole design can be evaluated using a risk tolerance parameter and the borehole design that satisfies the risk tolerance parameter can be selected as the recommended borehole design.
E21B 41/00 - Matériel ou accessoires non couverts par les groupes
G06F 30/27 - Optimisation, vérification ou simulation de l’objet conçu utilisant l’apprentissage automatique, p.ex. l’intelligence artificielle, les réseaux neuronaux, les machines à support de vecteur [MSV] ou l’apprentissage d’un modèle
E21B 47/12 - Moyens pour la transmission de signaux de mesure ou signaux de commande du puits vers la surface, ou de la surface vers le puits, p.ex. pour la diagraphie pendant le forage
75.
MACHINE LEARNING ASSISTED PARAMETER MATCHING AND PRODUCTION FORECASTING FOR NEW WELLS
Systems and methods for machine learning (ML) assisted parameter matching are disclosed. Wellsite data is acquired for one or more existing production wells in a hydrocarbon producing field. The wellsite data is transformed into one or more model data sets for predictive modeling. A first ML model is trained to predict well logs for the existing production well(s), based on the model data set(s). A first well model is generated to estimate production of the existing production well(s) based on the predicted well logs. Parameters of the first well model are tuned based on a comparison between the estimated and an actual production of the existing production well(s). A second ML model is trained to predict parameters of a second well model for a new production well, based on the tuned parameters of the first well model. The new well’s production is forecasted using the second ML model.
E21B 49/08 - Prélèvement d'échantillons de fluides ou test des fluides dans les trous de forage ou dans les puits
E21B 47/12 - Moyens pour la transmission de signaux de mesure ou signaux de commande du puits vers la surface, ou de la surface vers le puits, p.ex. pour la diagraphie pendant le forage
76.
SCORING A FINAL RISK FOR IDENTIFIED BOREHOLE DESIGN CONCEPTS
The disclosure presents processes for evaluating a borehole design against one or more identified risks. The processes can determine borehole design concepts for the borehole design. Each borehole design concept can have multiple risks assigned, which can be selected from a library of risks, a risk matrix or template, a risk model, or user entered risks. The risks can be scored using one or more statistics-based algorithms, such as a sum, an average, a mean, or other algorithms. The risks can be grouped by a risk level, forming a sub-risk score for each risk level for each borehole design concept. A final risk score can be generated using the sub-risk scores for the borehole design. More than one borehole design can be evaluated using a risk tolerance parameter and the borehole design that satisfies the risk tolerance parameter can be selected as the recommended borehole design.
E21B 43/30 - Disposition particulière des puits, p.ex. disposition rendant optimum l'espacement des puits
E21B 47/12 - Moyens pour la transmission de signaux de mesure ou signaux de commande du puits vers la surface, ou de la surface vers le puits, p.ex. pour la diagraphie pendant le forage
E21B 41/00 - Matériel ou accessoires non couverts par les groupes
A method of managing oilfield activity with a control system is provided having a plurality of virtual sensors and integrating the virtual sensors into a virtual sensor network. The method includes determining interdependencies among the virtual sensors, obtaining operational information from the virtual sensors, and providing virtual sensor output to the control system based on the determined interdependencies and the operational information.
E21B 44/00 - Systèmes de commande automatique spécialement adaptés aux opérations de forage, c. à d. systèmes à fonctionnement autonome ayant pour rôle d'exécuter ou de modifier une opération de forage sans l'intervention d'un opérateur humain, p.ex. systèmes de ; Systèmes spécialement adaptés à la surveillance de plusieurs variables ou conditions de forage
H04L 67/125 - Protocoles spécialement adaptés aux environnements propriétaires ou de mise en réseau pour un usage spécial, p.ex. les réseaux médicaux, les réseaux de capteurs, les réseaux dans les véhicules ou les réseaux de mesure à distance en impliquant la commande des applications des terminaux par un réseau
G05B 13/04 - Systèmes de commande adaptatifs, c. à d. systèmes se réglant eux-mêmes automatiquement pour obtenir un rendement optimal suivant un critère prédéterminé électriques impliquant l'usage de modèles ou de simulateurs
E21B 47/12 - Moyens pour la transmission de signaux de mesure ou signaux de commande du puits vers la surface, ou de la surface vers le puits, p.ex. pour la diagraphie pendant le forage
G05B 13/02 - Systèmes de commande adaptatifs, c. à d. systèmes se réglant eux-mêmes automatiquement pour obtenir un rendement optimal suivant un critère prédéterminé électriques
System and methods of random noise attenuation are provided. A first model may be trained to extract random noise from seismic datasets. A second model may be trained to reconstruct leaked signals from the random noise extracted by the first model. A seismic dataset corresponding to a subsurface reservoir formation and including random noise may be obtained. Using the trained first model, at least a portion of the random noise may be extracted from the first seismic dataset. Using the trained second model, a leaked signal, which includes a portion of the seismic dataset, may be reconstructed from the extracted random noise. A cleaned seismic dataset is generated based on the reconstructed leaked signal and the extracted random noise. The cleaned seismic dataset may include a quantity of random noise that is less than that of the original seismic dataset.
G01V 1/36 - Exécution de corrections statiques ou dynamiques sur des enregistrements, p.ex. correction de l'étalement; Etablissement d'une corrélation entre signaux sismiques; Elimination des effets produits par un excès d'énergie
A system can be provided for applying a dynamic filter to a velocity model for converting the domain of seismic data. The system can receive a velocity model for a geological area of interest. The system can apply a dynamic filter to the velocity model for smoothing an anomaly included in the velocity model. The system can apply the velocity model with the smoothed anomaly to seismic data associated with the geological area of interest for converting the domain of the seismic data.
G01V 1/36 - Exécution de corrections statiques ou dynamiques sur des enregistrements, p.ex. correction de l'étalement; Etablissement d'une corrélation entre signaux sismiques; Elimination des effets produits par un excès d'énergie
E21B 47/26 - Stockage des données en fond de puits, p.ex. dans une mémoire ou sur un support d'enregistrement
System and methods of random noise attenuation are provided. A first model may be trained to extract random noise from seismic datasets. A second model may be trained to reconstruct leaked signals from the random noise extracted by the first model. A seismic dataset corresponding to a subsurface reservoir formation and including random noise may be obtained. Using the trained first model, at least a portion of the random noise may be extracted from the first seismic dataset. Using the trained second model, a leaked signal, which includes a portion of the seismic dataset, may be reconstructed from the extracted random noise. A cleaned seismic dataset is generated based on the reconstructed leaked signal and the extracted random noise. The cleaned seismic dataset may include a quantity of random noise that is less than that of the original seismic dataset.
G01V 1/36 - Exécution de corrections statiques ou dynamiques sur des enregistrements, p.ex. correction de l'étalement; Etablissement d'une corrélation entre signaux sismiques; Elimination des effets produits par un excès d'énergie
G01V 1/28 - Traitement des données sismiques, p.ex. pour analyse, pour interprétation, pour correction
81.
Dynamic filter for smoothing velocity model for domain-converting seismic data
A system can be provided for applying a dynamic filter to a velocity model for converting the domain of seismic data. The system can receive a velocity model for a geological area of interest. The system can apply a dynamic filter to the velocity model for smoothing an anomaly included in the velocity model. The system can apply the velocity model with the smoothed anomaly to seismic data associated with the geological area of interest for converting the domain of the seismic data.
Hydrocarbon exploration and extraction can be facilitated by determining fault surfaces from fault attribute volumes. For example, a system described herein can receive a fault attribute volume for faults in a subterranean formation determined using seismic data. The fault attribute volume may include multiple traces with trace locations. The system can determine a set of fault samples for each trace location. Each fault sample can include fault attributes such as a depth value, an amplitude value, and a vertical thickness value. The system can determine additional fault attributes such as a dip value and an azimuth value for each fault sample of each trace location. The system can determine fault surfaces for the faults using the fault samples and fault attributes. The system can then output the fault surfaces for use in a hydrocarbon extraction operation.
G01V 1/36 - Exécution de corrections statiques ou dynamiques sur des enregistrements, p.ex. correction de l'étalement; Etablissement d'une corrélation entre signaux sismiques; Elimination des effets produits par un excès d'énergie
G01V 1/40 - Séismologie; Prospection ou détection sismique ou acoustique spécialement adaptées au carottage
83.
Determining fault surfaces from fault attribute volumes
Hydrocarbon exploration and extraction can be facilitated by determining fault surfaces from fault attribute volumes. For example, a system described herein can receive a fault attribute volume for faults in a subterranean formation determined using seismic data. The fault attribute volume may include multiple traces with trace locations. The system can determine a set of fault samples for each trace location. Each fault sample can include fault attributes such as a depth value, an amplitude value, and a vertical thickness value. The system can determine additional fault attributes such as a dip value and an azimuth value for each fault sample of each trace location. The system can determine fault surfaces for the faults using the fault samples and fault attributes. The system can then output the fault surfaces for use in a hydrocarbon extraction operation.
A method includes retrieving an unstructured document and defining an area of interest of the unstructured document that visually represents geological formation information. The method also includes extracting a set of vectorized polygons from the area of interest. Additionally, the method includes assigning properties from the unstructured document to each of the vectorized polygons in the set of vectorized polygons. Further, the method includes assigning a coordinate reference frame to the set of vectorized polygons and generating a user-interactive document from the set of vectorized polygons.
G06V 10/25 - Détermination d’une région d’intérêt [ROI] ou d’un volume d’intérêt [VOI]
G06V 10/44 - Extraction de caractéristiques locales par analyse des parties du motif, p.ex. par détection d’arêtes, de contours, de boucles, d’angles, de barres ou d’intersections; Analyse de connectivité, p.ex. de composantes connectées
A system can provide for determining characteristics loss in a wellbore. The system can include a processor and a non-transitory memory with instructions that are executable by the processor for causing the processor to execute operations. The operations can include receiving, from sensors in a wellbore, data corresponding to loss indicators. The operations can include determining a loss probability for each loss indicator. The operations can include determining a total loss probability of fluid loss in the wellbore based on the loss probabilities. The operations can include outputting the total loss probability to be used in a drilling operation in the wellbore.
E21B 21/08 - Commande ou surveillance de la pression ou de l'écoulement du fluide de forage, p.ex. remplissage automatique des trous de forage, commande automatique de la pression au fond
E21B 21/00 - Procédés ou appareils pour nettoyer les trous de forage par jet de fluide, p.ex. en utilisant l'air d'échappement du moteur
E21B 47/117 - Détection de fuites, p.ex. du tubage, par test de pression
E21B 43/26 - Procédés pour activer la production par formation de crevasses ou de fractures
86.
Active reinforcement learning for drilling optimization and automation
Systems and methods for automated drilling control and optimization are disclosed. Training data, including values of drilling parameters, for a current stage of a drilling operation are acquired. A reinforcement learning model is trained to estimate values of the drilling parameters for a subsequent stage of the drilling operation to be performed, based on the acquired training data and a reward policy mapping inputs and outputs of the model. The subsequent stage of the drilling operation is performed based on the values of the drilling parameters estimated using the trained model. A difference between the estimated and actual values of the drilling parameters is calculated, based on real-time data acquired during the subsequent stage of the drilling operation. The reinforcement learning model is retrained to refine the reward policy, based on the calculated difference. At least one additional stage of the drilling operation is performed using the retrained model.
E21B 44/00 - Systèmes de commande automatique spécialement adaptés aux opérations de forage, c. à d. systèmes à fonctionnement autonome ayant pour rôle d'exécuter ou de modifier une opération de forage sans l'intervention d'un opérateur humain, p.ex. systèmes de ; Systèmes spécialement adaptés à la surveillance de plusieurs variables ou conditions de forage
A system can provide for determining characteristics loss in a wellbore. The system can include a processor and a non-transitory memory with instructions that are executable by the processor for causing the processor to execute operations. The operations can include receiving, from sensors in a wellbore, data corresponding to loss indicators. The operations can include determining a loss probability for each loss indicator. The operations can include determining a total loss probability of fluid loss in the wellbore based on the loss probabilities. The operations can include outputting the total loss probability to be used in a drilling operation in the wellbore.
E21B 47/10 - Localisation des fuites, intrusions ou mouvements du fluide
E21B 44/00 - Systèmes de commande automatique spécialement adaptés aux opérations de forage, c. à d. systèmes à fonctionnement autonome ayant pour rôle d'exécuter ou de modifier une opération de forage sans l'intervention d'un opérateur humain, p.ex. systèmes de ; Systèmes spécialement adaptés à la surveillance de plusieurs variables ou conditions de forage
E21B 43/26 - Procédés pour activer la production par formation de crevasses ou de fractures
E21B 47/06 - Mesure de la température ou de la pression
88.
COMBINED SOFT AND STIFF-STRING TORQUE AND DRAG MODEL
Aspects of the disclosed technology provide techniques for determining frictional forces bearing on a downhole drill string. In some implementations, a method of the disclosed technology can include steps for segmenting a plurality of continuous nodes of the drilling string into a first segment and a second segment, computing a first set of values corresponding with one or more nodes in the first segment using a first model, computing a second set of values corresponding with one or more nodes in the second segment using a second model, and determining a torque of the drill string based on the first set of values and the second set of values. In some aspects, the method can further include steps for determining a drag force on the drill string based on the first set of values and the second set of values. Systems and machine-readable media are also provided.
Aspects and features of this disclosure relate to projecting physical drilling parameters to control a drilling operation. A computing system applies Bayesian optimization to a model incorporating the input data using varying values for an adverse drilling factor to produce a target function. The computing system determines a minimum value for the target function. The computing system provides a projected value for the physical drilling parameters based on the minimum value. The computing system generates an alert responsive to determining that the projected value for the physical drilling parameters exceeds a prescribed limit.
E21B 44/00 - Systèmes de commande automatique spécialement adaptés aux opérations de forage, c. à d. systèmes à fonctionnement autonome ayant pour rôle d'exécuter ou de modifier une opération de forage sans l'intervention d'un opérateur humain, p.ex. systèmes de ; Systèmes spécialement adaptés à la surveillance de plusieurs variables ou conditions de forage
E21B 47/007 - Mesure des contraintes dans le cuvelage ou la tige de forage
G06F 30/20 - Optimisation, vérification ou simulation de l’objet conçu
90.
Diffusion flux inclusion for a reservoir simulation for hydrocarbon recovery
A method includes selecting a model for a simulation of hydrocarbon recovery from a reservoir having a plurality of fractures during injection of an injected gas into the plurality of fractures. Selecting the model includes determining a flux ratio of a convection rate to a diffusion rate for the reservoir, determining whether the flux ratio is less than a threshold, and in response to the flux ratio being less than the threshold, selecting the model that includes diffusion. Selecting the model includes performing the simulation of the hydrocarbon recovery from the reservoir based on the model.
G06F 30/20 - Optimisation, vérification ou simulation de l’objet conçu
E21B 49/00 - Test pour déterminer la nature des parois des trous de forage; Essais de couches; Procédés ou appareils pour prélever des échantillons du terrain ou de fluides en provenance des puits, spécialement adaptés au forage du sol ou aux puits
G06F 9/455 - Dispositions pour exécuter des programmes spécifiques Émulation; Interprétation; Simulation de logiciel, p.ex. virtualisation ou émulation des moteurs d’exécution d’applications ou de systèmes d’exploitation
E21B 43/16 - Procédés de récupération assistée pour l'extraction d'hydrocarbures
E21B 41/00 - Matériel ou accessoires non couverts par les groupes
A least one seismic attribute is determined for each voxel of the seismic volume. A first horizon is selected for mapping and a sparse global grid is generated which includes the horizon, at least one constraint point identifying the horizon, and a number of points having a depth in the seismic volume. A value of at least one seismic attribute is determined for each point and their depths are adjusted based on the value of the seismic attribute. A map of the horizon can be generated based on the adjusted depths. Multiple local grids can be generated based on the sparse global grid, and the depths of the local grid points adjusted to generate a map of the horizon at voxel level resolution. The seismic volume can be mapped into multiple horizons, where previously mapped horizons can function as constraints on the sparse global grid.
G01V 1/36 - Exécution de corrections statiques ou dynamiques sur des enregistrements, p.ex. correction de l'étalement; Etablissement d'une corrélation entre signaux sismiques; Elimination des effets produits par un excès d'énergie
92.
Formation evaluation based on seismic horizon mapping with multi-scale optimization
A least one seismic attribute is determined for each voxel of the seismic volume. A first horizon is selected for mapping and a sparse global grid is generated which includes the horizon, at least one constraint point identifying the horizon, and a number of points having a depth in the seismic volume. A value of at least one seismic attribute is determined for each point and their depths are adjusted based on the value of the seismic attribute. A map of the horizon can be generated based on the adjusted depths. Multiple local grids can be generated based on the sparse global grid, and the depths of the local grid points adjusted to generate a map of the horizon at voxel level resolution. The seismic volume can be mapped into multiple horizons, where previously mapped horizons can function as constraints on the sparse global grid.
The disclosure presents processes to improve the ability to analyze geological information for an area or region of interest. A user can specify one or more input files, such as from public, private, or proprietary sources. The user can specify a geological or geographic framework to utilize. The process can then perform a matching between the data in the input files and the data in the framework. The matching process can utilize a geological matching using a specified range of depths or a geographical matching followed by the geological matching. Other parameters can be utilized such as a radius to define an area of interest around a central location of interest. Matched data elements can have geological attributes from the geological framework data linked to data elements in the input files. The input files can be downloaded, displayed, printed, or communicated to another computing system or program for further analysis
E21B 49/00 - Test pour déterminer la nature des parois des trous de forage; Essais de couches; Procédés ou appareils pour prélever des échantillons du terrain ou de fluides en provenance des puits, spécialement adaptés au forage du sol ou aux puits
E21B 41/00 - Matériel ou accessoires non couverts par les groupes
The disclosure presents processes to improve the ability to analyze geological information for an area or region of interest. A user can specify one or more input files, such as from public, private, or proprietary sources. The user can specify a geological or geographic framework to utilize. The process can then perform a matching between the data in the input files and the data in the framework. The matching process can utilize a geological matching using a specified range of depths or a geographical matching followed by the geological matching. Other parameters can be utilized such as a radius to define an area of interest around a central location of interest. Matched data elements can have geological attributes from the geological framework data linked to data elements in the input files. The input files can be downloaded, displayed, printed, or communicated to another computing system or program for further analysis.
A location of a cut and an amount of force to be used in a pull operation for a plug & abandonment (P&A) operation can be determined. Measurements of at least one characteristic of fluids and solids disposed in an annulus defined between a casing and a wall of a wellbore can be received. A total fluid and solid friction force drag can be determined using hydrostatic force that is determined from the measurements. A mechanical friction force drag can be determined based on a weight of the casing. The mechanical friction force drag and the total fluid and solid friction force drag can be used to determine a friction factor. The friction factor can be used to determine a depth location at which to cut the casing and a pull force for pulling the casing from the wellbore in the P&A operation.
E21B 47/12 - Moyens pour la transmission de signaux de mesure ou signaux de commande du puits vers la surface, ou de la surface vers le puits, p.ex. pour la diagraphie pendant le forage
E21B 47/04 - Mesure de la profondeur ou du niveau du liquide
E21B 44/04 - Commande automatique de l'avance de l'outil en réponse au couple fourni par le moyen d'entraînement
96.
MACHINE LEARNING MODEL SELECTION BASED ON FEATURE MERGING FOR A SPATIAL LOCATION ACROSS MULTIPLE TIME WINDOWS
A method comprises receiving a current dataset for a current time window from at least one sensor in a wellbore created in a subsurface formation, wherein the current dataset comprises values of a number of current features of the subsurface formation at a spatial location in the wellbore. The method includes selecting at least one previous time window from a number of previous time windows that includes a previously cached dataset that was detected by the at least one sensor or a different sensor in the wellbore and that spatially overlaps with the spatial location for the current dataset. The method includes merging the current dataset with the previously cached dataset to create a merged dataset. The method includes selecting a machine learning model from a plurality of machine learning models for the spatial location in the wellbore based on the merged dataset.
G06N 20/20 - Techniques d’ensemble en apprentissage automatique
E21B 47/12 - Moyens pour la transmission de signaux de mesure ou signaux de commande du puits vers la surface, ou de la surface vers le puits, p.ex. pour la diagraphie pendant le forage
97.
MACHINE LEARNING MODEL SELECTION BASED ON FEATURE MERGING FOR A SPATIAL LOCATION ACROSS MULTIPLE TIME WINDOWS
A method comprises receiving a current dataset for a current time window from at least one sensor in a wellbore created in a subsurface formation, wherein the current dataset comprises values of a number of current features of the subsurface formation at a spatial location in the wellbore. The method includes selecting at least one previous time window from a number of previous time windows that includes a previously cached dataset that was detected by the at least one sensor or a different sensor in the wellbore and that spatially overlaps with the spatial location for the current dataset. The method includes merging the current dataset with the previously cached dataset to create a merged dataset. The method includes selecting a machine learning model from a plurality of machine learning models for the spatial location in the wellbore based on the merged dataset.
A location of a cut and an amount of force to be used in a pull operation for a plug & abandonment (P&A) operation can be determined. Measurements of at least one characteristic of fluids and solids disposed in an annulus defined between a casing and a wall of a wellbore can be received. A total fluid and solid friction force drag can be determined using hydrostatic force that is determined from the measurements. A mechanical friction force drag can be determined based on a weight of the casing. The mechanical friction force drag and the total fluid and solid friction force drag can be used to determine a friction factor. The friction factor can be used to determine a depth location at which to cut the casing and a pull force for pulling the casing from the wellbore in the P&A operation.
E21B 29/00 - Découpage ou destruction de tubes, packers, bouchons ou câbles, situés dans les trous de forage ou dans les puits, p.ex. découpage de tubes endommagés, de fenêtres; Déformation des tubes dans les trous de forage; Remise en état des tubages de puits sans les retirer du sol
E21B 47/09 - Localisation ou détermination de la position d'objets dans les trous de forage ou dans les puits; Identification des parties libres ou bloquées des tubes
E21B 33/14 - Procédés ou dispositifs de cimentation, de bouchage des trous, des fissures ou analogues pour la cimentation des tubes dans les trous de forage ou de sondage
A method comprises determining a value of at least one oppositional force for a drillstring at multiple depths in the wellbore, determining a value of a drag force for the drillstring at the multiple depths, determining a value of hook load for the drillstring at the multiple depths based on the value of the at least one opposition force and the value of the drag force at the multiple depths, and determining a calibrated drillstring weight based on a change in the value of the hook load over the multiple depths. From the calibrated drillstring weight, an adjusted estimated hook load can be determined. The drag force can be calculated based on a drag per centralizer and the number of centralizers in the wellbore. A centralizer friction factor can be determined and used to calibrate the value of the drag per centralizer.
A method includes acquiring historical well construction data associated with a set of historical wells. The method also includes developing a well construction model using the corpus of historical well construction data. Additionally, the method includes acquiring real-time well construction data during a well construction operation and applying the well construction model to the real-time well construction data to identify changes to a well construction parameter. Further, the method includes outputting a command to update the well construction operation using the changes to the well construction parameter.
G06Q 10/0639 - Analyse des performances des employés; Analyse des performances des opérations d’une entreprise ou d’une organisation
E21B 44/00 - Systèmes de commande automatique spécialement adaptés aux opérations de forage, c. à d. systèmes à fonctionnement autonome ayant pour rôle d'exécuter ou de modifier une opération de forage sans l'intervention d'un opérateur humain, p.ex. systèmes de ; Systèmes spécialement adaptés à la surveillance de plusieurs variables ou conditions de forage
