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11.发明申请公开(公告)号：US20220019933A1
公开(公告)日：2022-01-20
申请号：US16929191
申请日：2020-07-15
Applicant: Landmark Graphics Corporation
Inventor： Zainab Diana Titus , Annabel Causer , Graham Baines , Christine Yallup , Olutobi Adeyemi , Marc Paul Servais
Abstract: A heat flow modeler preprocesses geological and heat flow data for an earth formation for inputting into a plurality of supervised learning models. The heat flow modeler trains the plurality of supervised learning models on the preprocessed geological data to estimate heat flow throughout the earth formation. The heat flow modeler interpolates the estimated heat flow values to a set of desired locations in the earth formation and cosimulates the preprocessed heat flow values with the interpolated heat flow values as auxiliary variables to generate a cosimulated heat flow map. A final heat flow map is generated by rasterizing the cosimulated heat flow map.
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公开(公告)号：US12105237B2
公开(公告)日：2024-10-01
申请号：US17537143
申请日：2021-11-29
Applicant: Landmark Graphics Corporation
Inventor： Graham Baines , Yikuo Liu , Daniel Possee
Abstract: In contrast to existing methods wherein derived horizons are interpreted in isolation, the disclosure provides a process that does not interpret patches themselves but determines the relationships between patches, in order to associate and link patches to derive a holistic geological interpretation. Predefined patches, such as from a pre-interpreted suite, are received as inputs to determine the relationships and derive an interpretation for a complete volume. In one aspect the disclosure provides an automated method of generating a geological age model for a subterranean area. In one example, the automated method includes: (1) abstracting seismic data of a subsurface into a limited number of patches, (2) abstracting the patches by defining patch-links between the patches, and (3) generating a geological age model of the subsurface by solving for the relative geological age of each of the patches using the patch-links.
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公开(公告)号：US20240111067A1
公开(公告)日：2024-04-04
申请号：US17951250
申请日：2022-09-23
Applicant: Landmark Graphics Corporation
Inventor： Daniel James Possee , Graham Baines
CPC classification number: G01V1/345 , E21B49/00 , G01V1/30 , G01V2210/642
Abstract: 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.
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公开(公告)号：US20220391716A1
公开(公告)日：2022-12-08
申请号：US17775460
申请日：2020-01-23
Applicant: Landmark Graphics Corporation
Inventor： Andrew Davies , Graham Baines , Alejandro Alberto Jaramillo , Yikuo Liu , Olutobi Adeyemi
Abstract: A lithology prediction that uses a geological age model as an input to a machine learning model. The geological age model is capable of separating and recoding different seismic packages derived from the horizon interpretation. Once the machine learning model has been trained, a validation may be performed to determine the quality of the machine learning model. The quality may be improved by refining the training of the machine learning model. The lithology prediction generated by the machine learning model that utilizes the geological age model provides an improved lithology prediction that more accurately reflects the subterranean formation of an area of interest.
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15.发明申请公开(公告)号：US20220004919A1
公开(公告)日：2022-01-06
申请号：US16963313
申请日：2020-02-20
Applicant: Landmark Graphics Corporation
Inventor： Jiazuo Zhang , Graham Baines
Abstract: According to some aspects, machine-learning models can be executed to classify a subsurface rock. Examples include training numerous machine-learning models using training data sets with different probability distributions, and then selecting a model to execute on a test data set. The selection of the model may be based on the similarity of each data point of the test data set and the probability distribution of each training class. Examples include detecting and recommending a pre-trained model to generate outputs predicting a classification, such as a lithology, of a test data set. Recommending the trained model may be based on calculated prior probabilities that measure the similarity between the training and test data sets. The model with a training data set that is most similar to the test data set can be recommended for classifying a physical property of the subsurface rock for hydrocarbon formation.
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Patent Agency Ranking
公开(公告)号：US20210011192A1
公开(公告)日：2021-01-14
申请号：US16083604
申请日：2018-03-19
Applicant: Landmark Graphics Corporation
Inventor： Benjamin Stephen Saunders , Graeme Richard Nicoll , Graham Baines
Abstract: Analysis and display of source-to-sink information according to some aspects includes grouping target geochronological data and reference geochronological data into distinct population groups representing a reference population and target populations and characterizing subpopulations within the reference population and the target populations according a statistical attribute or statistical attributes. Subpopulations are compared within the reference population and the target populations based on the statistical attribute or attributes to determine correlations between the reference population and the target populations, and the results can be displayed in many different ways. As one example, results can be displayed using a present day geographic map as well as using a geodynamic plate tectonic model to show data points and their paleogeographic locations for the relevant geological time frame of investigation.
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公开(公告)号：US20200174158A1
公开(公告)日：2020-06-04
申请号：US16787564
申请日：2020-02-11
Applicant: Landmark Graphics Corporation
Inventor： Graham Baines , Benjamin S. Saunders , Graeme Richard Nicoll , Jean-Christophe Wrobel-Daveau
Abstract: The disclosed embodiments include systems and methods to assess geological data. The method includes obtaining data associated with a geological state of a geological entity. The method also includes assessing a nature of a geological age constraint of the geological entity. The method further includes generating a first probability distribution of a geological age of the geological entity based on the nature of the geological age constraint of the geological entity. The method further includes selecting a time of interest for analysis of the geological entity. The method further includes assessing a nature of the geological age constraint during the time of interest. The method further includes generating a second probability distribution for the time of interest. The method further includes determining a likelihood that the geological age constraint of the geological entity coincides with the time of interest.
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公开(公告)号：US20250052918A1
公开(公告)日：2025-02-13
申请号：US18233054
申请日：2023-08-11
Applicant: Landmark Graphics Corporation
Inventor： Satyan Singh , Konstantin Osypov , Graham Baines
Abstract: A method for creating a generated signal which includes training a signal generator using a physics informed constraint, providing, to the signal generator, raw data that includes a signal and noise, where the signal generator processes the raw data to create the generated signal, and obtaining the generated signal from the signal generator.
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公开(公告)号：US12118477B2
公开(公告)日：2024-10-15
申请号：US16881166
申请日：2020-05-22
Applicant: Landmark Graphics Corporation
Inventor： Andrew Davies , Elizabeth Atar , Masoud Ghaderi Zefreh , Graham Baines , Benjamin Greselle
Abstract: A system includes a processor and a memory. The memory includes instructions that are executable by the processor to access training data of a modern feature of interest from direct observations, remotely determined data, or a combination thereof. The instructions are also executable to compile parameter data from at least one model simulation that impacts the modern feature of interest. The instructions are executable to train a machine-learning model to generate a predictive model that matches the training data of the modern feature of interest using the compiled parameter data as input. Furthermore, the instructions are executable to predict a feature of interest in a past time period using the predictive model and at least one historical model simulation that impacts the feature of interest. Additionally, the instructions are executable to execute a processing operation for facilitating hydrocarbon exploration based on the predicted feature of interest from the predictive model.
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公开(公告)号：US11630226B2
公开(公告)日：2023-04-18
申请号：US17447604
申请日：2021-09-14
Applicant: Landmark Graphics Corporation
Inventor： Daniel James Possee , Yikuo Liu , Graham Baines
Abstract: 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.
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