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公开(公告)号：US20240361490A1
公开(公告)日：2024-10-31
申请号：US18247505
申请日：2022-03-23
发明人： Yupeng Li , Maolin Luo , Shouxiang Ma , Peng Lu , Christopher Ayadiuno
CPC分类号： G01V11/002 , G06F30/28
摘要： A method for obtaining geological heterogeneity trends of a geological formation, including the steps: drilling wells (w1, w2, w3, w4, w5) that penetrate the formation, acquiring well logs (log 1, log 2) for each well (w1, w2, w3, w4, w5) as function of depth inter Vals (Dk) of the respective well, determining a third degree tensor (Tk, m, n), where a z-dimension denotes the depths, a x-dimension denotes the well logs, and a y-dimension denotes the wells, extracting matrices (L1k,m, L2k,m, . . . , LMk,n) from the tensor (Tk, m, n), clustering the matrices (L1k,n, L2k,n, . . . , LMk,n) based on the characteristics of the corresponding well logs (log 1, log 2) to a clustering result matrix, aggregating the clustering result matrix to a cluster ensemble (π1, π2, . . . , πM), and spatial partitioning the cluster ensemble (π1, π2, . . . , πM) to a map that shows the geological heterogeneity trends associated with cluster types of the wells (w1, w2, w3, w4, w5).
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公开(公告)号：US20210278935A1
公开(公告)日：2021-09-09
申请号：US16810115
申请日：2020-03-05
IPC分类号： G06F3/0482 , G06N3/08 , G06N20/00 , G06F30/27 , G01V99/00
摘要： Systems and methods include a method for providing, for presentation to a user, a graphical user interface (GUI) for defining and generating machine learning-based proxy models as surrogates for process-based reactive transport modeling (RTM). User selections of training parameters for generating training sample data are received. The training sample data is generated in response to receiving a parameter files generation indication. A training cases generation indication is received. Training sample cases are executed using the training sample data. User selections of proxy models training parameters are received. A set of parameter-specific proxy models represented by a neural network are trained. Each parameter-specific proxy model corresponds to a specific RTM parameter from a set of RTM parameters. Blind tests are performed using the set of parameter-specific proxy models. Each blind test tests a specific one of the parameter-specific proxy models. Predictions are generated using the set of parameter-specific proxy models.
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公开(公告)号：USD930025S1
公开(公告)日：2021-09-07
申请号：US29727691
申请日：2020-03-12
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公开(公告)号：US11961002B2
公开(公告)日：2024-04-16
申请号：US16810124
申请日：2020-03-05
CPC分类号： G16C20/70 , G01V99/005 , G06N3/045 , G16C20/30
摘要： Systems and methods include a computer-implemented method for random selection and use of observation cells. Observation cells are randomly selected from a model of process-based reactive transport modeling (RTM). The observation cells are incorporated into a neural network for proxy modeling. A set of parameter-specific proxy models represented by a neural network is trained. Each parameter-specific proxy model corresponds to a specific RTM parameter from a set of RTM parameters. Blind tests are performed using the set of parameter-specific proxy models, where each blind test tests a specific one of the parameter-specific proxy models. Predictions are generated using the set of parameter-specific proxy models. 3-dimensional interpolation the observation cells is performed.
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公开(公告)号：US20210279592A1
公开(公告)日：2021-09-09
申请号：US16810106
申请日：2020-03-05
摘要： Systems and methods include a method for training machine learning-based proxy models as surrogates for process-based reactive transport modeling (RTM). Training sample data is generated. Training sample cases are executed using the training sample data. A set of parameter-specific proxy models represented by a neural network is trained. Each parameter-specific proxy model corresponds to a specific RTM parameter from a set of RTM parameters. Blind tests are performed using the set of parameter-specific proxy models. Each blind test tests a specific one of the parameter-specific proxy models. Predictions are generated using the set of parameter-specific proxy models.
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公开(公告)号：US11561674B2
公开(公告)日：2023-01-24
申请号：US16810115
申请日：2020-03-05
IPC分类号： G06N20/00 , G06F3/0482 , G06F30/27 , G01V99/00 , G06N3/08
摘要： Systems and methods include a method for providing, for presentation to a user, a graphical user interface (GUI) for defining and generating machine learning-based proxy models as surrogates for process-based reactive transport modeling (RTM). User selections of training parameters for generating training sample data are received. The training sample data is generated in response to receiving a parameter files generation indication. A training cases generation indication is received. Training sample cases are executed using the training sample data. User selections of proxy models training parameters are received. A set of parameter-specific proxy models represented by a neural network are trained. Each parameter-specific proxy model corresponds to a specific RTM parameter from a set of RTM parameters. Blind tests are performed using the set of parameter-specific proxy models. Each blind test tests a specific one of the parameter-specific proxy models. Predictions are generated using the set of parameter-specific proxy models.
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公开(公告)号：US20210279593A1
公开(公告)日：2021-09-09
申请号：US16810124
申请日：2020-03-05
摘要： Systems and methods include a computer-implemented method for random selection and use of observation cells. Observation cells are randomly selected from a model of process-based reactive transport modeling (RTM). The observation cells are incorporated into a neural network for proxy modeling. A set of parameter-specific proxy models represented by a neural network is trained. Each parameter-specific proxy model corresponds to a specific RTM parameter from a set of RTM parameters. Blind tests are performed using the set of parameter-specific proxy models, where each blind test tests a specific one of the parameter-specific proxy models. Predictions are generated using the set of parameter-specific proxy models. 3-dimensional interpolation the observation cells is performed.
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公开(公告)号：US20200308934A1
公开(公告)日：2020-10-01
申请号：US16365217
申请日：2019-03-26
摘要： The subject matter of this specification can be embodied in, among other things, a method for geological modeling includes receiving a forward depositional model, determining a Latin Hypercube Sampling (LHS) stratigraphic model based on the projected forward depositional model, performing forward depositional modeling, transform the forward depositional model from time domain to stratigraphic-depth domain, determining one or more pseudo-wells based on the transformed model, determining a mismatch value based on the transformed forward depositional model and a collection of simulated physical value, and determining a kriging surrogate model based on the LHS stratigraphic model and the mismatch value.
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