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

IPC: E21B41/00 ,  G01V20/00 ,  G06N5/04 ,  G06N20/00

CPC classification number: G06N5/04 ,  E21B41/00 ,  G01V20/00 ,  G06N20/00

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.

公开(公告)号：US20210365808A1

公开(公告)日：2021-11-25

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

IPC: G06N5/04 ,  G06N20/00 ,  E21B41/00

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.