公开(公告)号：US20230176242A1

公开(公告)日：2023-06-08

申请号：US17922739

申请日：2021-04-19

Applicant: ExxonMobil Upstream Research Company

Inventor： Peng Xu ,  Huseyin DENLI ,  Stijn De Waele ,  Mary K. Johns

IPC: G01V1/30 ,  G01V3/38 ,  G01V99/00 ,  G06N3/044 ,  G06N3/08 ,  G06N3/045

CPC classification number: G01V1/302 ,  G01V3/38 ,  G01V99/005 ,  G06N3/044 ,  G06N3/08 ,  G06N3/045 ,  G01V2210/64

Abstract: A computer-implemented method for analyzing geophysical data is disclosed. Interpretation of geophysical data, such as seismic data, can be performed in multiple stages, such as at an information extraction stage and an information analysis stage. Typically, the information analysis stage is performed by geologists or interpreters, which may be laborious and inconsistent. The disclosed method includes using an information extractor that extracts information indicative of geo-features in a subsurface and an inference engine that analyzes the information indicative of geo-features in a subsurface to generate an output, with the information extractor and the inference engine being integrated and acting in combination. For example, the information extractor may generate summaries of the geo-features or answers to questions. In this way, the information extractor and the inference engine in combination may act synergistically, such as in the context of reasoning, natural language processing, and the outputs generated.

公开(公告)号：US20200183035A1

公开(公告)日：2020-06-11

申请号：US16685657

申请日：2019-11-15

Applicant: ExxonMobil Upstream Research Company

Inventor： Wei D. LIU ,  Huseyin DENLI ,  Kuang-Hung LIU ,  Cody J. MACDONALD

IPC: G01V1/32 ,  G06N20/00 ,  G01V1/30 ,  G01V1/34 ,  G01V1/36

Abstract: A method and apparatus for machine learning for use with automated seismic interpretation include: obtaining input data; extracting patches from a pre-extraction dataset based on the input data; transforming data of a pre-transformation dataset based on the input data and geologic domain knowledge and/or geophysical domain knowledge; and generating augmented data from the extracted patches and the transformed data. A method and apparatus for machine learning for use with automated seismic interpretation include: a data input module configured to obtain input data; a patch extraction module configured to extract patches from a pre-extraction dataset that is based on the input data; a data transformation module configured to transform data from a pre-transformation dataset that is based on the input data and geologic domain knowledge and/or geophysical domain knowledge; and a data augmentation module configured to augment data from the extracted patches and the transformed data.

公开(公告)号：US20230168410A1

公开(公告)日：2023-06-01

申请号：US17922838

申请日：2021-04-19

Applicant: ExxonMobil Upstream Research Company

Inventor： Stijin De Waele ,  Huseyin DENLI ,  Peng Xu ,  Mary K. Johns

IPC: G01V99/00 ,  G06F30/27

CPC classification number: G01V99/005 ,  G06F30/27

Abstract: A method and apparatus for performing geological reasoning, A method includes: obtaining subsurface data for a subsurface region; obtaining a knowledge model; extracting a structured representation from the subsurface data using the knowledge model; and performing geological reasoning with a graph network based on the knowledge model and the structured representation. A method includes performing geological reasoning with a knowledge model that includes a set of geoscience rules or a geoscience ontology. A method includes performing geological reasoning with a structured representation that includes a graph. A method includes performing geological reasoning by one or more of the following: question answering; decision making; assigning ranking; and assessing probability.

公开(公告)号：US20200183047A1

公开(公告)日：2020-06-11

申请号：US16685700

申请日：2019-11-15

Applicant: ExxonMobil Upstream Research Company

Inventor： Huseyin DENLI ,  Cody J. MACDONALD ,  Victoria M. SOM DE CERFF

IPC: G01V99/00 ,  G01V1/50 ,  G06N3/08 ,  G06N3/04 ,  G06N20/20 ,  G06F17/18

Abstract: A method for generating one or more reservoir models using machine learning is provided. Generating reservoir models is typically a time-intensive idiosyncratic process. However, machine learning may be used to generate one or more reservoir models that characterize the subsurface. The machine learning may use geological data, geological concepts, reservoir stratigraphic configurations, and one or more input geological models in order to generate the one or more reservoir models. As one example, a generative adversarial network (GAN) may be used as the machine learning methodology. The GAN includes two neural networks, including a generative network (which generates candidate reservoir models) and a discriminative network (which evaluates the candidate reservoir models), contest with each other in order to generate the reservoir models.