公开(公告)号：US20230375735A1

公开(公告)日：2023-11-23

申请号：US18027266

申请日：2021-09-13

Applicant: EXXONMOBIL TECHNOLOGY AND ENGINEERING COMPANY

Inventor： Kuang-Hung Liu ,  Huseyin Denli ,  Mary Johns ,  Jacquelyn Daves

IPC: G01V1/30 ,  G01V1/28

CPC classification number: G01V1/301 ,  G01V1/282

Abstract: A computer-implemented method for detecting geological elements or fluid in a subsurface from seismic images is disclosed. Seismic data may be analyzed to identify one or both of fluid or geologic elements in the subsurface. As one example, the analysis may include unsupervised learning, such as variational machine learning, in order to learn relationships between different sets of seismic data. For example, variational machine learning may be used to learn relationships among partially-stack images or among pre-stack images in order to detect hydrocarbon presence. In this way, an unsupervised learning framework may be used for learning a Direct Hydrocarbon Indicator (DHI) from seismic images by learning relationships among partially-stack or pre-stack images.

公开(公告)号：US11669063B2

公开(公告)日：2023-06-06

申请号：US16662460

申请日：2019-10-24

Applicant: ExxonMobil Technology and Engineering Company

Inventor： Stijn De Waele ,  Myun-Seok Cheon ,  Kuang-Hung Liu ,  Shivakumar Kameswaran ,  Francisco Trespalacios ,  Dimitri J. Papageorgiou

IPC: G05B17/02 ,  G06F30/20 ,  G06F17/18 ,  G06N3/08 ,  G06F18/214

CPC classification number: G05B17/02 ,  G06F17/18 ,  G06F18/214 ,  G06F30/20 ,  G06N3/08

Abstract: Aspects of the technology described herein comprise a surrogate model for a chemical production process. A surrogate model is a machine learned model that uses a collection of inputs and outputs from a simulation of the chemical production process and/or actual production data as training data. Once trained, the surrogate model can estimate an output of a chemical production process given an input to the process. Surrogate models are not directly constrained by physical conditions in a plant. This can cause them to suggest optimized outputs that the not possible to produce in the real world. It is a significant challenge to train a surrogate model to only produce outputs that are possible. The technology described herein improves upon previous surrogate models by constraining the output of the surrogate model to outputs that are possible in the real world.