公开(公告)号：US20210097390A1

公开(公告)日：2021-04-01

申请号：US17039403

申请日：2020-09-30

Applicant: Chevron U.S.A. Inc.

Inventor： Baosheng LIANG ,  Chaoshun HU ,  Min LI

IPC: G06N3/08 ,  G01V99/00 ,  G06N3/04

Abstract: A method is described for predicting permeability including receiving a 3-D earth model including a volume of interest; generating 2-D property images; receiving 2-D fracture images; training a physics-guided neural network using the 2-D fracture images; and predicting permeability using the physics-guided neural network applied to the 2-D property images. The method is executed by a computer system.

公开(公告)号：US20160291177A1

公开(公告)日：2016-10-06

申请号：US15084627

申请日：2016-03-30

Applicant: CHEVRON U.S.A. INC.

Inventor： Uwe Karsten ALBERTIN ,  Chaoshun HU

IPC: G01V1/28 ,  G01V1/30

CPC classification number: G01V1/282 ,  G01V1/303 ,  G01V2210/6222 ,  G01V2210/679

Abstract: A method is described for improving subsalt seismic imaging using wavefield redatuming. The method redatums the source wavefield and receiver wavefield to the salt surface. It cross-correlates the redatumed wavefields and uses the time cross-correlations to update the salt surface. The method may be executed by a computer system.

Abstract translation: 描述了一种使用波场重绘来改进亚波斯地震成像的方法。 该方法将源波场和接收器波场重写为盐面。 它将重绘波场交叉相关，并使用时间互相关来更新盐表面。 该方法可以由计算机系统执行。

公开(公告)号：US20220164657A1

公开(公告)日：2022-05-26

申请号：US17534076

申请日：2021-11-23

Applicant: Chevron U.S.A. Inc.

Inventor： Jincong HE ,  Xian-Huan WEN ,  Meng TANG ,  Chaoshun HU ,  Shusei TANAKA ,  Kainan WANG

IPC: G06N3/08 ,  G06N3/04

Abstract: Embodiments of generating a field development plan for a hydrocarbon field development are provided herein. One embodiment comprises generating a plurality of training reservoir models of varying values of input channels of a reservoir template; normalizing the varying values of the input channels to generate normalized values of the input channels; constructing a policy neural network and a value neural network that project a state represented by the normalized values of the input channels to a field development action and a value of the state respectively; and training the policy neural network and the value neural network using deep reinforcement learning on the plurality of training reservoir models with a reservoir simulator as an environment such that the policy neural network generates a field development plan. A field development plan may be generated for a target reservoir on the reservoir template using the trained policy network and the reservoir simulator.