公开(公告)号：US20210089905A1

公开(公告)日：2021-03-25

申请号：US17027321

申请日：2020-09-21

Applicant: ConocoPhillips Company

Inventor： Christopher S. Olsen ,  Douglas Hakkarinen ,  Christopher R. Zaremba ,  Everett Robinson ,  Morgan Cowee ,  R. James Provost

IPC: G06N3/08 ,  G06N3/04

Abstract: Various aspects described herein relate to a system that utilized deep learning and neural networks to estimate/predict an amount of natural resource production in a well given a set of parameters indicative of physical changes to the well. In one aspect, a virtual flow meter includes memory having computer-readable instructions stored therein and one or more processors configured to execute the computer-readable instructions to receive one or more input parameters indicative of physical changes to at least one well; apply the one or more input parameters to a trained neural network architecture; and determine one or more outputs of the trained neural network architecture, the one or more outputs corresponding to predicted fluid output of the at least one well.

公开(公告)号：US12086709B2

公开(公告)日：2024-09-10

申请号：US17027321

申请日：2020-09-21

Applicant: ConocoPhillips Company

Inventor： Christopher S. Olsen ,  Douglas Hakkarinen ,  Christopher R. Zaremba ,  Everett Robinson ,  Morgan Cowee ,  R. James Provost

IPC: G06N3/08 ,  G06N3/04

CPC classification number: G06N3/08 ,  G06N3/04

Abstract: Various aspects described herein relate to a system that utilized deep learning and neural networks to estimate/predict an amount of natural resource production in a well given a set of parameters indicative of physical changes to the well. In one aspect, a virtual flow meter includes memory having computer-readable instructions stored therein and one or more processors configured to execute the computer-readable instructions to receive one or more input parameters indicative of physical changes to at least one well; apply the one or more input parameters to a trained neural network architecture; and determine one or more outputs of the trained neural network architecture, the one or more outputs corresponding to predicted fluid output of the at least one well.

公开(公告)号：US20220404515A1

公开(公告)日：2022-12-22

申请号：US17842304

申请日：2022-06-16

Applicant: ConocoPhillips Company

Inventor： Christopher S. Olsen ,  Douglas Hakkarinen ,  Michal Brhlik ,  Upendra K. Tiwari ,  Timothy D. Osborne ,  Nickolas Paladino ,  Mark A. Wardrop ,  David W. Glover ,  Brock Johnson ,  Peter Bormann ,  Charles Ildstad

IPC: G01V1/28 ,  G01V1/30 ,  G06N3/08 ,  G01V1/36

Abstract: Implementations described and claimed herein provide systems and methods for reservoir modeling. In one implementation, an input dataset comprising seismic data is received for a particular subsurface reservoir. Based on the input dataset and utilizing a deep learning computing technique, a plurality of trained reservoir models may be generated based on training data and/or validation information to model the particular subsurface reservoir. From the plurality of trained reservoir models, an optimized reservoir model may be selected based on a comparison of each of the plurality of reservoir models to a dataset of measured subsurface characteristics.