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.