Invention Grant
- Patent Title: Progressive modeling of optical sensor data transformation neural networks for downhole fluid analysis
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Application No.: US16598802Application Date: 2019-10-10
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Publication No.: US11604982B2Publication Date: 2023-03-14
- Inventor: Dingding Chen , Christopher Michael Jones , Bin Dai , Anthony Van Zuilekom
- Applicant: HALLIBURTON ENERGY SERVICES, INC.
- Applicant Address: US TX Houston
- Assignee: HALLIBURTON ENERGY SERVICES, INC.
- Current Assignee: HALLIBURTON ENERGY SERVICES, INC.
- Current Assignee Address: US TX Houston
- Agency: Novak Druce Carroll LLP
- Main IPC: G06N3/045
- IPC: G06N3/045 ; G06N3/08

Abstract:
Disclosed herein are examples embodiments of a progressive modeling scheme to enhance optical sensor transformation networks using both in-field sensor measurements and simulation data. In one aspect, a method includes receiving optical sensor measurements generated by one or more downhole optical sensors in a wellbore; determining synthetic data for fluid characterization using an adaptive model and the optical sensor measurements; and applying the synthetic data to determine one or more physical properties of a fluid in the wellbore for which the optical sensor measurements are received.
Public/Granted literature
- US20210110246A1 PROGRESSIVE MODELING OF OPTICAL SENSOR DATA TRANSFORMATION NEURAL NETWORKS FOR DOWNHOLE FLUID ANALYSIS Public/Granted day:2021-04-15
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