Invention Grant
- Patent Title: Machine-learning based fracture-hit detection using low-frequency DAS signal
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Application No.: US16815378Application Date: 2020-03-11
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Publication No.: US11768307B2Publication Date: 2023-09-26
- Inventor: Ge Jin , Kevin Mendoza , Baishali Roy , Darryl G. Buswell
- Applicant: ConocoPhillips Company
- Applicant Address: US TX Houston
- Assignee: ConocoPhillips Company
- Current Assignee: ConocoPhillips Company
- Current Assignee Address: US TX Houston
- Agency: Polsinelli PC
- Main IPC: G01V1/50
- IPC: G01V1/50 ; E21B47/14 ; G06N3/084 ; G06F17/18 ; G02B6/44 ; G06N20/00

Abstract:
Various aspects described herein relate to a machine learning based detecting of fracture hits in offset monitoring wells when designing hydraulic fracturing processes for a particular well. In one example, a computer-implemented method includes receiving a set of features for a first well proximate to a second well, the second well undergoing a hydraulic fracturing process for extraction of natural resources from underground formations; inputting the set of features into a trained neural network; and providing, as output of the trained neural network, a probability of a fracture hit at a location associated with the set of features in the first well during a given completion stage of the hydraulic fracturing process in the second well.
Public/Granted literature
- US20200309982A1 MACHINE-LEARNING BASED FRACTURE-HIT DETECTION USING LOW-FREQUENCY DAS SIGNAL Public/Granted day:2020-10-01
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