Peng L Ray

age ~61

from Spring, TX

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Apparatus And Method For Testing Solids Production In A Wellbore
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US Patent:

20120255727, Oct 11, 2012
Filed:

Apr 11, 2011
Appl. No.:

13/084028
Inventors:

John Alasdair MacDonald Cameron - The Woodlands TX, US
Peng L. Ray - Spring TX, US
Assignee:

Chevron U.S.A. Inc. - San Ramon CA
International Classification:

E21B 47/00
E21B 27/00
US Classification:

16625001, 166107
Abstract:

An apparatus and method is disclosed for conducting a fluid and solids production test in a subterranean well. A testing apparatus may be inserted into a wellbore. The apparatus may employ a tubular housing with entry port(s) through the housing. An inner assembly may be positioned within the tubular housing, the inner assembly being configured for connection to the tubular housing. The inner assembly may include a fluid permeable screen and a distal end forming a reservoir that is scaled, or may be adapted to be scaled, for the collection of solids during a well production testing event. Following a testing event, the apparatus may be removed from the wellbore to enable the collected solids to be measured to determine the amount of solids generated by a subterranean formation at actual field flow conditions.

Uncertainty-Aware Modeling And Decision Making For Geomechanics Workflow Using Machine Learning Approaches
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US Patent:

20210382198, Dec 9, 2021
Filed:

Jun 3, 2020
Appl. No.:

16/892050
Inventors:

- San Ramon CA, US
Peng L. Ray - Spring TX, US
International Classification:

G01V 99/00
G06F 30/20
G06F 30/17
G06N 7/00
Abstract:

A Gaussian process is used to provide a nonparametric approach for modeling nonlinear relationships among physical quantities involved in the geomechanics workflow supporting drilling & completion operations. Gaussian process provides a nonparametric framework that enables injection of a prior belief into the basic model format while allowing its specific format to be adaptive in a certain range following an estimated distribution. Both this model-related uncertainty and the pre-assumed input data distributions may be calibrated using non-parametric Bayesian framework with Gaussian process as prior. This approach not only the addresses the uncertainty stemming from the input physical properties but also tackles the uncertainties underlying the adopted physical model, all in this nonparametric Bayesian framework with Gaussian process encoded as prior.