Pedro Marg Castillo

US Patent:

20210301644, Sep 30, 2021

Filed:

Mar 26, 2021

Appl. No.:

17/214160

Inventors:

- Bedford MA, US
Pedro Alejandro Castillo Castillo - Houston TX, US
Hong Zhao - Sugar Land TX, US
Mir Khan - Houston TX, US
Magiel J. Harmse - Houston TX, US

International Classification:

E21B 47/00
G06N 20/00
G06N 5/04

Abstract:

A method and system for estimating wax or hydrate deposits is desirable for the oil industry and important for assuring flow conditions and production, avoiding downtime, and reducing or preventing costly interventions. The method and system disclosed herein use artificial intelligence and machine learning techniques combined with oil well historical operational sensor data and historical operational event records (such as diesel hot flush, slick line, coil tubing, etc.) to build an oil well model. The method and system enable oil well practitioners to test and validate the built model and deploy the model online to estimate and/or detect wax or hydrate deposition status. By using one or more such models in operating an oil well, users can monitor and/or detect the status of wax of hydrate deposits in an oil well and can optimize production, maintenance, and planning for oil wells.

US Patent:

20190188584, Jun 20, 2019

Filed:

Dec 18, 2018

Appl. No.:

16/223763

Inventors:

- Bedford MA, US
Hong Zhao - Sugar Land TX, US
Pedro Alejandro Castillo Castillo - Houston TX, US
Mark-John Bruwer - Richmond TX, US
Mir Khan - Houston TX, US
Alexander B. Bates - San Diego CA, US

International Classification:

G06N 5/04
G06F 16/215
G06N 20/00

Abstract:

A system that provides an improved approach for detecting and predicting failures in a plant or equipment process. The approach may facilitate failure-model building and deployment from historical plant data of a formidable number of measurements. The system implements methods that generate a dataset containing recorded measurements for variables of the process. The methods reduce the dataset by cleansing bad quality data segments and measurements for uninformative process variables from the dataset. The methods then enrich the dataset by applying nonlinear transforms, engineering calculations and statistical measurements. The methods identify highly correlated input by performing a cross-correlation analysis on the cleansed and enriched dataset, and reduce the dataset by removing less-contributing input using a two-step feature selection procedure. The methods use the reduced dataset to build and train a failure model, which is deployed online to detect and predict failures in real-time plant operations.

Address:

5850 Gulfton St APT 1859, Houston, TX 77081

VIN:

1GNFC13C47R334199

Make:

CHEVROLET

Model:

TAHOE C1500

Year:

2007

Address:

7707 Glover St APT 1, Houston, TX 77012

VIN:

2G1WB55K679213443

Make:

CHEVROLET

Model:

IMPALA

Year:

2007