Title: Subsurface stress criticality associated with fluid injection and determined using machine learning

Abstract

Machine-learning methods and apparatus are disclosed to determine critical state or other parameters related to fluid-driven failure of a terrestrial locale impacted by anthropogenic activities such as hydraulic fracturing, hydrocarbon extraction, wastewater disposal, or geothermal harvesting. Acoustic emission, seismic waves, or other detectable indicators of microscopic processes are sensed. A classifier is trained using time series of microscopic data along with corresponding data of critical state or failure events. In disclosed examples, random forests and artificial neural networks are used, and grid-search or EGO procedures are used for hyperparameter tuning. Once trained, the classifier can be applied to live data from a fluid injection locale in order to assess a frictional state, assess seismic hazard, assess permeability, make predictions regarding a future fluid-driven failure event, or drive engineering solutions for mitigation or remediation. Variations are disclosed.

Inventors:

Johnson, Paul Allan; Hulbert, Claudia L.; Rouet-Leduc, Bertrand

Issue Date:

2022-05-24

Research Org.:

Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Org.:

USDOE National Nuclear Security Administration (NNSA)

OSTI Identifier:

1892967

Patent Number(s):

11341410

Application Number:

16/706,166

Assignee:

Triad National Security, LLC (Los Alamos, NM)

Patent Classifications (CPCs):

G - PHYSICS G06 - COMPUTING G06N - COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS

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G - PHYSICS G06 - COMPUTING G06N - COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS

G06N20/10 - using kernel methods, e.g. support vector machines [SVM]
G06N20/20 - Ensemble learning
G06N3/08 - Learning methods
G06N5/02 - Knowledge representation

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DOE Contract Number:  

89233218CNA000001

Resource Type:

Patent

Resource Relation:

Patent File Date: 12/06/2019

Country of Publication:

United States

Language:

English