US 2025/0027289 A1Published application

Self-learning framework for predictive topographic modeling and intelligent real-time system control of subterranean slurry injection system

OwnerCole Brayton Johnson, Laurence E. Allen, III +1inventors

InventorsCole Brayton Johnson, Laurence E. Allen, III, Laurence E. Allen, IV

Life of this patent

Estimating the term…

Loading patent…

Abstract

The invention presents a system for geospatial topographical modeling and real-time system control to be used with subterranean slurry injection technology. The system consists of a predictive modeling subsystem, which processes land characteristics to generate optimal site plans for drilling and injection. The real-time control subsystem implements these plans, dynamically adjusting slurry compositions and injection parameters based on sensor feedback. A feedback loop between the subsystems allows continuous refinement of both predictive models and operational controls, enhancing the precision with which the technology shapes the ground and the efficiency with which the technology sequesters carbon.

Claims12

Dependent claims

A method for altering topology of terrain, incorporating a method for predicting the topological deformation of a land surface due to subterranean slurry injection incorporating:

a. Geospatial mesh computation using data extracted from one or more of the following mechanisms:
i. Land Survey
ii. Electrical Resistivity Tomography
iii. Ground Penetrating Radar
iv. Seismic Refraction
v. Reflection Surveys
vi. Electromagnetic Surveys
vii. Gravimetric Surveys
viii. Geotechnical Drilling
ix. Soil Sampling
x. Geocore Sampling
xi. Topsoil Testing
xii. Vegetation Analysis
xiii. Property Valuations
b. Geospatial mesh computation, utilizing one or more of the following methodologies with weighted contribution coefficients from the inputs in a):
i. Interpolation techniques
ii. Learning techniques
c. Topological deformation computation, utilizing one or more of the following methodologies:
i. CFD/FEA time step simulations
ii. Reinforcement learning-based approaches
iii. Supervised learning-based approaches
iv. Rule-based approximations
v. Geometric simplification approaches—aperture symmetry assumptions,
d. The aforementioned methods constitute in part or full a method which output one or more of the following:
i. An actionable set of guidelines for the execution of surface topology alteration through subterranean slurry injection, which may be augmented by surface grading, extraction, or fill
ii. Predicted resulting geospatial meshes
iii. Predicted flood statistics
iv. Flatness coefficients
v. Water pooling hotspot predictions
vi. Updated seismic data
vii. Predicted sequestration-related data
e. The aforementioned methods are refined through collected data pertaining to one or more of the following:
i. Pre-specified guidelines
ii. Guidelines concurrently modified or generated alongside injection procedures
iii. Sensor data associated with geospatial mesh states.

32 more elements

Dependencies are inferred from the claim text.

Facts

Application: 18/629,026Filed 8 Apr 2024
Priority: 15 Feb 2022
Published: 23 Jan 2025
Term ends: Not estimatedThe source records don’t support an estimate.

Classification7 codes

E02D 3/12
Improving or preserving soil or rock, e.g. preserving permafrost soil: consolidating by placing solidifying or pore-filling substances in the soil
G01N 33/24
Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00: earth materials
G05B 13/0265
Electric: the criterion being a learning criterion
G05B 13/04
Electric: involving the use of models or simulators

Family

Application17/672,553Continuation in part · filed 15 Feb 2022
Application18/629,026Continuation in part
US 11,952,735 B2Related patent
US 2022/0169917 A1Published application
US 2025/0027289 A1This patent

From the US filing history. Foreign family members aren’t listed.