Neurosynaptic Computing for Ambiguous Oilfield Decision Support

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Solution Overview

Problem

Traditional von Neumann computers are inefficient in adapting to complex, unpredictable situations and unable to learn or handle ambiguous data, limiting their application in the oil and gas industry.

Innovation Solution

Cognitive computers with neurosynaptic systems modeled after the mammalian brain, utilizing extensive networks of electronic neurons and synapses that learn through experiences, enabling adaptability and intelligent decision-making in oilfield operations.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Power

If traditional von Neumann computer architecture is used, then calculations and deterministic instructions can be performed efficiently, but adaptability to new and unfamiliar situations deteriorates

Engineering Contradiction:

Improvecalculation efficiencyVSAvoidadaptability to new situations

Core Design Contradiction:

PowerVSAdaptability or versatility

Solution Approach 1:

The patent replaces the traditional von Neumann mechanical computing architecture with a biomimetic neural network system that emulates biological brain functions. This substitution enables the system to process information through distributed neural connections rather than sequential instruction execution, fundamentally changing how computational tasks are performed to achieve both efficiency and adaptability

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent transforms the computational model from deterministic binary logic to probabilistic neural activation patterns. By changing the fundamental parameters of computation from fixed 0/1 states to continuous activation levels that can learn and adapt, the system achieves versatility while maintaining computational power through parallel processing

Inventive Principle:

Principle #35Parameter changes

2Measurement precision

If traditional von Neumann computer architecture is used, then specific instructions can be executed precisely, but ability to learn and handle vague data deteriorates

Engineering Contradiction:

Improveinstruction execution precisionVSAvoidability to learn and handle ambiguous data

Core Design Contradiction:

Measurement precisionVSAdaptability or versatility

Solution Approach 1:

The patent replaces precise binary instruction execution with probabilistic neural pattern recognition. The system substitutes deterministic logic gates with stochastic neural activations that can interpret and learn from ambiguous inputs, maintaining computational capability while gaining learning abilities through weight adjustment mechanisms

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent creates a computational model that copies the functional principles of biological neurons and synapses. By replicating the brain's information processing architecture rather than using traditional digital logic, the system gains the ability to handle vague data through distributed representation and pattern matching while preserving computational efficiency

Inventive Principle:

Principle #26Copying

3Adaptability or versatility

If neurosynaptic systems are implemented, then adaptability and learning capability are improved, but device complexity increases

Engineering Contradiction:

Improvelearning capabilityVSAvoidsystem architecture complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent divides the complex neurosynaptic system into modular functional components including input layers, hidden layers, output layers, and supporting infrastructure. This segmentation allows the complex learning system to be built from manageable modules, reducing overall system complexity while maintaining adaptability

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent creates a universal neural network architecture that can perform multiple functions through a single integrated system. The same neurosynaptic core handles pattern recognition, classification, prediction, and learning tasks, eliminating the need for separate specialized systems and reducing overall device complexity

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS11168558B2Enhancing oilfield operations with cognitive computing

Publication Date: 2021.11.09 HALLIBURTON ENERGY SERVICES INC

AI summary

A cognitive computing system for enhancing oilfield operations, in some embodiments, comprises: neurosynaptic processing logic; and one or more information repositories accessible to the neurosynaptic processing logic, wherein the neurosynaptic processing logic produces a recommendation in response to an oilfield operations indication, the neurosynaptic processing logic produces said recommendation based on a probabilistic analysis of said oilfield operations indication, resources in the one or more information repositories, and oilfield operations models in the one or more information repositories, said oilfield operations models pertaining to oilfield operations associated with said indication, wherein the neurosynaptic processing logic presents said recommendation to a user.