Multi-Agent Petrophysical Data Processing for Reservoir Interpretation

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Solution Overview

Problem

Managing and understanding petrophysical data for hydrocarbon reservoirs is challenging due to the complexity and volume of data, requiring human intervention at each stage, leading to suboptimal operations and inefficient extraction.

Innovation Solution

A multi-agent architecture is employed to automate the management and processing of petrophysical data, utilizing rule-based and AI-based software agents organized into groups for planning, acquisition, delivery, interpretation, and reporting, facilitating data collection, quality checking, and intelligent decision-making.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If human personnel manually manage and evaluate petrophysical data at various stages of reservoir development, then flexibility and adaptability in handling different reservoir types and operations are maintained, but the process becomes time-consuming and leads to suboptimal operations due to human limitations in grasping vast amounts of data

Engineering Contradiction:

Improveability to handle different reservoir types and operationsVSAvoidtime required for multi-stage data management process

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The system enables self-service through autonomous agents that automatically perform data collection, quality checking, interpretation, and operation recommendation without requiring human intervention at each stage. The agents independently navigate the multi-stage process, reducing time loss while maintaining adaptability through programmable logic that handles different reservoir types and operations.

Inventive Principle:

Principle #25Self-service

2Reliability

If human personnel manually interpret petrophysical data and determine operation parameters, then judgment and decision-making can be exercised, but the process is inefficient and may lead to suboptimal extraction due to human capacity limitations

Engineering Contradiction:

Improveaccuracy of data interpretation and operation decisionsVSAvoidefficiency of data processing and operation execution

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The patent replaces the mechanical human cognitive system with an automated computational system consisting of software agents that process petrophysical data. These agents execute predefined algorithms and logic to interpret data, determine operation parameters, and recommend actions, thereby eliminating human capacity limitations while maintaining reliability through systematic processing.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Loss of information

If vast amounts of petrophysical data are managed and analyzed, then comprehensive reservoir understanding is achieved, but the complexity and volume of data become difficult for humans to grasp and manage

Engineering Contradiction:

Improvecompleteness of petrophysical data utilizationVSAvoidcomplexity of data management system

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The patent segments the complex data management system into multiple specialized agents, each responsible for specific functions such as data collection, quality checking, interpretation, and operation recommendation. This segmentation allows the system to handle vast amounts of petrophysical data comprehensively while managing complexity through modular, functionally-separated components that operate independently but coordinate together.

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS12565831B1Adaptive multi-agent architecture for petrophysical data management and processing

Publication Date: 2026.03.03 SAUDI ARABIAN OIL CO

AI summary

A system and architecture for development of a hydrocarbon reservoir via automatic processing of petrophysical data using software agents. The software agents may provide data collection, quality checking, data integration, interpretation, processing, and reporting, and may be organized into six groups of functionalities: planning, acquisition, delivery, interpretation, enrichment, and reporting. The software agents may be rule-based, artificial intelligence-based, or a combination thereof.