Autonomous Petrophysical Formation Evaluation System

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Solution Overview

Problem

Existing petrophysical formation evaluation methods require manual interpretation of drilling data, leading to delayed and partially informed decision-making during drilling operations.

Innovation Solution

The development of an autonomous petrophysical formation evaluation system that analyzes petrophysical and drilling data in real-time, using a workflow that includes data consultation and extraction, expert systems, data analysis, and visualization, to enable simultaneous evaluation of multiple wellbores and automatic integration of new data sources.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual interpretation of drilling data is performed, then data analysis accuracy is improved, but decision-making time is increased

Engineering Contradiction:

Improvedata analysis accuracyVSAvoiddecision-making time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system enables autonomous petrophysical formation evaluation where the computational system automatically performs data analysis, model execution, and formation characterization without requiring manual interpretation. The system serves itself by continuously processing drilling data and generating formation evaluations autonomously

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the mechanical manual interpretation process with an automated computational system that uses machine learning models and algorithms to analyze petrophysical and drilling data, substituting human manual analysis with automated computational mechanisms

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Speed

If real-time autonomous evaluation is implemented, then decision-making speed is improved, but system complexity is increased

Engineering Contradiction:

Improvedecision-making speedVSAvoidsystem complexity

Core Design Contradiction:

SpeedVSDevice complexity

Solution Approach 1:

The system is divided into distinct functional modules including data acquisition modules, data processing modules, machine learning model execution modules, and visualization modules. Each module handles specific tasks independently, allowing the complex system to be managed through modular components

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces computational systems and machine learning models as intermediaries between raw drilling data and formation evaluation results. These intermediaries automatically process and interpret data, reducing the complexity burden on users while enabling real-time autonomous evaluation

Inventive Principle:

Principle #24Intermediary (Mediator)

3Productivity

If multiple wellbores are evaluated simultaneously, then productivity is improved, but computational resources are increased

Engineering Contradiction:

Improveevaluation throughputVSAvoidcomputational resources

Core Design Contradiction:

ProductivityVSUse of energy by moving object

Solution Approach 1:

The system merges the evaluation processes of multiple wellbores into a single integrated computational framework. By combining data processing and model execution across multiple wellbores simultaneously, the system achieves economies of scale in computational resource utilization while maintaining high productivity

Inventive Principle:

Principle #5Merging (Combining)

Data Source

PatentUS20250138220A1Real Time and Autonomous Petrophysical Formation Evaluation and Machine Learning Deployment

Publication Date: 2025.05.01 SAUDI ARABIAN OIL CO

AI summary

A computer implemented method is described. The method includes streaming data comprising petrophysical data associated with at least one subsurface formation obtained in real time. The method includes analyzing the stream of data to determine at least one model configured to evaluate the at least one subsurface formation. The method includes executing the at least one model to evaluate the at least one subsurface formation using the stream of data as input. Additionally, the method includes outputting a representation of formation characteristics in real time.