Autonomous Wellbore Log Analysis for Consistent Multi-Formation Interpretation
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Solution Overview
Problem
Existing wellbore log analysis methods are time-consuming, require substantial domain expertise, and result in inconsistent interpretations due to the complexity and diversity of subsurface conditions, high computational costs, and lack of transferability across tasks or geological settings.
Innovation Solution
An autonomous agent powered by artificial intelligence (AI) automates wellbore log analysis using a foundation model that adapts to multiple applications and formation types, performing end-to-end workflows with generative models for data selection, quality control, and inference, and iteratively improves results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional wellbore log analysis methods are used, then domain expertise can be applied to interpret subsurface data, but the process is time-consuming and requires substantial manual effort
Solution Approach 1:
The system enables self-service automation where the wellbore analysis system automatically performs data exploration, cleaning, correction, selection, model calibration, and evaluation without requiring manual intervention at each step. The system serves itself by autonomously navigating the complex workflow and making decisions based on the subsurface data characteristics.
Solution Approach 2:
Manual mechanical processes of data analysis are replaced with an automated computational system that uses algorithms and machine learning models to perform wellbore log analysis, substituting human manual operations with an intelligent software system.
2Adaptability or versatility
If existing machine learning solutions are deployed, then task-specific analysis can be performed, but high computational costs are incurred and models are not transferable across tasks or geological settings
Solution Approach 1:
The system implements a universal wellbore analysis platform that can handle multiple different tasks and geological settings through a single integrated system. The platform is designed to be multi-functional, adapting to various analysis requirements without requiring separate specialized models for each task or geological formation.
Solution Approach 2:
The system performs preliminary preparation by automatically exploring, cleaning, and correcting data before analysis, and pre-calibrates models based on the specific characteristics of the subsurface data. This preliminary action enables the system to adapt quickly to new tasks and geological settings without requiring extensive retraining or manual preparation.
3Reliability
If manual wellbore log analysis is performed, then quality control can be applied, but inconsistencies in final interpretations occur due to complexity and diversity of subsurface conditions
Solution Approach 1:
The system incorporates feedback mechanisms where the automated analysis process continuously monitors and adjusts its operations based on data quality metrics and interpretation results. The system provides feedback loops that ensure quality control at each stage, maintaining consistency in interpretations by automatically correcting deviations and standardizing the analysis output across different subsurface conditions.
Data Source
AI summary
A method for automating analysis of a plurality of wells includes receiving input data including a database related to the plurality of wells. The method also includes receiving a submission related to the plurality of wells at an autonomous agent. The method further includes performing an automated workflow for the analysis of one or more wells of the plurality of wells based on the submission using the autonomous agent. The method also includes generating an output to the submission based on the automated workflow using a writer agent of the autonomous agent. The method also includes displaying the output from the autonomous agent.