AI Logging Deployment Selection System
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Solution Overview
Problem
Current logging deployment options, such as wireline and pipe conveyed logging, often result in sub-optimal decision-making due to reliance on operator experience, leading to increased costs, failures, and inefficient operations, as there is no systematic approach to select the best logging method based on well and tool-specific data.
Innovation Solution
A system and method that utilize a user interface to provide options for well, mud, and logging tool data, determine triggers for optimal deployment, and employ statistical analysis and machine learning to predict the likelihood of success for different logging options, comparing current data to historical data to select the most suitable logging method and configuration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If wireline logging is used as the preferred deployment option, then logging quality and reliability are improved, but it becomes difficult to implement in certain conditions such as high angle/horizontal wells
Solution Approach 1:
The system changes the deployment parameters by selecting different logging methods (wireline vs. pipe conveyed) based on well-specific parameters such as well angle, depth, and geological conditions. The AI model evaluates multiple parameters to determine the optimal deployment configuration for each specific well scenario.
2Ease of operation
If logging decisions are made based on operator experience and personal prioritization, then implementation is simple, but sub-optimal deployment decisions lead to increased costs, failures, and operation times
Solution Approach 1:
The system replaces the mechanical decision-making process (operator experience and judgment) with an AI-based computational system that analyzes well data, mud data, and tool configuration data to automatically recommend optimal logging deployment options, thereby improving efficiency while maintaining ease of use through automated recommendations.
Solution Approach 2:
The AI model acts as an intermediary between the raw well data and the final deployment decision, processing and analyzing multiple data sources (well data, mud data, tool configuration) to provide informed recommendations that bridge the gap between simple operation and optimized productivity.
3Quantity of substance
If expensive logging tools are deployed without systematic selection, then tool availability is ensured, but tools may be deployed when not needed, increasing costs
Solution Approach 1:
The system performs preliminary analysis of well data, mud data, and tool configuration data before deploying logging tools, using the AI model to predict the likelihood of success and recommend appropriate tool selections in advance, thereby avoiding unnecessary deployments and reducing costs while ensuring tool availability when truly needed.
Data Source
AI summary
Systems and methods for selecting a logging deployment option are provided. One embodiment of a method includes providing a user interface that provides at least one user option regarding well data about a well, mud data about mud, and logging tools configuration data about logging tools at the well, receiving the well data about the well, the mud data about mud, and logging tools configuration data about logging tools at the well, and determining whether at least one of the well data, the mud data, or logging tools configuration data includes a trigger for determining a desired logging option. In response to determining that, at least one of the well data, the mud data, or logging tools configuration data includes a trigger for determining the desired logging option, selecting the desired logging option and providing the desired logging option for display.