Well Log Interpreter for Automated Real-Time Data Interpretation
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Solution Overview
Problem
The oil and gas industry faces challenges in quickly and accurately interpreting complex well data from various locations, due to limitations in data acquisition, transmission, and interpretation capabilities, which affects the completeness, comprehensiveness, and robustness of data analysis.
Innovation Solution
A system and method utilizing a well log interpreter with machine-learning models to automatically process and interpret well data from multiple sources, including image, waveform, and scalar log data, providing real-time preview data and interpretation reports, integrating a logging module, quality control module, and quicklook interpretation reporting module for rapid and automated data interpretation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated machine-learning models are used to process well data, then interpretation speed and productivity are improved, but the complexity of the system increases
Solution Approach 1:
The system segments the complex interpretation task into multiple specialized machine-learning models, each handling specific data types (image logs, waveform logs, scalar logs). This modular approach enables parallel processing and improves productivity while managing system complexity through organized modularity.
Solution Approach 2:
The well log interpreter acts as an intermediary layer between raw well data and final interpretation results. It coordinates multiple machine-learning models, manages data flow, and integrates outputs, thereby improving overall system productivity while abstracting the complexity from end users.
2Loss of information
If multiple data types from various logging tools are integrated, then the completeness and comprehensiveness of data analysis are improved, but the difficulty of detecting and measuring increases
Solution Approach 1:
The well log interpreter is designed as a universal platform capable of processing multiple data types (image, waveform, scalar logs) from various logging tools through a single integrated interface. This multi-functionality approach improves data completeness while managing processing difficulty through unified data handling mechanisms.
Solution Approach 2:
The system transforms diverse well data types into standardized parameters and formats that can be processed by machine-learning models. By changing the parameter representation of different data types to a common framework, the system improves information completeness while reducing the difficulty of detecting and measuring various data sources.
3Loss of time
If real-time processing is implemented, then the response time is improved, but the use of energy and computational resources increases
Solution Approach 1:
The system performs preliminary processing and preprocessing of well data before main interpretation, organizing and preparing data in advance. This preliminary action enables more efficient real-time processing by reducing the computational burden during critical interpretation phases, thereby improving response time while managing energy consumption.
Solution Approach 2:
The machine-learning models focus on processing the most critical and informative features of well data rather than analyzing every detail equally. This partial action approach achieves adequate interpretation results with reduced computational resources, improving response time while controlling energy consumption.
Data Source
AI summary
A method for well log data interpretation includes obtaining well data by a well log interpreter and determining, automatically by the well log interpreter, a plurality of machine-learning models corresponding to the well data based on a plurality of well data type. Additionally, the method includes determining, by the well log interpreter and in real-time, preview data regarding a well operation using the machine-learning models, and transmitting, by the well log interpreter to a user device, an interpretation report comprising the preview data. A system for well log data interpretation includes a logging system coupled to a plurality of logging tools, a logging system coupled to a plurality of logging tools, a drilling system coupled to the logging system, and a well log interpreter comprising a computer processor. The well log interpreter is coupled to the logging system and the drilling system. The well log interpreter comprising functionality for performing the well log data interpretation method.