Automated Well Log Interpretation Using Fuzzy Logic and Machine Learning
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Solution Overview
Problem
The manual interpretation of well logs by experts leads to variability and inefficiency in identifying depositional units, which affects the uniformity and speed of oil recovery efficiency prediction in reservoirs.
Innovation Solution
An automated system using fuzzy logic and machine learning to segment well log data, define membership functions, and determine depositional types based on attribute values, employing a trained expert system to replicate human interpretation and provide uniform analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual interpretation of well logs by expert stratigraphers is used, then interpretation accuracy can be maintained through expert knowledge, but the time required to analyze well logs becomes quite long and information obtained can vary according to the particular expert
Solution Approach 1:
The system creates a digital copy of the expert stratigrapher's interpretation process by training a machine learning model on expert-labeled well log data. The model learns to replicate expert decision-making patterns and depositional unit identification, enabling automated interpretation that mimics human expert performance without requiring actual expert time investment.
Solution Approach 2:
The system performs preliminary action by pre-training the machine learning model on a large dataset of well logs that have been manually interpreted by experts. This preliminary training phase captures expert knowledge and interpretation patterns in advance, so that when new well logs need interpretation, the system can immediately apply this pre-learned knowledge without requiring real-time expert involvement.
2Adaptability or versatility
If manual interpretation by multiple experts is used, then diverse expert perspectives can be captured, but uniformity of analysis deteriorates as results vary according to the particular expert performing the analysis
Solution Approach 1:
The system merges multiple expert perspectives by training the machine learning model on data labeled by multiple different expert stratigraphers. During training, the model learns to synthesize and reconcile different interpretation approaches and depositional unit definitions, producing a unified interpretation framework that incorporates the strengths of multiple experts while eliminating variability between them.
Solution Approach 2:
The system applies parameter changes by adjusting the machine learning model's decision thresholds and classification parameters based on aggregated expert feedback during training. The model learns optimal parameter settings that balance different expert interpretations, transforming subjective expert variations into objective, consistent parameter-based decision rules that ensure uniform analysis across all well logs.
3Productivity
If automated interpretation systems are implemented, then productivity increases and workload is reduced, but the complexity of the system increases requiring machine learning models and algorithms
Solution Approach 1:
The system replaces the mechanical system of manual expert analysis with an automated machine learning-based interpretation system. Instead of relying on human experts to visually examine and interpret well log data, the system uses trained algorithms to automatically process the data, identify depositional units, and generate interpretations, thereby eliminating the need for manual mechanical analysis while significantly increasing throughput.
Solution Approach 2:
The system applies segmentation by breaking down the complex interpretation task into distinct processing stages: data preprocessing, feature extraction, pattern recognition, and interpretation generation. The machine learning model processes well log data through these segmented stages, with each stage handling specific aspects of the interpretation, making the overall complex system more manageable and easier to implement while maintaining high productivity.
Data Source
AI summary
Well log data is assigned depositional labels by a soft computing method. A model is trained on an expert-interpreted well log by segmenting, assigning fuzzy symbols to the segments, and calculating attribute values for units labeled by the expert. From these values, classifiers are trained for each of a number of depositional types. Finally, a model is developed for translating fuzzy symbols into depositional labels. Once trained, the model is applied to well log data.