AI Logging Prediction of Lacustrine Shale Clay Minerals
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Solution Overview
Problem
Conventional methods for predicting clay mineral development in lacustrine shale are time-consuming, labor-intensive, and lack accuracy, particularly in distinguishing between certain clay minerals like kaolinite and illite, and are prone to human interpretation errors.
Innovation Solution
A method utilizing artificial intelligence learning of logging data, including data preprocessing, construction of a BP neural network model, and optimization, to predict clay mineral development in lacustrine shale, using logging data such as density, natural potential, and X-ray diffraction tests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods (rock thin-section observation and chemical analysis) are used to determine clay mineral types and contents, then measurement precision can be achieved, but productivity is significantly reduced due to time-consuming and labor-intensive processes
Solution Approach 1:
The patent replaces manual mechanical analysis methods (rock thin-section observation and chemical analysis) with an automated artificial intelligence system that processes logging data. The AI model learns from labeled samples containing mineral composition data and automatically predicts clay mineral types and contents, eliminating the need for time-consuming manual laboratory analysis while maintaining high prediction accuracy.
2Ease of operation
If traditional logging methods (thorium-potassium crossplot, cation exchange capacity-hydrogen index crossplot) are used, then ease of operation is improved, but measurement precision deteriorates due to inability to accurately distinguish certain clay minerals
Solution Approach 1:
The patent transforms the approach by changing from direct interpretation of traditional logging parameters (thorium, potassium, cation exchange capacity) to using an AI model that processes multiple logging parameters simultaneously. The model learns complex non-linear relationships between various logging responses and clay mineral compositions, enabling accurate differentiation of minerals like kaolinite and illite that traditional single-parameter methods cannot distinguish.
3Manufacturing precision
If stepwise multiple regression method is used to improve measurement precision, then manufacturing precision is improved, but device complexity increases due to human factor interference in model selection
Solution Approach 1:
The patent implements a self-service system where the AI model automatically selects and optimizes the mathematical relationships between logging data and clay mineral composition without human intervention. Through automated training on labeled samples, the model independently determines the optimal prediction approach, eliminating the need for researchers to manually select and adjust complex regression models while maintaining high interpretation accuracy.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Improves prediction accuracy and efficiency, reduces costs and time, and provides rapid, reliable predictions of clay mineral distribution in lacustrine shale.
Implementation Method 1
including one input layer, a plurality of hidden layers and one output layer, and the formula for forward propagation is as follows: where zj is an net output value of a jth node, wij is a weight value between a ith node and the jth node, xi is an input value of the ith node, bj is a threshold value of the jth node
Implementation Method 2
the formula for backward propagation is: where E is a loss function, N is a number of output layer nodes, and yj is a label value of the jth node
Implementation Method 3
through an X-ray diffraction (XRD) shale mineral type test and an XRD clay mineral content test, acquiring the types and contents of clay minerals in shale development
Data Source
AI summary
A method for predicting clay mineral development in lacustrine shale based on artificial intelligence is disclosed. The method comprises acquiring logging data of lacustrine shale, collecting rock samples of lacustrine shale, performing data preprocessing on the collected logging data, and acquiring types and contents of clay minerals in shale development a prediction model of clay mineral development is then developed based on an artificial intelligence. The artificial intelligence-based prediction model of clay mineral development is then optimized; and clay mineral development in lacustrine shale is predicted. In the present disclosure, the accuracy and efficiency of existing prediction methods are improved, allowing for the rapid acquisition of valuable information for the exploration and development of lacustrine shale.