Rock Typing via MICP Data Clustering and Petrographic Adjustment
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Solution Overview
Problem
Current methods for rock typing using high-pressure mercury injection capillary pressure (MICP) testing may introduce bias and noise due to the indirect analysis of MICP data, which can lead to inaccurate classification of reservoir rocks.
Innovation Solution
A method that involves obtaining MICP data, constructing a distance matrix using a statistical metric like Wasserstein Distance, generating a cluster tree through hierarchical agglomerative clustering, and adjusting it based on petrographic characteristics to determine pore structure types, thereby providing a more direct and accurate rock typing classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If mathematical models are used to fit MICP data before clustering, then the clustering process can be standardized, but bias and noise are introduced into the typing process
Solution Approach 1:
The patent extracts and removes the intermediate mathematical modeling step from the traditional workflow. Instead of fitting MICP data to models like bimodal Gaussian density functions or Thomeer models before clustering, the invention directly applies clustering algorithms to the raw MICP data, thereby eliminating the source of model-induced bias and noise while maintaining process standardization through direct data-driven clustering
Solution Approach 2:
The patent introduces a new intermediary processing step that transforms raw MICP data into a format suitable for direct clustering without requiring mathematical model fitting. This intermediary transformation preserves the original data characteristics while making it compatible with clustering algorithms, avoiding the bias introduced by model-based fitting
2Loss of information
If key parameters of MICP curves are extracted as input for clustering, then physical meanings are preserved, but information loss occurs during parameter extraction
Solution Approach 1:
The patent makes the MICP data itself serve multiple functions: it contains both the physical meaning information (through its shape and characteristics) and the complete dataset needed for clustering. By using the full MICP curves directly as input rather than extracting specific parameters, the data performs both informational and clustering input functions simultaneously, eliminating information loss
Solution Approach 2:
The patent creates a transformed copy of the MICP data that retains all essential characteristics while being optimized for clustering input. This copy maintains the physical meaning of the original data through its structural properties while being in a format that can be directly processed by clustering algorithms without parameter extraction
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
This approach enhances the accuracy of rock typing by directly analyzing MICP data, reducing bias and noise, and providing a more consistent classification of reservoir rocks, which is essential for effective reservoir characterization and modeling.
Implementation Method 1
High-pressure mercury injection capillary pressure (MICP) testing is routinely used to evaluate core samples taken from heterogeneous reservoir rocks
Implementation Method 2
measuring the volume of mercury intruded into pore spaces in the sample at each pressure step
Data Source
AI summary
A method of rock typing includes obtaining mercury injection capillary pressure (MICP) data regarding a region of interest. A distance matrix is computed for distributions determined from the MICP data using a statistical distance metric. A cluster tree of the distributions is generated using the distance matrix. The cluster tree is adjusted based on a petrographic characteristic to produce an adjusted cluster tree, which is used to determine a pore structure types of the region of interest.