Pore Type Classification via MICP Parameterization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods lack a systematic approach to classify dominant pore type groups (PTGs) in complex hydrocarbon reservoirs, particularly in carbonate formations, which hinders accurate prediction of petrophysical properties and reservoir performance.
Innovation Solution
A method utilizing mercury injection capillary pressure (MICP) data parameterization and Gaussian error function curve fitting to classify pore types independently of depositional geology, enabling objective clustering and extrapolation to the well log domain for accurate rock typing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional rock typing methods based on depositional geology are used, then geological classification is achieved, but accurate prediction of petrophysical properties in complex reservoirs is hindered
Solution Approach 1:
The patent transforms the rock typing approach from geological parameter-based classification to pore-type parameter-based classification using MICP data. By changing the classification parameters from depositional features to capillary pressure characteristics, the system achieves accurate petrophysical property prediction in complex reservoirs where traditional geological methods fail.
Solution Approach 2:
The patent replaces the mechanical/geological classification system with a physics-based capillary pressure system. Instead of relying on depositional geometry and rock fabric analysis, the invention uses MICP measurements and Gaussian error function fitting to objectively classify rocks by pore type, substituting mechanical classification with physical property-based classification.
2Reliability
If subjective classification decisions are used in rock typing, then flexibility in interpretation is maintained, but objectivity and consistency are compromised
Solution Approach 1:
The patent implements self-service through automated objective clustering that performs rock typing without human intervention. The Gaussian error function fitting and clustering algorithms automatically classify MICP data into pore types, eliminating subjective interpretation while maintaining classification consistency. The system serves itself by using mathematical optimization rather than human expert judgment.
Solution Approach 2:
The patent incorporates feedback through the Gaussian error function fitting process, which iteratively optimizes the match between measured MICP data and theoretical pore type models. This feedback mechanism continuously refines the classification by comparing actual measurements with expected pore type characteristics, ensuring objective and consistent results.
3Measurement precision
If pore type classification is performed independently of depositional geology, then petrophysical property prediction is improved, but the link to original rock fabric is lost
Solution Approach 1:
The patent extracts the pore-type characteristics from the complex depositional geological context. By taking out the pore structure information embodied in MICP data and classifying it independently using Gaussian error functions, the system achieves accurate permeability prediction without being constrained by potentially misleading depositional fabric classifications in complex reservoirs.
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 allows for self-consistent predictions of porosity, permeability, and water saturation, improving the accuracy of reservoir modeling and hydrocarbon production optimization by classifying rocks based on unique pore characteristics.
Implementation Method 1
The shape of the mercury injection capillary pressure (MICP) curve reflects characteristics of a rock's porosity network, such as the distribution of pore and pore throat sizes, interconnectivity of the pores
Data Source
AI summary
Embodiments of a method of pore type classification for petrophysical rock typing are disclosed herein. In general, embodiments of the method utilize parameterization of MICP data and/or other petrophysical data for pore type classification. Furthermore, embodiments of the method involve extrapolating, predicting, or propagating the pore type classification to the well log domain. The methods described here are unique in that: they describe the process from sample selection through log-scale prediction; PTGs are defined independently of the original depositional geology; parameters which describe the whole MICP curve shape can be utilized; and objective clustering can be used to remove subjective decisions. In addition, the method exploits the link between MICP data and the petrophysical characteristics of rock samples to derive self-consistent predictions of PTG, porosity, permeability and water saturation.