Rock Fabric Classification for Reservoir Modeling
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Solution Overview
Problem
Current methods for modeling subterranean formations are limited by interpretation biases and fail to accurately account for the diverse petrophysical properties of carbonate rocks, leading to uncertainties in hydrocarbon reservoir characterization and flow predictions.
Innovation Solution
A holistic approach to classify rock fabrics based on permeability, sedimentological parameters, compaction, pore types, and grain size, which reduces interpretation biases and provides a more accurate prediction of permeability properties for both cored and un-cored well intervals by considering additional rock data such as grain size, sorting, sedimentary structure, dolomitization, fractures, and stylolites.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If interpreted depositional environment lithofacies are used for classification, then understanding of depositional behavior is improved, but accuracy of petrophysical property prediction deteriorates due to overlapping regions and multiple rock fabrics
Solution Approach 1:
The patent segments the classification system into multiple independent components: depositional environment classification, rock fabric classification, and diagenetic history classification. Each component is classified separately using specific parameters, and their effects on porosity and permeability are evaluated independently. This allows the system to handle the complexity of overlapping lithofacies by treating each classification dimension separately, thereby improving petrophysical property prediction accuracy while preserving depositional behavior understanding.
Solution Approach 2:
The patent adds new classification dimensions beyond traditional lithofacies by incorporating rock fabric parameters (grain size, sorting, shape), diagenetic features (cementation, recrystallization, dissolution), and pore type classifications. This multi-dimensional approach transforms the classification from a single lithofacies-based dimension to a comprehensive multi-parameter system, enabling better differentiation of rock properties that control fluid flow while maintaining depositional environment context.
2Adaptability or versatility
If manual classification by individual sedimentologists is used, then interpretation flexibility is improved, but reliability deteriorates due to interpretation biases
Solution Approach 1:
The patent creates a universal classification framework that can be applied consistently across different reservoirs and by different analysts. The system uses standardized parameters (grain size ranges, sorting categories, sedimentary structure types, diagenetic features) that provide a common language and methodology for all users. This universal approach maintains flexibility in interpretation through the comprehensive parameter set while ensuring reliability through consistent application criteria, eliminating individual biases.
Solution Approach 2:
The patent transitions from subjective qualitative interpretation to objective quantitative parameter-based classification. Specific measurable parameters are defined for each classification category (e.g., grain size in phi units, sorting coefficients, porosity percentages, permeability ranges). This parameter-driven approach allows analysts to maintain interpretative flexibility in selecting and weighting parameters while ensuring reliability through objective measurement and consistent parameter application across all samples.
3Measurement precision
If comprehensive rock data analysis is performed, then prediction accuracy is improved, but measurement cost and time increase
Solution Approach 1:
The patent performs preliminary classification of core samples using readily observable parameters (grain size, sorting, sedimentary structure, rock fabric type) before conducting expensive permeability measurements. This preliminary sorting groups samples with similar petrophysical characteristics, allowing the system to predict permeability properties for entire groups based on representative measurements. This approach significantly reduces the number of time-consuming and expensive direct permeability measurements needed while maintaining high prediction accuracy through the comprehensive classification framework.
Solution Approach 2:
The patent creates detailed classification models and databases from a limited set of comprehensively analyzed core samples. These models serve as templates that can be applied to predict properties of uncoded intervals and additional samples without requiring the same level of direct measurement. The comprehensive rock data analysis is performed once on representative samples, and the resulting classification framework is then copied and applied broadly, reducing overall measurement time and cost while maintaining prediction accuracy.
Data Source
AI summary
A rock fabric classification for modeling subterranean formation includes receiving petrophysical properties from a core analysis of a core sample from a wellbore, receiving a core description of the core sample, the core description comprising sedimentological properties of the core sample, determining one or more groups of core samples with similar sedimentological properties and similar core descriptions, determining bounds for each of the one or more groups, providing the bounds and an identifier of each of the one or more groups, as input to a model for petrophysical rock typing or saturation modeling.