Capillary Pressure Analysis for Reservoir Rock Type Prediction
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Solution Overview
Problem
Current methods for predicting rock types and characteristics in subterranean reservoirs are either expensive and time-consuming, such as actual rock sample analysis, or rely on simplifications like saturation modeling from lithological log data, which may not accurately represent reservoir properties.
Innovation Solution
The use of capillary pressure analysis with Reservoir Development Controlling Factors (RDCFs) to discriminate multiple curve data sets into groups, creating capillary pressure type curves for each pore structure group, and processing log data to derive RDCFs, thereby improving the prediction of rock types and characteristics and creating representative saturation height models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If actual rock sample analysis is performed, then measurement precision of reservoir properties is improved, but loss of time and cost increase significantly
Solution Approach 1:
The patent applies preliminary action by performing capillary pressure analysis on core samples before full-scale reservoir modeling. This preliminary analysis establishes pore structure groups and type curves that can be reused for multiple wells and formations, avoiding repeated time-consuming analysis while maintaining measurement precision for reservoir properties like porosity and permeability.
Solution Approach 2:
The patent creates capillary pressure type curves that serve as templates or copies representing entire pore structure groups. These type curves are derived from limited core sample data but can be applied to predict reservoir properties across multiple wells and formations, significantly reducing the time and cost of actual rock sample analysis while maintaining measurement precision through statistical characterization.
2Loss of time
If saturation modeling approaches with rock type and assigned permeability characteristics are used, then loss of time and cost are reduced, but manufacturing precision of reservoir property prediction deteriorates
Solution Approach 1:
The patent transforms the approach by changing from using single assigned permeability values to using statistical distributions of permeability and porosity derived from capillary pressure type curves. This parameter change allows the modeling approach to maintain speed while improving prediction accuracy by accounting for the natural variability and uncertainty in reservoir properties through probabilistic methods.
Solution Approach 2:
The patent creates a composite modeling approach by combining capillary pressure analysis data with saturation modeling. The method integrates type curve data representing pore structure characteristics with rock type and permeability information to create a hybrid model that retains the computational efficiency of saturation modeling while incorporating the measurement precision of capillary pressure analysis for improved reservoir property prediction.
3Reliability
If capillary pressure analysis is performed on core samples, then reliability of reservoir characterization is improved, but loss of time and cost increase
Solution Approach 1:
The patent applies segmentation by dividing the capillary pressure data into distinct pore structure groups based on similarity of curve characteristics. This segmentation allows the analysis to focus on identifying representative type curves for each group rather than analyzing every individual core sample in detail, thereby maintaining reliable reservoir characterization while reducing overall analysis time through systematic categorization.
Solution Approach 2:
The patent creates capillary pressure type curves that serve multiple functions: they characterize pore structure, predict saturation-pressure relationships, and provide statistical parameters for uncertainty analysis. This multi-functionality allows a single set of type curves to be applied across multiple wells and formations, improving reliability of reservoir characterization while significantly reducing the time and cost of repeated capillary pressure analysis.
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 more accurate and efficient prediction of rock types and saturation distributions, reducing the time and cost associated with reservoir modeling, while providing improved representation and accuracy of reservoir properties, which can enhance well placement and field development plans.
Implementation Method 1
capillary pressure analysis can be performed on an actual rock sample from a reservoir well core sample. The capillary pressure analysis can provide concrete information about the porosity and permeability of types of rock present in the rock sample
Data Source
AI summary
A multiple curve capillary pressure data set derived from a core sample is discriminated into groups of similar curves representing similar pore structure groups. Primary reservoir development controlling factors (RDCFs) are identified for each pore structure group and a set of capillary pressure type curves are created for each pore structure group to statistically characterize saturation-pressure response. Data is processed from a core sample log to derive identified RDCFs from the log data. A preliminary reservoir development designation log is derived by applying cutoffs to the log-based RDCFs and a preliminary saturation distribution equivalent to the preliminary reservoir development designation log is obtained by applying the capillary pressure type curves. A capillary pressure type uncertainty envelope is compared with saturation measurements from the log. The modelled saturation from the average capillary pressure type curve is recalculated to generate an optimized reservoir development designation.