3D Formation Evaluation for Rock Type Distribution
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Solution Overview
Problem
Current petrophysical modeling techniques struggle to accurately determine the distribution of rock types and their structural characteristics in reservoirs, leading to inadequate hydrocarbon volume predictions and poor reservoir evaluation, especially in complex formations with thin layers and high-angle wells.
Innovation Solution
The implementation of 3D Formation Evaluation (3DFE) modeling, which uses directional measurements to define rock types and their mixing degrees in geological cells, allowing for more accurate determination of porosity, permeability, and fluid content, and enables upscaling of these models for larger reservoir simulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional petrophysical modeling techniques are used, then the modeling process is simple, but the accuracy of rock type distribution and hydrocarbon volume predictions is insufficient
Solution Approach 1:
The reservoir is divided into multiple geological cells arranged in a three-dimensional grid around the wellbore. Each cell is independently characterized by rock type distribution and petrophysical properties, allowing detailed local analysis while maintaining overall reservoir context. This segmentation enables accurate representation of heterogeneous rock formations and thin layers that conventional techniques cannot resolve.
Solution Approach 2:
The invention transitions from conventional one-dimensional or two-dimensional modeling to three-dimensional formation evaluation. By defining geological cells in three dimensions with radial and vertical positioning, the method captures the spatial distribution of rock types and anisotropy, providing comprehensive reservoir characterization that accounts for complex geometries and high-angle well trajectories.
2Adaptability or versatility
If conventional modeling techniques are used, then the computational requirements are low, but the ability to characterize complex formations with thin layers and high-angle wells is inadequate
Solution Approach 1:
Each geological cell is assigned specific rock type distributions and petrophysical properties that reflect local formation characteristics. The method allows different cells to have different rock types, porosities, permeabilities, and anisotropy parameters, enabling accurate representation of heterogeneous reservoirs with thin layers and complex lithology variations that conventional uniform modeling cannot capture.
Solution Approach 2:
The formation is modeled as a composite of multiple rock types within each geological cell, with each rock type contributing differently to the overall petrophysical properties. This composite approach allows the model to represent complex formations containing mixed lithologies, cements, and pore structures, accurately predicting hydrocarbon volumes in heterogeneous reservoirs.
3Measurement precision
If rock structure and anisotropy are not accounted for, then the modeling is simpler, but the hydrocarbon volume predictions are less accurate
Solution Approach 1:
The method performs preliminary characterization of rock type distribution and anisotropy parameters for each geological cell before calculating hydrocarbon volumes. By pre-defining the structural framework and rock type assignments, the model prepares the necessary inputs for accurate volume calculations, ensuring that anisotropy and rock structure effects are properly accounted for in the final hydrocarbon estimation.
Solution Approach 2:
The invention creates a detailed three-dimensional digital copy of the reservoir formation, replicating the actual rock type distributions, anisotropy patterns, and geological structures. This virtual model allows comprehensive analysis of rock structure effects on hydrocarbon distribution without requiring physical sampling, providing accurate predictions while reducing the need for extensive field measurements.
Data Source
AI summary
A method includes acquiring measurement data from a plurality of measurements corresponding to different depths within a wellbore. Using a processor, a distribution of rock types in each cell of a plurality of geological cells around the wellbore is determined from the measurement data. Petrophysical characteristics of each cell of the plurality of geological cells are calculated from the distribution of rock types.