Quantitative Seismic Integration Modeling for Reservoir Accuracy
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Solution Overview
Problem
The industry faces challenges in integrating seismic amplitude data into geocellular models to derive accurate reservoir properties, particularly in processing and analyzing large amounts of sensor data, including pre-stack seismic data, and in upscaling seismic data to resolve thin reservoirs or heterogenic geology.
Innovation Solution
A computer-implemented method and system for modeling subsurface regions by receiving pre-stack seismic datasets and well log datasets, selecting key wells, generating geophysical and geological models, and integrating these models into a three-dimensional reservoir model to simulate realistic sub-surface reservoir properties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional seismic data processing methods are used, then processing time and computational resources are reduced, but measurement precision and manufacturing precision of reservoir properties deteriorate
Solution Approach 1:
The system performs preliminary AVO response measurement and iterative re-processing of seismic amplitude data before final inversion. This preliminary action ensures that the seismic data meets prescribed requirements upfront, preventing the need for repeated processing cycles and reducing overall processing time while maintaining high precision in reservoir property derivation.
Solution Approach 2:
The system implements feedback control by measuring AVO response, comparing it against prescribed requirements, and iteratively re-processing the seismic data until requirements are met. This closed-loop feedback mechanism ensures measurement precision is maintained while optimizing processing efficiency through systematic iteration rather than trial-and-error approaches.
2Reliability
If detailed processing of large seismic datasets is performed, then reliability of reservoir models improves, but device complexity and difficulty of detecting and measuring increase
Solution Approach 1:
The processing system divides the complex task of reservoir model generation into distinct modular components: AVO response measurement module, iterative re-processing module, inversion module, and integration module. Each module handles a specific aspect of the data processing pipeline, making the overall system more manageable and maintainable while processing large datasets with high reliability.
Solution Approach 2:
The system introduces an intermediary iterative re-processing stage between raw seismic data collection and final inversion. This intermediary process acts as a mediator that prepares the data by ensuring AVO response requirements are met, thereby simplifying the subsequent inversion process and improving overall system reliability without requiring excessive complexity in any single component.
3Manufacturing precision
If iterative re-processing of seismic data is performed, then manufacturing precision of geophysical models improves, but loss of time increases
Solution Approach 1:
The iterative re-processing is governed by feedback control where each iteration is guided by measured AVO response compared against prescribed requirements. This feedback mechanism ensures that re-processing stops as soon as requirements are met, preventing unnecessary iterations and optimizing the balance between manufacturing precision and time consumption.
Solution Approach 2:
The system performs self-validation by automatically measuring AVO response and determining whether prescribed requirements are met, eliminating the need for manual intervention in each iteration. This self-service capability accelerates the iterative process by automating the evaluation and decision-making steps, thereby reducing overall processing time while maintaining high precision.
Data Source
AI summary
Systems and methods for quantitative seismic integrated modelling (QSIM) are disclosed for integrating the one, two and three-dimensional (1D, 2D, 3D) data from different geoscience domains within a framework in order to produce hi-resolution geocellular models that simulate realistic sub-surface reservoir properties. The QSIM systems and methods accurately leverage the seismically derived reservoir rock properties, integrating the geophysical, geological and engineering information through an optimum rock physics models and takes in consideration all the empirically constrained templates to correct, validate and quality check all the input data.