Calibrated Rock-Physics Model for Subsoil Characterization
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Solution Overview
Problem
Current methods for calibrating rock-physics models in subsoil characterization are inefficient, as they attempt to globally fit unsuitable rock-physics models with inverted seismic data, failing to accurately predict the evolution of petro-elastic parameters due to simultaneous physical phenomena and uncertainties in layer structure changes.
Innovation Solution
A computer-implemented method that calibrates rock-physics parameters within a specific calibration body with homogeneous rock-physics parameters, reducing the volume subject to single physical phenomena, using standard deviation to determine spatial coherence and update the calibration body, and adjusting constant parameters to match petro-elastic parameter evolutions from seismic data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If global calibration of rock-physics model is performed across entire subsoil volume, then comprehensive coverage is achieved, but accuracy decreases due to simultaneous physical phenomena and heterogeneous conditions
Solution Approach 1:
The subsoil volume is segmented into multiple calibration bodies based on geological layers and facies types. Each calibration body is treated independently with its own calibration parameters, allowing accurate modeling of local physical phenomena while maintaining comprehensive coverage through the division of the entire subsoil volume into manageable homogeneous segments.
Solution Approach 2:
Different calibration parameters and rock-physics models are applied to different calibration bodies according to their specific geological characteristics. This local quality approach ensures that each region is modeled with appropriate parameters reflecting its unique physical phenomena, thereby improving overall prediction accuracy while maintaining comprehensive coverage.
2Quantity of substance
If calibration body volume is increased to improve statistical reliability, then more data points are available, but spatial coherence decreases due to inclusion of heterogeneous regions
Solution Approach 1:
The calibration body volume is dynamically adjusted based on the calculated spatial coherence of rock-physics parameters. When coherence exceeds the threshold, the calibration body is expanded to include more data points; when coherence falls below the threshold, it is contracted to maintain homogeneity. This dynamic adjustment optimizes both statistical reliability and spatial coherence.
Solution Approach 2:
A feedback mechanism using spatial coherence calculation controls the calibration body volume. The standard deviation of rock-physics parameters is continuously monitored, and when it exceeds the threshold, the calibration body is automatically adjusted. This feedback loop ensures that the calibration body maintains optimal volume for statistical reliability while preserving spatial coherence.
3Measurement precision
If rock-physics model is calibrated to match inverted seismic data, then model accuracy improves, but reliability decreases due to potential mismatches from incorrect model or data
Solution Approach 1:
The subsoil is divided into multiple calibration bodies, each calibrated independently. This segmentation allows identification of local calibration issues without compromising the entire model. By calibrating smaller homogeneous regions separately, the method improves both local accuracy and overall reliability through localized validation and adjustment.
Solution Approach 2:
The calibration process adjusts rock-physics model parameters within defined ranges to match inverted seismic data while maintaining physical plausibility. By constraining parameter changes to realistic ranges and validating against multiple seismic datasets, the method improves model accuracy while preserving reliability through physically-based constraints.
Data Source
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AI summary
A method for providing a calibrated rock-physics model of a subsoil (1 ) is presented. First, a geological model (2) of the subsoil comprising a grid made of cells (40), associated with a rock-physics parameter is obtained. A group of cells forming a calibration body (3, 31, 32) is selected in the grid. The calibration body corresponds to a region of the subsoil having substantially homogenous rock-physics parameter values. Finally, an adjustable constant parameter in a physical equation expressing a relationship between the petro-physical parameter and a petro-elastic parameter in the calibration body is calibrated so as to reduce a mismatch between the petro- elastic parameter estimated using the physical equation and a petro-elastic parameter value determined from inverted seismic data, the calibrated physical equation providing a calibrated rock-physics model of the subsoil in the calibration body.