Reservoir Model Uncertainty Reduction via Correlated Seismic Inversion
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Solution Overview
Problem
Current methods for developing reservoir models from seismic inversion data fail to effectively preserve inter-property and inter-layer correlations, leading to uncertainties in flow simulations and volumetric calculations, particularly when integrating well data and geological constraints.
Innovation Solution
A method that converts stochastic seismic inversion data into a form suitable for reservoir models by interpolating and smoothing properties, simulating spatially correlated random fields, and performing kriging adjustments to honor inter-property and inter-layer correlations, allowing for accurate flow simulations and volumetric calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If seismic inversion data is used directly for reservoir modeling, then pointwise property estimates are obtained, but inter-property and inter-layer correlations are lost
Solution Approach 1:
The patent combines multiple seismic inversion realizations to create an ensemble that preserves correlations between different properties (porosity, net sand, etc.) and between different layers. This is achieved by processing multiple inversion results together rather than independently, maintaining the joint probability distributions that represent the correlations.
Solution Approach 2:
The patent introduces an intermediary processing step that takes seismic inversion output and transforms it into a format suitable for reservoir modeling while preserving correlations. This intermediary process includes creating trend maps, calculating covariances, and generating correlated random fields that serve as a bridge between the inversion data and the final reservoir model.
2Ease of operation
If trend maps are used to control geostatistical population, then simple volumetric calculations are enabled, but rich inter-property and inter-layer correlations are not respected
Solution Approach 1:
The patent creates a composite approach by combining trend maps with correlated random fields. The trend maps provide the overall spatial variation and enable volumetric calculations, while the correlated random fields add the necessary variability and preserve inter-property and inter-layer correlations. This composite model integrates both simplicity and accuracy.
Solution Approach 2:
The patent moves from simple 2D trend maps to a multi-dimensional approach by incorporating vertical layer information and inter-property correlations. This adds dimensions of correlation (between properties and between layers) to the traditional trend map approach, enabling both ease of operation and reliability.
3Manufacturing precision
If fine-scale models are built for reservoir simulation, then detailed property representation is achieved, but computational complexity and data requirements increase
Solution Approach 1:
The patent segments the reservoir model into discrete layers with specific properties for each layer. This segmentation allows detailed property representation at appropriate scales without requiring excessive detail throughout the entire model. Each layer can be modeled with the necessary detail while maintaining overall model manageability.
Solution Approach 2:
The patent changes the parameters used to describe the reservoir model from extremely fine-scale properties to scaled properties that are appropriate for reservoir simulation. This includes using effective properties that capture the essential behavior at the simulation scale, reducing model complexity while maintaining accuracy for flow calculations.
Data Source
AI summary
A method for estimating and/or reducing uncertainty in reservoir models of potential petroleum reservoirs comprises receiving the results of a stochastic seismic inversion, and transforming the inversion data into a form suitable for reservoir modelling and flow simulations, while honoring inter-property and inter-layer correlations in the inversion data as well as measured well data and other geological constraints.