Geophysical Imaging Inversion with Seismic Constraints
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Solution Overview
Problem
Current methods for inverting controlled-source electromagnetic (CSEM) data to obtain a subsurface resistivity model often result in structural inconsistency with other geological or geophysical data, such as seismic data, due to non-uniqueness, limited data coverage, and anisotropy, making it difficult to reliably predict hydrocarbon presence and rock properties.
Innovation Solution
The method involves acquiring data at multiple frequencies to distinguish between intrinsic and structurally-induced anisotropy, using seismic or other geophysical information to constrain the electromagnetic inversion, and implementing regularization functions based on low-frequency seismic data to produce a resistivity model that is consistent with seismic-derived structural models, thereby separating anisotropic resistivities from geological structure and inhomogeneity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional inversion methods are used to process CSEM data, then the resistivity model can be obtained, but the structural consistency with seismic data deteriorates due to non-uniqueness and limited data coverage
Solution Approach 1:
The patent merges CSEM electromagnetic data with seismic data by formulating a joint inversion problem. The objective function combines misfit terms for both CSEM data and seismic data, allowing the inversion to simultaneously satisfy both data types. This merging enables the resistivity model to be constrained by seismic structural information, thereby improving structural consistency while utilizing the complementary information from both data sources.
Solution Approach 2:
The patent introduces an intermediary approach by using a mapping function or transformation that relates electromagnetic properties to seismic properties. This intermediary mechanism allows the inversion to transfer structural information from seismic data to the resistivity model, mediating between the two different data types and enabling consistent integration of both datasets into a unified subsurface model.
2Measurement precision
If inversion is performed with limited data coverage, then the resistivity model can be derived, but the accuracy and reliability of hydrocarbon prediction deteriorates
Solution Approach 1:
The patent applies multi-functionality by using the joint inversion framework to simultaneously achieve multiple objectives: (1) invert CSEM data to obtain resistivity, (2) incorporate seismic structural information, (3) constrain the solution to improve prediction accuracy, and (4) handle anisotropic effects. This universal approach allows the inversion to derive reliable hydrocarbon predictions by integrating information from multiple data sources, effectively compensating for limited coverage in any single dataset.
Solution Approach 2:
The patent utilizes parameter changes by adjusting the weighting parameters and regularization terms in the objective function to optimize the balance between CSEM and seismic data. By dynamically adjusting these parameters, the inversion can adapt to varying data coverage and prioritize the most reliable constraints, thereby maintaining high prediction accuracy even when data coverage is limited in certain regions.
3Reliability
If standard inversion regularization is applied, then the resistivity model smoothness is improved, but the ability to distinguish intrinsic anisotropy from structurally-induced anisotropy deteriorates
Solution Approach 1:
The patent applies local quality by implementing direction-dependent regularization that distinguishes between different spatial directions. The regularization operator is designed to treat vertical and horizontal variations differently, allowing intrinsic anisotropy (vertical electrical conductivity variations) to be preserved while smoothing out structurally-induced anisotropy (horizontal variations due to geological structures). This localized differentiation enables reliable identification of hydrocarbon-bearing zones with intrinsic anisotropic properties.
Solution Approach 2:
The patent introduces asymmetry in the regularization operator to break the symmetry between different spatial directions. By using asymmetric regularization weights or operators that favor certain directions (e.g., vertical direction for intrinsic anisotropy) over others (horizontal direction for structural effects), the inversion can selectively preserve or smooth specific features. This asymmetric treatment enables the model to distinguish between intrinsic and structurally-induced anisotropy, a capability that symmetric regularization cannot achieve.
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 enables the generation of more reliable and geologically consistent subsurface resistivity models that accurately reflect hydrocarbon accumulations and rock properties, improving the reliability of hydrocarbon exploration by maintaining structural consistency and accurately differentiating between intrinsic and structurally-induced anisotropy.
Implementation Method 1
The earth's electromagnetic response is commonly measured both offshore and on land in an effort to identify possible areas of hydrocarbon accumulation. Controlled-source and/or magnetotelluric data are used to derive a resistivity model of the subsurface
Implementation Method 2
The diffusion equation is used to model the propagation of electromagnetic waves through the earth at a range of frequencies, from which the data are inverted for anisotropic resistivities
Data Source
AI summary
Method for transforming electromagnetic survey data acquired from a subsurface region to a subsurface resistivity model indicative of hydrocarbon accumulations or lack thereof. In one embodiment, data are selected for two or more non-zero frequencies (100), and a structural model of the region is developed based on available geological or geophysical information. An initial resistivity model of the region is developed based on the structural model (101), and the selected data are inverted to update the resistivity model (106) by iterative forward modeling (103) and minimizing an objective function (105) including a term measuring mismatch between model synthesized data and measured survey data, and another term being a diffusive regularization term that smoothes the resistivity model (104). The regularization term can involve a structure or geology constraint, such as an anisotropic resistivity symmetry axis or a structure axis, determined from the a priori information (102).