Subsurface Depth Uncertainty Estimation via Anisotropic Velocity Modeling
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Solution Overview
Problem
Current subsurface characterization methods using seismic data face uncertainty due to anisotropic velocity models, leading to inaccurate depth and structural interpretations, which impact oil and gas prospect evaluation and field development.
Innovation Solution
A computer-implemented method estimates depth uncertainty by analyzing anisotropic seismic velocity models, using detectability criteria to compute bounding velocity and eta curves, and subsequently deriving depth uncertainty functions, allowing for the quantification of uncertainty in subsurface characterization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If anisotropic velocity models are used for subsurface characterization, then depth and structural interpretation accuracy is improved, but uncertainty in the measurements and model sensitivity increases
Solution Approach 1:
The patent implements a feedback mechanism by computing depth uncertainty values and feeding them back to the interpretation process. The system calculates uncertainty based on detectability criteria and uses this information to adjust confidence levels in depth interpretations, allowing interpreters to understand the reliability of their results and make more informed decisions about reserve estimation and well placement.
Solution Approach 2:
The patent introduces depth uncertainty as an intermediary parameter that mediates between the velocity model and the final depth interpretation. This intermediary provides quantitative information about model sensitivity and measurement reliability, bridging the gap between raw seismic data and confident geological conclusions without requiring direct observation of subsurface properties.
2Loss of information
If velocity and anisotropy assumptions are made for migration, then depth image can be produced, but inherent uncertainty in depth and structural interpretation increases
Solution Approach 1:
The patent applies preliminary action by computing detectability criteria and depth uncertainty values before final interpretation is made. The system pre-calculates uncertainty metrics based on the velocity model and seismic data quality, allowing interpreters to account for potential errors in advance rather than discovering them after interpretation.
Solution Approach 2:
The patent changes parameters by introducing detectability-based uncertainty metrics that quantify the impact of velocity and anisotropy assumptions. Instead of treating velocity models as fixed truths, the system varies parameters within detectable ranges and computes corresponding depth uncertainty, transforming deterministic interpretations into probabilistic assessments.
Data Source
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AI summary
A system and method for subsurface characterization including depth and structural uncertainty estimation is disclosed. In one embodiment, the method may include determining a detectability threshold for moveout in a seismic data gather based on the seismic data and computing a depth uncertainty function, wherein the depth uncertainty function represents an error estimate that is used to analyze an interpretation of the seismic data. In another embodiment, the method may include receiving a depth uncertainty volume and at least one interpreted horizon from seismic data, extracting a depth uncertainty cage for each of the interpreted horizons based on the depth uncertainty volume, and simulating multiple realizations for each of the interpreted horizons, constrained by the depth uncertainty cage. The multiple realizations may be used for analyzing changes to geometrical or structural properties of the at least one interpreted horizon. The changes may be plotted as at least one distribution and may be used to make P10, P50 and P90 estimates.