Simultaneous Joint Inversion for Subsurface Property Estimation
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Solution Overview
Problem
Current methods for interpreting geologic data, such as seismic and gravity measurements, face challenges in accurately modeling subsurface structures, leading to inaccuracies in velocity models that can result in mispositioning of hydrocarbon-bearing structures, increasing the risk of drilling dry wells and misidentifying oil and gas reserves.
Innovation Solution
The implementation of simultaneous joint inversion (SJI) techniques, which integrate seismic, gravity, and electromagnetic data using artificial neural networks (ANNs) or polynomial-based approaches to establish physically meaningful relationships between subsurface properties, allowing for the simultaneous estimation of multiple properties like velocity and density, thereby improving the accuracy of earth models and reducing uncertainty.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current interpretation methods are used to examine geologic data, then the process is simpler and faster, but the accuracy of subsurface structure modeling deteriorates, leading to mispositioning of hydrocarbon-bearing structures
Solution Approach 1:
The patent combines multiple geophysical datasets (seismic, gravity, electromagnetic) into a single simultaneous joint inversion process. This merging of previously separate interpretation methods allows the system to leverage complementary information from each data type, improving the accuracy of subsurface property estimation while maintaining a unified computational framework that manages complexity through integrated processing.
2Reliability
If single-domain inversion is performed separately for each geophysical dataset, then the processing is more straightforward, but the reliability of velocity models deteriorates, increasing the risk of drilling dry wells
Solution Approach 1:
The patent implements a multi-domain inversion framework where a single computational system processes multiple types of geophysical data (seismic, gravity, electromagnetic) simultaneously. This universal approach allows the same inversion algorithm to handle different data types with their unique physical properties, improving model reliability by constraining solutions with multiple independent measurements while managing complexity through a unified mathematical framework.
Solution Approach 2:
The patent introduces physically-based relationships as intermediary constraints that link different geophysical properties (e.g., relating density, velocity, and electromagnetic properties through rock physics models). These intermediary relationships act as mediators that connect otherwise independent data types, allowing information to be transferred and constrained across domains while maintaining physical realism, thereby improving reliability without requiring direct coupling of all data types.
3Measurement precision
If traditional interpretation methods are used, then computational resources are used less intensively, but the imaging capability in complex geologic settings like subsalt and subbasalt areas deteriorates
Solution Approach 1:
The patent applies preliminary structural constraints and rock physics relationships before performing the full simultaneous joint inversion. By pre-establishing physically-based connections between geophysical properties and incorporating structural information from seismic data, the system reduces the search space for the inversion algorithm, improving imaging capability in complex settings while reducing computational resource intensity by avoiding exhaustive exploration of all possible models.
Data Source
AI summary
A method can include receiving data associated with a geologic environment; based on at least a portion of the data, estimating relationships for multiple properties of the geologic environment; and based at least in part on the relationships, performing simultaneous joint inversion for at least one property of the geologic environment.