Subterranean Property Estimation via Probabilistic Data Assimilation
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Solution Overview
Problem
Current subterranean survey techniques face challenges in accurately estimating properties of subterranean structures due to uncertainties in survey data, which can lead to exploration and drilling risks, and require improved methods for integrating probabilistic information from geological, rock physics, and seismic data.
Innovation Solution
A system and method that assimilates prior information, including geological and rock physics probability structures, with acquired survey data using probabilistic techniques to produce estimated properties of subterranean structures, incorporating uncertainty analysis and iterative processes to achieve target spatial scaling, and generates images and models for refined interpretations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional survey techniques are used to estimate subterranean structure properties, then the survey process is simpler, but the measurement precision and reliability are reduced due to uncertainties in survey data
Solution Approach 1:
The patent combines multiple probability structures (geological, rock physics, and survey data) into a unified probabilistic framework. This merging of diverse data sources and their associated uncertainties allows for more precise property estimation by leveraging complementary information from each source, directly resolving the contradiction between measurement precision and system complexity.
Solution Approach 2:
The patent introduces probabilistic techniques as an intermediary layer between raw survey data and final property estimates. This intermediary framework systematically handles uncertainties by propagating probability distributions through the estimation process, enabling precise measurements while managing the inherent complexity through structured probabilistic modeling.
2Reliability
If prior information and survey data are assimilated using probabilistic techniques, then the reliability of property estimation is improved, but the computational complexity and processing time increase
Solution Approach 1:
The patent performs preliminary computations by pre-processing survey data and pre-characterizing probability structures before the actual property estimation. This preliminary action prepares the data and uncertainty representations in advance, reducing the computational burden during the main estimation process and thereby decreasing processing time while maintaining high reliability.
Solution Approach 2:
The patent transforms complex probabilistic relationships into simplified parameter representations that capture the essential uncertainty characteristics. By changing the representation of probability structures into more computationally efficient forms, the system achieves reliable property estimation with reduced processing time.
3Measurement precision
If multiple probability structures are integrated, then the measurement precision is improved, but the device complexity and data processing requirements worsen
Solution Approach 1:
The patent develops a universal probabilistic framework that can handle multiple types of probability structures (geological, rock physics, survey data) through a single integrated system. This multi-functional approach allows the same computational infrastructure to process diverse data sources and uncertainty representations, improving measurement precision without proportionally increasing system complexity.
Data Source
AI summary
Prior information describing a distribution of values of a parameter relating to physical characteristic of a target structure is received. Acquired survey data of the target structure is received. Using a probabilistic technique, the prior information and the survey data is assimilated to produce an estimated property of the target structure.