Rock Composition Analysis via Borehole Sensor Indexing
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Solution Overview
Problem
Current methods for evaluating rock composition in geologic environments are limited in accurately determining depositional and diagenetic components, which are crucial for stratigraphic modeling and reservoir management, especially in identifying thin igneous deposits and their impact on completion plans.
Innovation Solution
A method that utilizes data from various borehole tool sensors to determine rock composition, including depositional and diagenetic components, by calculating detrital and diagenetic index values, and attributing these to specific geological sources, thereby enhancing stratigraphic modeling and completion planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional rock evaluation methods are used, then the process is simpler, but the accuracy in determining depositional and diagenetic components is insufficient
Solution Approach 1:
The patent segments rock composition analysis into distinct depositional and diagenetic components by calculating separate index values (e.g., detrital index, carbonate index, siliceous index) for different mineral groups. This segmentation allows precise identification of each component's contribution to overall rock composition, resolving the contradiction by enabling accurate component determination through systematic division of the analysis process.
Solution Approach 2:
The patent transforms raw sensor data into meaningful geological parameters by calculating multiple index values that represent different aspects of rock composition. By changing parameters from raw measurements to composite indices (e.g., combining mineral abundance data into depositional vs. diagenetic indices), the method achieves higher measurement precision while managing complexity through parameter transformation.
2Reliability
If comprehensive sensor data is collected, then the stratigraphic model accuracy improves, but the data processing complexity increases
Solution Approach 1:
The patent extracts specific diagnostic information from comprehensive sensor data by calculating targeted index values that represent key geological characteristics. Instead of processing all raw data equally, the method extracts and emphasizes relevant features (e.g., extracting detrital mineral signatures from bulk composition data), thereby improving model reliability while reducing processing complexity through selective extraction of critical information.
Solution Approach 2:
The patent transforms comprehensive sensor datasets into a simplified set of diagnostic indices that capture essential geological information. By changing parameters from raw multi-dimensional sensor readings to reduced-set composite indices (e.g., converting spectral data into mineral abundance indices), the method maintains high model accuracy while managing data processing complexity through dimensionality reduction.
3Measurement precision
If detailed rock composition analysis is performed, then thin igneous deposits are identified, but the analysis time increases
Solution Approach 1:
The patent performs preliminary classification of rock composition into major groups (detrital, carbonate, siliceous, igneous) using quickly calculable index values before conducting detailed analysis. This preliminary action filters and prioritizes data, enabling rapid identification of potential thin igneous deposits by detecting anomalous index patterns, thereby reducing overall analysis time while maintaining detection precision for thin features.
Solution Approach 2:
The patent uses parameter transformations that enable rapid detection of thin igneous deposits by calculating index values sensitive to minor compositional variations. By changing parameters to emphasize diagnostic mineral ratios (e.g., using normalized indices that highlight igneous mineral signatures against background noise), the method achieves precise detection of thin deposits without requiring exhaustive detailed analysis of the entire dataset.
Data Source
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AI summary
A method can include receiving data for a geologic environment where the data include data acquired via different types of borehole tool sensors; based at least in part on the data, determining rock composition of the geologic environment where the rock composition includes depositional components and diagenetic components; and, based at least in part on the rock composition, outputting a stratigraphic model of at least a portion of the geologic environment.