Well-to-Seismic Lithology Model Updating via Frequency-Division Processing
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Solution Overview
Problem
Existing logging while drilling (LWD) technologies face challenges in making quick and accurate inversion decisions due to the complexity of formations, leading to deviations in predicting geological conditions of different formations with a single model.
Innovation Solution
A system for updating a clastic rock lithology model through well-to-seismic integration with deep earth oil and gas precision navigation, which includes data acquisition and standardization, variance attribute extraction, formation thickness prediction, frequency-division seismic data processing, optimal seismic frequency band determination, and real-time geological model updating.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single lithology model is used for prediction, then the model structure is simple, but the prediction accuracy deviates due to formation complexity
Solution Approach 1:
The patent segments the single lithology model into multiple formation-specific models based on different formation types (sandstone, mudstone, carbonate, etc.). Each model is tailored to the specific characteristics of its corresponding formation, allowing accurate predictions across heterogeneous formations while maintaining manageable complexity through modular structure.
Solution Approach 2:
The system dynamically selects and switches between different lithology models based on real-time drilling data and formation identification. As the drill bit encounters different formation types, the system automatically activates the appropriate model, enabling adaptive prediction accuracy without requiring a single overly complex static model.
2Reliability
If forward simulation method is used, then the prediction model can be established, but it requires a lot of calculations and model parameter adjustments
Solution Approach 1:
The patent performs preliminary calibration of lithology models using historical well data and seismic data before actual drilling operations. By pre-establishing formation-specific models with optimized parameters based on past experiences, the system reduces the need for extensive real-time calculations and parameter adjustments during drilling, thereby saving time while maintaining reliability.
Solution Approach 2:
The system implements continuous feedback loops where real-time drilling data (LWD logs, gamma ray measurements) are compared against model predictions. Discrepancies trigger automatic model parameter adjustments and refinements, allowing the system to maintain high reliability with minimal iterative calculations by learning from actual measurements rather than relying solely on complex forward simulations.
3Measurement precision
If lithology inference method is used, then lithology model can be established, but large deviation occurs in complex underground geological structure
Solution Approach 1:
The patent applies different inference methods and model parameters tailored to specific local geological conditions. For sandstone formations, one set of inference criteria is used, while mudstone, carbonate, and other formation types have their own specialized inference methods. This localized approach maintains high inference accuracy for each formation type while collectively covering diverse geological structures.
Solution Approach 2:
The system combines multiple data sources (gamma ray logs, resistivity logs, acoustic logs, seismic data) to create a composite lithology inference approach. By integrating information from different measurement types and combining them with formation-specific models, the system achieves both high precision in lithology identification and broad adaptability to various complex geological structures that single-method approaches cannot handle.
4Measurement precision
If inversion simulation method is used comprehensively, then prediction accuracy is improved, but the inversion decision process becomes complex
Solution Approach 1:
The patent focuses on changing and optimizing key inversion parameters (seismic frequency bands, density contrasts, velocity models) rather than performing comprehensive inversion of all geological parameters. By identifying and adjusting the most critical parameters that have the greatest impact on prediction accuracy, the system achieves high precision while keeping the inversion process manageable and interpretable.
Data Source
AI summary
The present invention belongs to the technical field of geological exploration deep Earth. The system of the present invention is configured to: collect seismic data, and preprocess original seismic data to obtain standardized gamma ray logging parameters; extract features from the standardized gamma ray logging parameters to obtain a variance attribute curve while drilling; obtaining a formation thickness prediction model of each set based on the variance attribute curve while drilling; acquire frequency-division seismic data based on the standardized seismic data by means of a frequency-division processing method based on wavelet transform; acquire optimal seismic frequency band information based on a predicted formation thickness value of each set; establish a geological prediction model of the current formation based on the frequency-division seismic data, the optimal seismic frequency band information, historical gamma ray logging parameters and the standardized gamma ray logging parameters.