Borehole Dip Interpretation Using Automated Web Models
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Solution Overview
Problem
Current methods for interpreting formation geometry around a borehole are limited by the need for costly seismic data, expertise in local geology, and time-consuming manual processes, especially when dealing with steeply dipping layers or complex geological structures.
Innovation Solution
A method involving borehole dip data analysis using a web model workflow that generates instantaneous geological cross-sections, distinguishes structural and sedimentary features from non-structural events, and enables well-to-well correlation and trend analysis, incorporating automated dip detection and classification for improved accuracy and accessibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual dip picking and interpretation methods are used, then expertise in local geology can be applied, but the process becomes time-consuming and less accessible
Solution Approach 1:
The system performs automated dip detection and geological model application, allowing the data to interpret itself without requiring continuous expert intervention. The automated workflow processes borehole data through predefined geological models to generate cross-sections independently
Solution Approach 2:
Manual interpretation processes are replaced with automated computational algorithms that detect dips and apply geological models. The mechanical/manual operation of expert analysis is substituted with automated software processing that maintains accuracy while reducing time consumption
2Reliability
If costly seismic data is used for formation interpretation, then comprehensive geological models can be obtained, but the cost and complexity increase significantly
Solution Approach 1:
The essential interpretative function is extracted from complex seismic data processing and applied specifically to borehole dip data. The system takes out the core geological modeling capability and applies it to the simpler, more accessible borehole data format, maintaining reliability without requiring full seismic data infrastructure
Solution Approach 2:
The system uses readily available borehole dip data instead of expensive seismic data. The automated interpretation process creates reliable geological models from this cheaper, more accessible data source, eliminating the need for costly seismic surveys while maintaining interpretative reliability
3Productivity
If automated dip detection is implemented, then processing time is reduced, but the need for expertise in local geology may be compromised
Solution Approach 1:
Geological models are pre-configured and prepared before data processing. The system has predefined models that incorporate geological knowledge, so when automated dip detection runs, it applies these pre-prepared models immediately, maintaining both speed and accuracy through advance preparation
Solution Approach 2:
The system incorporates iterative refinement where automated dip detection results are evaluated and refined through multiple passes. The feedback mechanism allows the system to adjust and improve interpretation accuracy while maintaining automated processing speed, combining algorithmic efficiency with geological precision
Data Source
AI summary
Embodiments of the disclosure involve a method comprising a method comprising inputting borehole dip data; determining characteristics of a plurality of dips based on the borehole dip data; applying one or more geological models to the characteristics; and generating one or more geological cross-sections based on geological modeling.