Virtual Drilling Modeling from Outcrop Data for Accurate 3D Stratigraphy
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Solution Overview
Problem
Existing geological modeling methods rely on limited drilling data, leading to insufficient model accuracy and reliability, and are costly and time-consuming.
Innovation Solution
A virtual drilling modeling method based on outcrop fine characterization, utilizing outcrop observation technology, drone photography, and machine learning to construct a three-dimensional geological model, incorporating vertical sequence stratigraphic histograms and interpolation methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional drilling operations are used to obtain underground geological information, then reliable geological data can be obtained, but the operations are costly and time-consuming, limiting the number and density of drill holes
Solution Approach 1:
The patent creates virtual copies of geological formations through 3D modeling based on outcrop observations. The virtual drilling system simulates drilling operations on digital replicas of rock formations, allowing multiple virtual drill holes to be created without the cost and time of physical drilling, while maintaining geological accuracy through machine learning models trained on actual core sample data
Solution Approach 2:
The patent replaces the mechanical drilling system with a computational modeling system. Instead of physically drilling into rock formations to obtain geological data, the system uses computer-based 3D modeling, machine learning algorithms, and virtual drilling simulations to extract and analyze geological information from outcrop observations and existing core samples
2Manufacturing precision
If limited drilling data is used for geological modeling, then modeling speed is maintained, but model accuracy and reliability are insufficient
Solution Approach 1:
The patent performs preliminary characterization of outcrops and existing core samples before virtual drilling begins. Detailed observations of lithology, structure, and composition are made on exposed rock formations and stored in a database, creating a pre-prepared foundation that enables rapid and accurate virtual drilling operations without requiring time-consuming physical drilling for each model
3Manufacturing precision
If outcrop fine observation technology is used to characterize distribution characteristics, then model accuracy is improved, but the complexity of data collection and processing increases
Solution Approach 1:
The patent develops a multi-functional integrated system that combines outcrop observation, 3D scanning, machine learning training, and virtual drilling operations into a unified platform. The system performs multiple functions including data acquisition from various sources, automated processing through machine learning algorithms, and generation of virtual drilling results, reducing the complexity that would arise from managing separate systems for each function
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Establishes an accurate three-dimensional geological model with lower costs and higher efficiency without actual drilling, enhancing model accuracy and reliability.
Implementation Method 1
deploying drones to take high-resolution photography of the typical sedimentary stratigraphic point positions, and using an oblique photography technology to capture images from different angles of the typical sedimentary stratigraphic point positions; processing the images from the different angles to generate three-dimensional point cloud data
Data Source
AI summary
Provided is a virtual drilling modeling method and system based on outcrop fine characterization. The method includes: selecting multiple typical sedimentary stratigraphic point positions respectively, and using an outcrop fine observation technology to characterize distribution characteristics of lithology, occurrence, thickness, and an extension range of each outcrop layer at the typical sedimentary stratigraphic point positions; establishing each vertical sequence stratigraphic histogram according to the distribution characteristics, the vertical sequence stratigraphic histogram being used to reflect vertical changes of actual stratigraphy; constructing a modeling data set according to each of the vertical sequence stratigraphic histograms and preset sample drilling core columns; and based on machine learning and interpolation operation methods, constructing a three-dimensional geological model according to the modeling data set. Through the high-precision outcrop characterization technology, the present disclosure can establish an accurate three-dimensional geological model with lower cost and higher efficiency without actual drilling.