Cloud Platform Integrating Simulation Engines with Machine Learning Models
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Solution Overview
Problem
Current technologies lack a straightforward solution for end users without coding skills to integrate cloud-based data science applications with simulation engines, limiting their ability to leverage machine learning for predicting physical phenomena in oilfields.
Innovation Solution
A method and system that integrate a simulation engine with a visualization web application via an intermediary data science engine, utilizing pre-trained machine-learning models and physics-based neural networks to predict pressure results and the likelihood of physical phenomena, such as land subsidence or earthquakes, in oilfields.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If cloud-based data science applications are integrated with simulation engines using programming and computer knowledge, then functionality and performance are enhanced, but the complexity of operation increases and accessibility to end users without coding skills is reduced
Solution Approach 1:
A cloud-based platform is introduced as an intermediary layer between simulation engines and end users. This platform provides graphical user interfaces and automated workflows that allow users without coding skills to access and utilize machine learning models and simulation capabilities. The platform handles the complex integration and data processing in the background while presenting simplified controls to users.
2Adaptability or versatility
If standalone applications are integrated through programming connections, then data science modeling capability is improved, but the time required for integration and setup increases
Solution Approach 1:
The system pre-configures integration templates, data pipelines, and workflow automations that are ready to deploy. Common integration scenarios between simulation engines and data science applications are prepared in advance with standardized interfaces and pre-written code modules, allowing users to quickly deploy models without performing complex integration tasks from scratch.
Solution Approach 2:
The cloud-based platform merges multiple standalone applications into a unified system. Simulation engines, data science tools, and visualization applications are combined into a single integrated environment where data flows automatically between components. This eliminates the need for users to manually connect separate applications through programming.
3Measurement precision
If machine learning models are implemented with full customization options, then model accuracy and precision are improved, but the device complexity and computational resources required increase
Solution Approach 1:
The system provides dynamic model selection and configuration capabilities. Users can start with pre-trained models for quick deployment and gradually increase customization as needed. The platform automatically adjusts model parameters and complexity based on available data and computational resources, allowing the system to adapt its complexity level to match user needs and system capabilities.
Data Source
AI summary
A method for predicting a likelihood that a physical phenomenon will occur in an area of interest at a wellsite includes receiving input parameters for a well in the area of interest. The method also includes generating or updating a geomodel based upon the input parameters. The geomodel includes a first model or a second model. The method also includes predicting a pressure result using the geomodel. The pressure result is based upon the input parameters. The method also includes predicting the likelihood that the physical phenomenon will occur in the future in the area of interest based upon the pressure results. The likelihood that the physical phenomenon will occur is predicted using a third model that is different than the first and second models.