Edge Wellsite Controller for Real-Time Reservoir Equipment Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing technologies face challenges in efficiently controlling and optimizing equipment operations in reservoirs due to uncertainties in subsurface environments, leading to inefficiencies in hydrocarbon fluid extraction and production.
Innovation Solution
An equipment controller is developed that includes a processor, memory, and processor-executable instructions to instantiate an edge application and edge framework, processing sensor data to generate control signals for equipment based on real-time results, utilizing frameworks like DELFI, PETREL, and PIPESIM for enhanced reservoir management and production optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If real-time data processing and equipment control are integrated, then operational efficiency and accuracy are improved, but device complexity increases
Solution Approach 1:
The system is divided into distinct modular components: sensor modules for data acquisition, edge computing devices for real-time processing, a framework engine for coordinating operations, and equipment control modules for actuation. This segmentation allows each component to be optimized independently while maintaining overall system efficiency, resolving the contradiction between improved productivity and increased device complexity through structured modularity
Solution Approach 2:
The framework engine serves multiple functions simultaneously: it receives and processes sensor data, generates control signals, coordinates communication between different system components, and manages equipment operations. This multi-functionality consolidates what would otherwise require separate dedicated systems, improving operational efficiency while controlling the growth of overall device complexity through functional integration
2Measurement precision
If edge computing framework is implemented for real-time processing, then response time and control accuracy are improved, but manufacturing complexity increases
Solution Approach 1:
The edge computing framework and processing algorithms are pre-configured and deployed to edge devices before field deployment. Sensor data processing rules, control logic, and framework engine parameters are established in advance, allowing the system to immediately begin real-time processing upon deployment. This preliminary configuration ensures high control accuracy from day one while simplifying manufacturing by separating the complex setup phase from the deployment phase
Solution Approach 2:
The framework engine acts as an intermediary layer between raw sensor data and equipment control commands. It standardizes data processing protocols and control signal formats, making the system more manufacturable by providing clear interfaces and reducing the need for custom integration work. This intermediary framework enables plug-and-play compatibility among different sensor types and equipment, improving ease of manufacture while maintaining high control accuracy through standardized processing
Data Source
AI summary
An equipment controller can include a processor; memory accessible to the processor; and processor-executable instructions stored in the memory to instruct the equipment controller to: instantiate an edge application and an edge framework, where the edge framework includes a framework engine; receive sensor data; process the sensor data via the edge application to issue a call to the edge framework; responsive to the call, implement the framework engine to generate a result; and based at least in part on the result, issue an equipment control signal.