Oilfield Resource Modeling for Autonomous Equipment Configuration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for resource exploration and exploitation, such as oil and gas extraction, face challenges in accurately leveraging data points to characterize resource parameters, leading to flawed resource models that cause delays and financial losses due to non-productive time and equipment misconfiguration.
Innovation Solution
Implementing a system that uses interconnected devices and sensors to collect and analyze data, applying iterative refinement through feedback loops and algorithmic processes to optimize resource models and autonomously configure equipment, enabling real-time dynamic adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processes are used for acquiring model data and configuring equipment, then flexibility in human judgment is maintained, but the processes become extremely cumbersome, expensive, and error-prone with delays
Solution Approach 1:
The system enables autonomous configuration of production equipment by having the system configure itself based on resource model parameters. The autonomous configuration system automatically translates resource model data into equipment configuration settings without requiring manual human intervention, making the system self-sufficient in the configuration process.
Solution Approach 2:
The patent replaces manual mechanical processes with automated computational systems. Instead of human operators manually configuring equipment, the system uses software algorithms and automated control systems to translate resource model parameters into equipment configuration commands, substituting human manual work with automated digital processes.
2Measurement precision
If real-time dynamic equipment configuration is implemented, then operational accuracy is improved, but system complexity increases
Solution Approach 1:
The system implements feedback loops where sensor data from production equipment is continuously collected and fed back to update resource models in real-time. This closed-loop feedback mechanism allows the system to automatically adjust equipment configuration based on actual production data, improving accuracy while maintaining manageable complexity through automated control.
Solution Approach 2:
The patent implements dynamic equipment configuration that adapts in real-time based on changing resource model parameters and production conditions. Instead of static configuration, the system continuously adjusts equipment settings dynamically response to updated data, enabling operational accuracy to improve while the system handles complexity through automated adaptation.
3Reliability
If multiple scenarios are modeled and tested during initial planning, then operational stability is improved, but the time and computational resources required increase
Solution Approach 1:
The system performs preliminary modeling and testing of multiple production scenarios during the initial planning phase before actual production begins. By conducting virtual simulations and stability analyses in advance, the system identifies optimal configuration parameters and potential issues beforehand, reducing the need for time-consuming adjustments during actual production operations.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
The present disclosure relates to a method comprising: receiving a resource model associated with a resource site and receiving one or more objective parameters, such that a first objective parameter comprised in the one or more objective parameters is a function of one or more parameter values of the resource model. The method comprises executing simulations to generate a first uncertainty value based on at least one of a first parameter value and a first uncertainty value of a first parameter of the resource model. The simulations may be executed to generate a first forecast uncertainty value for each scenario comprised in a plurality of scenarios. The method also identifies one service that minimizes an uncertainty value of the objective parameter based on the forecast uncertainty value. The method further includes generating a first visualization comprising the one identified service for viewing by a user via a user interface.