Autonomous Oilfield Operations With Sensor Data Quality Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for data capture and equipment configuration in resource explorations, such as oil and gas, are cumbersome, expensive, and error-prone, lacking real-time dynamic updates, leading to flawed resource models that cause delays and losses.
Innovation Solution
A system and method for capturing sensor data at a resource site, executing an execution plan, and performing quality control operations to ensure accurate data capture and equipment configuration, using machine learning or AI for data evaluation and dynamic model updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual processes are used for acquiring model data and configuring equipment, then flexibility and adaptability are maintained, but the processes become extremely cumbersome, expensive, and error-prone
Solution Approach 1:
The system performs self-service through automated execution plans that autonomously acquire sensor data, evaluate it using machine learning models, and configure equipment parameters without manual intervention. The execution plan independently manages the entire workflow from data capture to model updating, eliminating human error while maintaining operational flexibility.
Solution Approach 2:
Manual mechanical processes for data acquisition and equipment configuration are replaced with an automated digital system. The execution plan uses software-based control to substitute human operators, employing algorithms and machine learning models to perform tasks that were previously done manually, thereby improving both efficiency and reliability.
2Productivity
If manual processes are used for acquiring model data and configuring equipment, then system complexity is reduced, but real-time or pseudo-real-time dynamic equipment configuration based on updates to the resource model is lost
Solution Approach 1:
The execution plan is prepared in advance with predefined sensor configurations, data processing workflows, and equipment parameter settings. This preliminary action allows the system to rapidly execute updates when resource models change, as the framework is already in place and only needs to be triggered with new parameters, achieving real-time responsiveness without ad-hoc complexity.
Solution Approach 2:
The system implements dynamic equipment configuration through the execution plan, which automatically adjusts sensor parameters and equipment settings based on updated resource models. The plan enables real-time adaptation of system parameters in response to changing conditions, transforming a static manual process into a dynamic automated workflow that responds continuously to model updates.
3Measurement precision
If comprehensive sensor data capture and quality control operations are implemented, then data accuracy and resource model reliability are improved, but time and computational resources are consumed
Solution Approach 1:
The execution plan implements continuous feedback loops where sensor data is captured, evaluated against quality criteria using machine learning models, and used to update resource models. This automated feedback mechanism ensures high measurement precision while minimizing time loss by immediately processing data through predefined quality control operations rather than requiring manual review cycles.
Solution Approach 2:
The system dynamically changes processing parameters based on data quality assessments. The execution plan adjusts evaluation thresholds, sampling rates, and processing intensity according to the specific characteristics of each dataset and the current resource model state, optimizing the balance between measurement precision and processing time for each operational context.
Data Source
AI summary
The present disclosure relates to a system that is operable to receive an execution plan and execute a control operation on one or more equipment based operations within the execution plan. The one or more operations may include a data capturing operation associated with a resource site. In one embodiment, the system may be operable to execute at least a first operation in response to a success variable of the data capturing operation indicating a successful execution of the data capturing operation. The first operation may include a quality control operation that is executed by comparing at least one characteristic of the captured data to an expected characteristic to generate quality state data. The quality state data may have one of an acceptable status and an undesirable status. In response to the quality state data indicating an acceptable status for the quality control operation, executing at least a second operation.