Oilfield Equilibrium Monitoring for Real-Time Production Control
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Solution Overview
Problem
Oilfields are under-sampled systems, leading to significant uncertainty and inefficiency in optimizing hydrocarbon production and economic viability, as current solutions fail to provide effective real-time monitoring and management of changes in oilfield equilibrium.
Innovation Solution
The implementation of intelligent, real-time monitoring and management systems using IoT devices coupled with sensors, actuators, and models (physics-based, data-driven, and hybrid) to generate graphs and decision trees, allowing for adjustments to optimize hydrocarbon production and economic viability by identifying and addressing issues such as leaks and pressure problems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If real-time monitoring and management systems are implemented, then optimization of hydrocarbon production and economic viability is improved, but device complexity and initial costs increase
Solution Approach 1:
The system divides the oilfield into multiple monitored zones with distributed sensors and computing devices. Each zone can be independently monitored and controlled, allowing the complex monitoring task to be segmented into manageable units that can be deployed incrementally across the field.
Solution Approach 2:
The system implements continuous feedback loops where sensor data is collected, analyzed by computing devices, and used to automatically adjust production parameters. This closed-loop control enables real-time optimization of hydrocarbon production while the system learns and adapts to changing field conditions.
2Measurement precision
If comprehensive sensing and computing devices are deployed, then measurement precision and detection capability improve, but device complexity and operational costs increase
Solution Approach 1:
The computing devices are designed to perform multiple functions: collecting data from various sensor types, processing measurements, generating decisions, and controlling actuators. This multi-functionality reduces the need for separate specialized systems while maintaining high measurement precision across multiple parameters.
Solution Approach 2:
The system includes self-diagnostic and self-calibration capabilities where computing devices automatically validate sensor readings, detect anomalies, and adjust for drift. This self-service functionality maintains measurement precision without requiring constant external intervention or complex manual calibration procedures.
3Productivity
If autonomous control of well components is implemented, then productivity and response time improve, but extent of automation increases system complexity
Solution Approach 1:
The system implements dynamic control where actuator settings are continuously adjusted based on real-time field conditions. Production parameters such as valve positions and pump rates are automatically modified in response to changing reservoir conditions, maximizing productivity while the automation adapts to the dynamic nature of oilfield operations.
Solution Approach 2:
Autonomous control is achieved through feedback mechanisms where sensor measurements are continuously compared against target values and control actions are automatically adjusted. This feedback-driven automation improves productivity by enabling rapid response to equilibrium changes without requiring complex centralized control for every decision.
Data Source
AI summary
Systems, methods, and computer-readable media are described for intelligent, real-time monitoring and managing of changes in oilfield equilibrium to optimize production of desired hydrocarbons and economic viability of the field. In some examples, a method can involve generating, based on a topology of a field of wells, a respective graph for the wells, each respective graph including computing devices coupled with one or more sensors and/or actuators. The method can involve collecting, via the computing devices, respective parameters associated with one or more computing devices, sensors, actuators, and/or models, and identifying a measured state associated with the computing devices, sensors, actuators, and/or models. Further, the method can involve automatically generating, based on the respective graph and respective parameters, a decision tree for the measured state, and determining, based on the decision tree, an automated adjustment for modifying production of hydrocarbons and/or an economic parameter of the hydrocarbon production.