Acoustic Wireless Network Node Self-Organization via Frequency Signatures
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Solution Overview
Problem
Existing methods for installing and operating acoustic wireless networks in wells, such as hydrocarbon wells, are time-consuming, energy-draining, and prone to misconfiguration, with challenges in node identification and unique numbering, leading to operational inefficiencies and risks.
Innovation Solution
The method employs acoustic wireless networks with nodes that use frequency and amplitude signatures to identify and self-organize, allowing for non-sequential installation and autonomous network formation without energy-draining random identification numbers, using polyhistogram signatures to determine unique node identities and optimize communication links.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If nodes are assigned random identification numbers to eliminate sequential installation requirements, then installation flexibility is improved, but the risk of duplicate numbers and energy consumption increases
Solution Approach 1:
Each node automatically determines its unique identification by analyzing the acoustic signature of its communication channel to a reference node. The node measures frequency shifts and propagation characteristics of acoustic signals transmitted through the wellbore fluid, and uses these measurements to generate a unique ID without requiring manual assignment or random number generation, thereby eliminating duplicate IDs while maintaining installation flexibility
Solution Approach 2:
The system uses physical parameters of the acoustic communication channel (frequency shifts, propagation time, attenuation characteristics) as the basis for node identification. By measuring these physical parameters which inherently differ for each node position in the wellbore, the system creates unique identifiers that are guaranteed to be distinct without requiring large random number spaces
2Reliability
If nodes transmit large random identification numbers, then uniqueness is improved, but energy consumption increases
Solution Approach 1:
Nodes generate compact identification numbers by processing acoustic channel measurements locally. Instead of transmitting large random numbers, each node measures frequency shifts and propagation characteristics, processes this data through a hash function or encoding algorithm to create a compact unique ID, and transmits only this compact identifier, significantly reducing transmission energy requirements
Solution Approach 2:
The system extracts only the essential unique characteristics from the acoustic channel measurements (such as dominant frequency shift values or propagation time differences) and uses these extracted features as identification basis. This extraction process creates compact identifiers that maintain uniqueness while minimizing transmission data volume and energy consumption
3Reliability
If nodes are installed in sequential order with central authority numbering, then identification uniqueness is improved, but installation time and complexity increase
Solution Approach 1:
Each node independently determines its own unique identification by measuring acoustic channel characteristics to a reference node. The node transmits a test signal through the wellbore fluid, measures the frequency shift and propagation characteristics returned, processes these measurements to generate a unique ID, and stores it locally. This self-configuration process eliminates the need for sequential installation and central authority numbering, allowing parallel installation while guaranteeing unique identifiers
Solution Approach 2:
The system performs preliminary acoustic channel characterization during the node initialization phase. Each node measures and stores the acoustic propagation characteristics from its location to the reference node before entering normal operation. This preliminary measurement and processing enables automatic unique ID generation without requiring time-consuming sequential installation or manual configuration during deployment
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables efficient, automated node identification and network assembly, reducing energy consumption and operational risks, allowing for dynamic optimization of communication parameters and scalability in larger networks, while predicting potential node failures and optimizing data transmission.
Implementation Method 1
a frequency shift is detected between the transmitted tone and the received tone. The frequency shift is used to identify the transmitting node
Data Source
AI summary
A method of communication using a wireless network is disclosed. A wireless transmission of a signal is received at a first node. The signal has a frequency signature. The frequency signature of the received signal is compared with a frequency signature of a previously received signal from a second node. If it is determined that the frequency signature of the received signal and the frequency signature of the previously received signal are within a predetermined range of similarity, the received signal and the previously received signal are accepted as having been transmitted by the second node.