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A Chemical IoT System for Flow Assurance - From Single-Well Applications to Field Implementation

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Abstract Asphaltene deposition is a significant flow assurance challenge in Abu Dhabi with over 100 onshore wells impacted. Until recently, there had been no applicable IoT device in the industry for direct measurement of the problem, which prompted the national oil company to sponsor a real-time sensor for asphaltene quantification. A second generation of that device is now available with enhanced capabilities and has been delivered in country, with preparation now ongoing for deployment across multiple onshore wells. Current ways of identifying asphaltene problems via accessibility checks with slickline are reactive, often too late, and thereby increase cost of clean-up and production losses. By quantifying in real-time the asphaltene as it flows through the wellhead, much earlier problem detection is made feasible. Such quantification has been made possible by resonating asphaltene molecules in applied magnetic and GHz fields as those molecules flow through the wellhead. The peak of the resonant signal is directly proportional to the asphaltene in the crude, with decrease in signal indicating potential deposition. Adding cloud-based, machine-learning to the system allows local in-country team to make efficient use of the data. First deployment of the resonant system in Abu Dhabi demonstrated the resolution of asphaltene signature was better than 0.1% when measured at atmospheric pressure and temperature. Subsequent testing in pressure vessels has shown remarkable independence to fluid pressure, even up to 2000 psi. Original system had a sensitivity sufficient to detect only the asphaltene peak. New system reveals multiple smaller peaks in the spectra which can be used for other flow assurance applications - for example, vanadyl porphyrins display a unique characteristic. By mixing solvents and precipitants with crude oil, we have confirmed that the asphaltene peak measures same value whether the asphaltene is in, or out, of solution which is precisely the feature that allows surface data to be representative of the cumulative precipitation downhole. The main advance, however, is that the system data will now be available on multiple wells in the field, which allows Operator to compare and contrast different flow assurance optimizations. This should lead to substantial cost savings in addition to minimizing well downtime and potential loss of production, all in alignment with the Operator’s 2030 "Smart Growth" strategy. The additional spectral information also allows spectrometer use for real-time analysis of water properties (dissolved solids) and rock typing (geochemistry). More generally, the system becomes a real-time platform for advanced chemical analysis at the wellhead. The result is an industrial Internet of Things (IoT) real-time monitoring device, the first of its kind, that not just detects asphaltene deposition but also makes possible the optimization of chemical programs by incorporating surface data into an integrated flow assurance management system.
Title: A Chemical IoT System for Flow Assurance - From Single-Well Applications to Field Implementation
Description:
Abstract Asphaltene deposition is a significant flow assurance challenge in Abu Dhabi with over 100 onshore wells impacted.
Until recently, there had been no applicable IoT device in the industry for direct measurement of the problem, which prompted the national oil company to sponsor a real-time sensor for asphaltene quantification.
A second generation of that device is now available with enhanced capabilities and has been delivered in country, with preparation now ongoing for deployment across multiple onshore wells.
Current ways of identifying asphaltene problems via accessibility checks with slickline are reactive, often too late, and thereby increase cost of clean-up and production losses.
By quantifying in real-time the asphaltene as it flows through the wellhead, much earlier problem detection is made feasible.
Such quantification has been made possible by resonating asphaltene molecules in applied magnetic and GHz fields as those molecules flow through the wellhead.
The peak of the resonant signal is directly proportional to the asphaltene in the crude, with decrease in signal indicating potential deposition.
Adding cloud-based, machine-learning to the system allows local in-country team to make efficient use of the data.
First deployment of the resonant system in Abu Dhabi demonstrated the resolution of asphaltene signature was better than 0.
1% when measured at atmospheric pressure and temperature.
Subsequent testing in pressure vessels has shown remarkable independence to fluid pressure, even up to 2000 psi.
Original system had a sensitivity sufficient to detect only the asphaltene peak.
New system reveals multiple smaller peaks in the spectra which can be used for other flow assurance applications - for example, vanadyl porphyrins display a unique characteristic.
By mixing solvents and precipitants with crude oil, we have confirmed that the asphaltene peak measures same value whether the asphaltene is in, or out, of solution which is precisely the feature that allows surface data to be representative of the cumulative precipitation downhole.
The main advance, however, is that the system data will now be available on multiple wells in the field, which allows Operator to compare and contrast different flow assurance optimizations.
This should lead to substantial cost savings in addition to minimizing well downtime and potential loss of production, all in alignment with the Operator’s 2030 "Smart Growth" strategy.
The additional spectral information also allows spectrometer use for real-time analysis of water properties (dissolved solids) and rock typing (geochemistry).
More generally, the system becomes a real-time platform for advanced chemical analysis at the wellhead.
The result is an industrial Internet of Things (IoT) real-time monitoring device, the first of its kind, that not just detects asphaltene deposition but also makes possible the optimization of chemical programs by incorporating surface data into an integrated flow assurance management system.

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