12 Months of Real-Time Digital Chemistry in 3-Phase Flow - Lessons Learned and Plans Forward

Lovell, John (MicroSilicon Inc) | Kulbrandstad, Omar (MicroSilicon Inc) | Madem, Sai (MicroSilicon Inc) | Godoy, Manuel (MicroSilicon Inc)

OnePetro 

Abstract

By miniaturizing and ruggedizing equipment used for quantum paramagnetic spectroscopy, it is now possible to take a real-time chemical snapshot of molecules flowing through the wellhead or other surface fixtures. The digital time-series captures unique chemical properties of the fluid, such as the percentage of asphaltene in the oil, the oil-water ratio and gas-oil ratio. That data can be transmitted via industry-standard cloud protocols and be monitored from a global service center. 12 months of real-time data has been collected from operations around the world and the real-time monitoring has enabled prompt feedback for upgrades in both hardware and software. In a three-phase well configuration that had high rates of both water (over 90%) and gas (~1 MMSCf/day), this feedback drove some significant hardware modifications in order to optimize the consistency of asphaltene data.

The heart of the system is a microwave resonator that was designed to receive fluid at wellhead conditions with minimal reduction from wellhead pressure and temperature. The parameters of the resonator were optimized to maximize microwave intensity for typical oilfield fluids. A tailor-made set-up of fluid accumulator and control-valves upstream of the resonator ensured that the resonator could obtain samples that were mostly oil. By combining the resonator with a solenoid that created a large magnetic field across the oil, the resulting system provided spectroscopic data similar to that available in chemical laboratories but in a smaller package and one that tolerates some gas and conductive water in the oil. The combined quantum data is now provided continuously to the operator via a cloud or other communication architecture of operator choosing. It is anticipated that the resulting Internet of Things (IoT) system will make possible the optimization of chemical program and asphaltene remediation by incorporating system data with integrated flow assurance management. Qualification for offshore is ongoing with 5ksi pressure certification already achieved.

It was not obvious before installation, but once the 3-phase system was installed and the data transmitting in real-time, it became clear that software to automatically extract asphaltene information from spectral data needed to be able to cope with sudden and large changes in both asphaltene level and water-cut/gas-oil ratio which in turn required building an adaptive software model. Asphaltene percentage at one producing well was seen to vary from 0.3% to 3% in a single day. It was also discovered from the cloud-based monitoring that daily temperature variation introduced a phase variation in the shape of the sensor response. Correct derivation of spectral voltages was achieved through the combination of machine learning, model-based analysis and additional diagnostic data such as the quality factor of the resonator and its resonance frequency. As a consequence, the AI-based software could extract the not only the asphaltene percentage but the oil-water cut in the resonator and its gas-oil ratio.

For the first time, it is now possible to make a change in, say injected chemicals, look at the times-series data for the corresponding change in asphaltene and then adjust the chemicals accordingly. Such frequency of sampling (and volume of data) would be too much to handle with samples collected by hand. This device lays the platform for a multiplicity of chemical sensors to be connected to the cloud in real-time and in turn sets the stage to take the hardware offshore and eventually to subsea.