M. Gonzalez, C. Gooneratne, A. Magana-Mora, S. Ayirala, A. Sofi, M. Deffenbaugh
Aramco Services Company: Aramco Research Center—Houston,
United States
Keywords: rheology, process monitoring, fluid property sensors, viscosity, density
Summary:
From food, to plastics, to oil production, there is a need across different industries for robust sensing solutions able to provide real-time feedback on the rheological properties of materials that are either being produced, manufactured, or utilized to accomplish a specific operational goal. In upstream oil and gas operations, different specially designed fluids are used during drilling, hydraulic fracturing, or enhanced-oil-recovery (EOR). Additionally, produced hydrocarbons can come as emulsions, suspensions or other complex forms that affect flow assurance. The quality of these fluids can be ensured by measuring their rheological properties, i.e. their shear-dependent viscosity and viscoelastic properties. Furthermore, optimizing these properties allows rig-operators maintain safe and efficient operations. Currently, many industries, like the oil and gas industry, still rely on legacy technologies or protocols that compromise the quality and quantity of rheological data that is made available to operators. For example, during a typical drilling operation, field technicians rely on periodically collecting samples and testing them using manual instruments such as funnel viscometers, weight balances or basic field rheometers. It is desirable to have automated systems that make continuous and robust measurements and does not depend on the skill level of the technician running measurements. Furthermore, compact and economical sensors that can be autonomously deployed at multiple and remote points during the fluid circuit of operation may enable the generation of greater amounts of data such that modern data analytics, machine learning, and AI techniques can be used as a process optimization tool. Here, we demonstrate different technologies developed for rheological process monitoring based on electromechanical tuning fork resonators. First, we discuss a robust in-tank viscometer for drilling fluid monitoring and automated drilling operations. The system is comprised of an electromechanical assembly carefully optimized to be the frequency-defining element of a tuned oscillator circuit. Since the system is tuned to vibrate close to its resonance frequency autonomously, it can track fast and sudden changes in fluid composition without user intervention. We discuss our integration of the tuning fork sensor into a rig-side edge-computing system for the development of time-series analysis using machine learning techniques and the detection of operational problems such as lost circulation events. Second, we discuss the development of a viscometer based on miniaturized piezoelectric tuning viscometers. In this case, the small footprint of the device allows their integration into an untethered logging tool for quick downhole deployment and monitoring of enhanced-oil-recovery (EOR) fluids. This demonstrates the versatility of these sensors for reaching measurement points where no standard rheometer can be deployed. The tuning forks were calibrated to provide equivalent rheological information as it is measured at surface. This tool enables periodic measurements of EOR polymer fluid viscosities in order to check for polymer degradation and therefore avoid incurring in costly operational setbacks that can have large impact in oil production rates. Finally, we describe our vision of using micro/nanotechnologies to enable more sophisticated rheological measurements based on micro/nano-scale vibrating structures that probe different viscoelastic regimes of material response, potentially useful for advanced material characterization.