Real-time monitoring of wellbore fluids is of crucial importance for oil recovery. During various operations such as drilling, production, stimulation, and intervention, the characteristics of the fluids being produced or injected into the well needs to be monitored to ensure safe operation or to optimize production. However, measuring their physical properties, such as viscosity and density, can still pose a great challenge when the fluids are part of a multiphase flow or are complex inhomogeneous fluids whose properties can vary largely with external parameters such as temperature, pressure, and flow rates. While commercial instruments that make comparable measurements can be found in other industries, a solution tailored to these oilfield challenges does not exist at the moment. In this paper, we demonstrate two examples of viscosity/density systems using tuning fork-based mechanical oscillators tailored to different measurement scenarios in the oilfield. In the first example, we use a miniature piezoelectric tuning fork which can be integrated in small-scale measurement platforms for downhole deployment. By using the tuning fork as the frequency-defining element of an oscillator circuit, fast measurements of viscosity and density are possible. This feature allows us to use this device in fast changing multiphase flows, such as those found in live producing wells. In the second example, we demonstrate a ruggedized tuning fork device for in-line measurements of viscosity and density of non-Newtonian fluids. This instrument is a robust permanent tool for surface installation with applications in quality assessment of drilling mud and other wellbore fluids. We discuss some initial studies in a water-based drilling fluid.

Clay slurries are both ubiquitous and essential in the oil exploration industry, and are most commonly employed as drilling fluids.

In this work, we report on the novel employment of lithium niobate tuning forks as acoustic transducers in photoacoustic spectroscopy for gas sensing. The lithium niobate tuning fork (LiNTF) exhibits a fundamental resonance frequency of 39196.6 Hz and a quality factor Q = 5900 at atmospheric pressure. The possibility to operate the LiNTF as a photoacoustic wave detector was demonstrated targeting a water vapor absorption line falling at 7181.14 cm⁻¹ (1.39 µm). A noise equivalent concentration of 2 ppm was reached with a signal integration time of 20 s. These preliminary results open the path towards integrated photonic devices for gas sensing with LiNTF-based detectors on lithium niobate platforms.

A compact and portable gas sensor based on quartz-enhanced photoacoustic spectroscopy (QEPAS) for the detection of methane (C1), ethane (C2), and propane (C3) in natural gas (NG)-like mixtures is reported. An interband cascade laser (ICL) emitting at 3367 nm is employed to target absorption features of the three alkanes, and partial least-squares regression analysis is employed to filter out spectral interferences and matrix effects characterizing the examined gas mixtures. Spectra of methane, ethane, and propane mixtures diluted in nitrogen are employed to train and test the regression algorithm, achieving a prediction accuracy of ∼98%, ∼96%, and ∼93% on C1, C2, and C3, respectively. With respect to previously reported QEPAS sensors for natural gas analysis, the high prediction accuracy as well as the capability to discriminate and detect C3 within natural gas-like complex mixtures provided by the employment of partial least-squares regression mark significant improvements. Furthermore, these results enable an improved performance of the sensor for in situ, real-time, and online natural gas composition analysis.

Maintaining the viscosity and density of drilling fluids within their optimal performance margins is of utmost importance in running a safe and efficient operation when drilling oil and gas wells. Safety and efficiency may be adversely affected by current measurement practices of these two key properties as they still mostly rely on onsite personnel to carry out periodic manual measurements. These laborious measurement protocols limit the quantity and quality of the produced data. Here we describe an automated system for viscosity/density measurements of drilling fluids based on a tuning fork electromechanical resonator. The resonator works as the frequency-defining element of an oscillator circuit, and a digital gain control algorithm is used to control the feedback loop and actuate the device. The self-oscillating circuit allows the measurement to be repeated several times per second, enabling higher measurement precision and real-time monitoring in changing flows. The resonator was integrated into an in-tank system, which performed continuous measurements at the suction tank of the mud circulating system during drilling operations. Measurements in three different wells are presented. The results were in very good agreement with manual measurements reported from standard mud reports and further highlighted opportunities in detecting operational problems from the real-time data streams obtained.

Anticipating qualitative changes in the rheological response of complex fluids (e.g., a gelation or vitrification transition) is an important capability for processing operations that utilize such materials in real-world environments. One class of complex fluids that exhibits distinct rheological states are soft glassy materials such as colloidal gels and clay dispersions, which can be well characterized by the soft glassy rheology (SGR) model. We first solve the model equations for the time-dependent, weakly nonlinear response of the SGR model. With this analytical solution, we show that the weak nonlinearities measured via medium amplitude parallel superposition (MAPS) rheology can be used to anticipate the rheological aging transitions in the linear response of soft glassy materials. This is a rheological version of a technique called structural health monitoring used widely in civil and aerospace engineering. We design and train artificial neural networks (ANNs) that are capable of quickly inferring the parameters of the SGR model from the results of sequential MAPS experiments. The combination of these data-rich experiments and machine learning tools to provide a surrogate for computationally expensive viscoelastic constitutive equations allows for rapid experimental characterization of the rheological state of soft glassy materials. We apply this technique to an aging dispersion of Laponite^® clay particles approaching the gel point and demonstrate that a trained ANN can provide real-time detection of transitions in the nonlinear response well in advance of incipient changes in the linear viscoelastic response of the system.