There is a need for embedded sensor technologies to monitor wellbore integrity in real-time for carbon storage and geothermal applications. Emerging sensing technologies such as optical fiber sensors and wireless sensors have been studied for physical parameter monitoring (e.g. temperature, vibration, and strain) and chemical parameter monitoring (e.g. pH, CO2, corrosion) to monitor structural health of the wellbore. The desirable sensors need to be able to withstand the harsh environments relevant for carbon storage and geothermal wellbores, and they must not inadvertently cause potential sources of wellbore failures. Therefore, we investigated the cement properties with embedded sensors to compare with baseline cement properties, including porosity, permeability, mechanical properties (e.g. Young’s modulus, Poisson’s Ratio, etc), and 3D computed tomography (CT) scans. The sensor devices (optical fiber sensors [OFS] and wireless chip sensors) were embedded in cement cores under wellbore relevant conditions. Then, the cement samples were examined using AutoLab 1500, nitrogen permeability testing, helium porosity testing, and 3D CT scanners. Results show that the cement samples with embedded sensor devices had a slight increase in porosity of 1.5% to 3.6% compared to the blank cement samples. Permeability slightly increased by 0.001 mD with embedded chip sensors. The embedded chip sensors did not significantly change the cement mechanical properties; whereas, the embedded OFS prototypes improved the cement mechanical strengths, e.g. increasing the Young’s modulus by as much as 10% and the bulk modulus by up to 25.5%. CT scans confirmed the proper embedding and good bonding between sensor devices and cement.
Radiation and detection of ultra-short terahertz pulses with picosecond duration advance a variety of applications, including imaging, spectroscopy, and wireless communication. Silicon-based integrated circuits can replace bulky, expensive femtosecond lasers with low-cost solutions to generate and detect THz pulses with GHz repetition rates. In this paper, we present laser-free fully electronic THz pulse sources and detectors to radiate and detect broadband frequency combs in mm-wave and terahertz regimes. A THz pulse radiator chip based on the reverse recovery of a PIN diode is presented. This chip radiates pulses with a tunable repetition rate that can go up to 10.5 GHz. In the frequency domain, the radiated pulses generate a frequency comb that extends up to 1.1 THz. The spacing between the THz tones can be tuned by changing the repetition rates of the pulses to cover the desired frequency range. In addition to the THz comb source, a broadband frequency comb detector chip is presented. The detector chip uses a tunable frequency comb as a reference to sense the spectrum over a wide bandwidth. Single-tone measurements were performed using the detector from 50 GHz to 280 GHz. The source and detector technologies are used to implement a dual-comb sensing system, in which the mm-wave/THz frequency components of the radiated combs are compressed to a small bandwidth in the RF regime.
Performing feature extractions in convolution neural networks for deep-learning tasks is computational expensive in electronics. Fourier optics allows convolutional filtering via dot-product multiplication in the Fourier domain similar to the distributive law in mathematics. Here we experimentally demonstrate convolutional filtering exploiting massive parallelism (10^6 channels, 8-bit at 1kHz) of digital mirror display technology, thus enabling 250 TMAC/s. An FPGA-PCIe board controls the ‘weights’ and handles the data I/O, whereas a high-speed camera detects the inverse-Fourier transformed (2nd lens) data. Gen-1 processes with a total delay (including I/O) of ~1ms, while Gen-2 at 1-10ns leveraging integrated photonics at 10GHz and changing the front-end I/O to a joint-transform-correlator (JTC). These processors are suited for image/pattern recognition, super resolution for geolocalization, or real-time processing in autonomous vehicles or military decision making.