Doctor of Philosophy (PhD)


Petroleum Engineering

Document Type



In the oil and gas industry, distributed fiber optics sensing (DFOS) has the potential to revolutionize well and reservoir surveillance applications. Using fiber optic sensors is becoming increasingly common because of its chemically passive and non-magnetic interference properties, the possibility of flexible installations that could be behind the casing, on the tubing, or run on wireline, as well as the potential for densely distributed measurements along the entire length of the fiber. The main objectives of my research are to develop and demonstrate novel signal processing and machine learning computational techniques and workflows on DFOS data for a variety of petroleum engineering applications. This includes distributed pressure sensing using distributed temperature sensor (DTS) and distributed acoustic sensor (DAS) data, automated gas rise velocity detection, multiphase flow characterization, and fluid interface detection. The information obtained with DFOS installed in oil and gas wells contributes to improved efficiency, safety, and ultimate recovery.

In this study, fiber-optic DAS and DTS data are applied for the first time on a big scale to predict pressure. The data pattern was characterized using a combination of machine-learning and signal processing approach, and the created model was subsequently applied to anticipate pressure data at various depths. Additionally, it was shown how low-frequency DAS and DTS might be utilized to track distributed pressure on a well-scale. In addition, a novel methodology was developed to automatically estimate the real-time gas influx velocity into a wellbore, which offers a significant improvement over current approaches in which the gas velocity is largely identified by surface-based methods that suffer from time-delay issues. Furthermore, two independent techniques (velocity band energy plus downhole pressure data and speed of sound) were used to estimate the gas void fraction. Gas void fraction is critical for flow characterization in different calculations such as two-phase viscosity, density, and pressure drop. A new analysis workflow is presented to delineate the gas-liquid interface in the presence of background noise caused by pump vibrations. Lastly, the effect of optical losses, fiber degradation, and noise in DFOS data quality are evaluated and quantified. In the final chapter of this dissertation, the overall findings of my work are summarized, and recommendations for future work are presented. The workflows and methods presented in this research will be beneficial to improve and validate fiber-optics-based interpretations and applications in petroleum engineering applications, including pressure prediction, flow characterization, and automated detection.



Committee Chair

Sharma, Jyotsna

Available for download on Friday, December 22, 2023