Research

Analysis of Wells with Multistage Fractures
Both unconventional resources and enhanced geothermal systems are heavily reliant on hydraulic fracturing technology, specifically horizontal wells with multistaged fractures. This fracture network provides crucial pathways between two media, namely the rock matrix (low permeability with large volume) and the fractures (high permeability with minimal volume), to exploit subsurface resources. Hence, the understanding of this subsurface system (e.g., permeability, thermal conductivity, fracture geometry) is a key to optimize resources extraction process sustainably.
This study aims to investigate practical pressure transient analysis and temperature transient analysis methods for multiple horizontal wells with multistaged fractures.

Triggering mechanisms of induced seismicity and predictive modelling
Understanding how induced seismicity is triggered is important for safety of Enhanced Geothermal Systems. During the development of Enhanced Geothermal Systems, high pressure water is injected into the target formation to stimulate fracture network and enhance permeability. However, this process carries the risk of activating nearby faults and triggering large earthquakes.
This study aims to understand triggering mechanisms of induced seismicity using physics-based models and to propose solutions to mitigate potential seismic hazards.

Techno-Economic Evaluation and Optimization of Flexible Geothermal Power
Intermittent renewables (e.g., solar and wind) are characterized with diminishing effective load carrying capabilities, and cannot solely drive a reliable net-zero energy portfolio. Consequently, geothermal operators have been exploring flexible generation to supply dispatchable power. There is limited literature that investigated how dispatchable geothermal power can be achieved through steam vent-off , wellhead throttling, turbine bypass, storage, etc. However, these methods involve various technical and economic challenges. This research investigates the techno-economic viability of flexible geothermal power generation.

Scale Buildup Detection and Characterization in Production Wells by Deep Learning Methods
Inorganic scale results from brine solution deposition onto the surfaces of the reservoir, wellbore and other production facilities. In this project, we focused on studying scale deposition in the wellbore, which can introduce decrease in tubing ID and induce abnormal production declines and pressure drops. There are limited recent studies focusing on scale deposition detection, while most of them only managed to detect the problem after considerable production declines. Therefore, as the objective of this work, we tried to implement deep learning methods to detect the scale deposition immediately after its occurrence, and to estimate the magnitude of the scaling to help with the further design of its removal.

A New Look into Nonlinear Regression
Well testing provides a set of useful tools to estimate reservoir parameters. Based on the estimates, it is possible to forecast the reservoir behavior for future production and hence a reservoir can be utilized in an optimal way. Nonlinear regression was introduced to well testing more than two decades ago and revolutionized well testing with new methods and substantially improved the accuracy of the prediction.

Distributed Temperature Sensing
Distributed Temperature Sensing is a newly developed measurement technique, and was introduced into the oil industry in recent years. DTS can provide high resolution, real-time and continuous temperature information along a wellbore. With the increasing need for monitoring reservoir production, more and more permanent downhole gauges are installed in oilfields. Compared with traditional measurement tools (e.g. PLT), DTS has many advantages, making it more suitable for permanent installation.

Machine Learning Applied to Multiwell Data
This research focuses on solving multiwell problems using machine learning approaches with Python. So far, the study has identified important features in a single-phase multiwell problem. This study will explore several different machine learning algorithms and compare them in terms of accuracy and performance. Python is very helpful for this purpose as it has a lot of machine learning libraries to choose from and is handier in solving machine learning related problems.

Monitoring and Control of Multilateral Wells
A smart (or intelligent) well is a nonconventional well that is completed with downhole instruments such as pressure and temperature sensors coupled with donwhole control devices. The smart well can be a segmented, single horizontal well where every segment is controlled by independent control valves or a multi-lateral well where each lateral is controlled separately.