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Departments & Programs



Our research focuses on four main areas: computer-aided interpretation, permanent downhole gauges, temperature transient analysis, and machine learning in well test interpretation. Below are highlights from several ongoing and recently completed studies.

DTS temperature map (extracted from Sierra et al., 2008)

Automated Analysis of DTS Data

Dante Orta
The research is focused in applying Machine Learning to aid with the interpretation of DTS data. The goal is to automatically detect the presence of fractures from the data and give an estimation of flow from them. Having the capacity to automate some of the interpretation would allow for the use of fiber optic data to real time decision making.

Analyzing Fractures with Resistivity Data

Analyzing Fractures with Resistivity Data

Jason Hu
As a potential approach to augment and improve on the existing methods, resistivity measurements can be used to characterize fractures. Fractures saturated with water effectively reduces the resistivity of rock matrix and create opportunities to extract fracture characteristics by monitoring the change in resistivity distribution.

Machine Learning Applied to Multiwell Data

Machine Learning Applied to Multiwell Data

Tita Ristanto
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. 

Machine Learning in a Full-Physics Analysis

Machine Learning in a Full-Physics Analysis

Abdullah Alakeely
The preliminary objective of this research is to
investigate the possibility to represent and/or replace the numerical reservoir
simulation model using a proxy based on machine learning. The approach is
envisioned to take advantage of the recent progress in machine learning and
data mining approaches to help complement or replace parts of the functionality
that numerical reservoir simulation models provide. 

Effect of gauge placement on well testing

Distributed Temperature Sensing

Zhe Wang
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.

Permanent Downhole Gauges (PDGs)

Large Volume Data Processing for Permanent Downhole Gauges

Yang Liu
Permanent Downhole Gauge (PDG for short) is a newly developed tool for well testing in the petroleum industry. In traditional well testing, pressure and flow rate transient data are collected for a short period which leads to a large uncertainty. Due to long time  continuous data acquisition, a PDG may provide measurements for several years or longer. However, at the same time, this brings a new problem--a large volume of noise together with large volume of measurement.

Spatial distribution of temperature change at a time instant from DAT.1

Modeling reservoir temperature transients, and matching to permanent downhole gauge (PDG) data for reservoir parameter estimation

Obinna Duru
Over the last decade, permanent download gauges (PDGs) have been used to provide a continuous source of downhole data in the form of pressure, temperature and sometimes flow rate. The tools provide access to data acquired continuously over a large period of time and containing reservoir information at a much larger radius of investigation than conventional wireline testing.

Relating Time Series in Data to Spatial Variation in the Reservoir Using Wavelets

Abeeb Awontunde
Accurate description of the reservoir is crucial to reservoir management. Yet, due to the complex nature of reservoir heterogeneity, obtaining accurate description of the reservoir poses a big challenge.

Schematic diagram of the condensate-flow system

Flow Behavior of Gas-Condensate Wells

Chunmei Shi
Gas-condensate reservoirs experience reductions in productivity by as much as a factor of 10 due to the dropout of liquid close to the wellbore. The liquid dropout blocks the flow of gas to the well and lowers the overall energy output by a very substantial degree. As heavier components separate into the dropped-out liquid while the flowing gas phase becomes lighter in composition, the overall composition of the reservoir fluid changes due to the combined effect of the condensate phase behavior and the rock relative permeability.

In these graphs, the x axis shows the number of iterations and the y axis shows

A New Look into Nonlinear Regression

Aysegul Dastan
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.