Our research collaborators and sponsors include:


Fiber Optic Sensing

Over the past few years, the oil and gas industry has seen a rapid increase in the deployment of optical fiber-based distributed acoustic sensors (DAS) and distributed temperature sensors (DTS) for real-time downhole monitoring in both onshore and offshore operations. Distributed fiber-optic sensors can provide real-time information, simultaneously along the entire fiber length. This technology can be used for leak detection and well integrity monitoring, injection/production profiling (to understand where the fluids are coming from), sand detection, monitoring fractures, sand detection, etc. Research in our group focuses on developing novel signal processing and machine learning workflows for analyzing fiber optic data. Ongoing projects involve DAS and DTS experiments in the well-scale PERTT facility (at LSU) and surface flow-loop testing at Oak Ridge National Lab and at LSU. Dr. Sharma is collaborating with a number of major E&P companies. To further expand the capability of current fiber optic systems, we are developing custom engineered fiber, quantum laser sources and nanomaterial enhanced sensors.

waterfall plot of fiber optic distributed acoustic sensing data

 (a) Waterfall plot of high-frequency DAS Frequency Band Energy (b) Low-frequency DAS gradient w.r.t. time (c) Frequency-Wavenumber Plot

Hydrocarbon Mapping Using Satellite Gravimetry

In an ongoing project in collaboration with NASA, we are utilizing satellite-based and land-based gravity data for hydrocarbon mapping using super-resolution image enhancement and machine learning techniques.

Bouguer gravity anomaly plots measured from satellite and land based gravimeters (a) Satellite based gravity earth model using GRACE data (b) Land-based gravity data from USGS.

Enhanced Oil Recovery

Dr. Sharma has over eight years of industry experience in Enhanced Oil Recovery (EOR) through her previous work at Chevron, Shell, and Schlumberger. Ongoing EOR projects in our group include analysis of fireflood (in-situ combustion) operations in Louisiana (figures below), steam injection and production profiling in steamflood and cyclic steam injection operations, and gas injection in shale reservoirs.

Location of fireflood operation in Bellevue field in Louisiana and typical temperature profile from air injector (a) Location of fireflood operation in the Bellevue oil field in Louisiana (b) Typical temperature profile in an air injector reaching above 800 degF.

Machine Learning

We are utilizing machine learning, data mining and numerical optimization techniques for a variety of applications in the oil and gas industry. In an ongoing project (figures below), we used random forest algorithm to predict time-lapse saturation profiles using actual field injection and production data from a structurally complex, heterogeneous, and heavily faulted offshore oil field achieving high accuracy.

3D reservoir view and cross-section showing the location of wells and results from machine learning model for predicting oil saturation profiles. (a) 3D reservoir view showing the horizontal permeability and structural location of wells (b) Intersection of well paths with faults (c) Predicted versus the actual time-lapsed oil saturation profiles.

Online Portal for Oil and Gas in Louisiana

The energy industry in Louisiana is one of the biggest in the U.S. Louisiana is one of the top five natural gas-producing states in the U.S. and one of the top 10 states in both crude oil reserves and annual crude oil production. In collaboration with the Louisiana Department of Natural Resource and LSU Libraries, we have developed an easy-to-use online web-portal on a GIS platform to visualize the oil and gas resources in Louisiana. Currently we are utilizing data mining techniques on the production database to decipher production trends and identify optimization opportunities. The portal is available at 

online webportal showing the location of oil and gas field and wells in Louisiana