R&D Projects


  Distributed Fiber-Optic Sensing Research

 

Distributed Fiber-Optic Sensing (DFOS) technology enables continuous, real-time measurements along the entire length of a fiber optic cable that can be deployed along entire wellbore.  This growing technology provides a reliable, cost-effective solution for many borehole geophysics/petroleum engineer applications. DFOS technology includes Distributed Acoustic Sensing (DAS), Distributed Temperature Sensing (DTS), and Distributed Strain Sensing (DSS). At RCP, we are working to improve the acquisition, processing, interpretation, and modeling of all types of DFOS data. The current active research topics include: 

      • Time-lapse VSP
      • Microseismic-based imaging and inversion 
      • DAS tube-wave analysis 
      • DFOS-based flow monitoring 
      • Low-frequency DAS cross-well strain interpretation and inversion 
      • DTS warmback modeling/inversion

Click the links below for more information

Inter-stage Time-lapse VSP

Reservoir elastic property changes induced by hydraulic fractures can be monitored by time-lapse DAS VSP immediately acquired at each stage. Using field data acquired in Midland Basin, we performed quantitative analysis on the time shift measurements and P-S scatter waves. We developed processing workflows to estimate fracture height and fracture closure time for each individual stage. Cross-well fracture responses are also analyzed to constraint fracture properties away from injectors.  

DAS Microseismic Analysis

Due to dense spatial resolution and large aperture, DAS provides great opportunities to extract more information from microseismic waveforms, and provides further constraint on reservoir and hydraulic fracture properties.

We use microseismic-induced guided waves to characterize unconventional reservoirs. Guided waves are seismic waves that are trapped by low-velocity layers. They are dispersive and behave similarly to surface waves. We successfully use guided-wave waveforms to estimate Eagle Ford reservoir thickness and shear velocity with high accuracy. The guided-wave energy can also be used to constrain microseismic event depth. The developed algorithms can significantly increase the value of DAS-based microseismic acquisition.

 

We also observed near-field energy of microseismic events in the DAS data. Near-field term is represented as a low-frequency signal between P and S waves. It is highly sensitive to the moment tensor orientation and to the low-frequency component of the source time function. We are investigating its potential for moment tensor inversion and source function estimation.

DAS tube-wave analysis

Perforation shots induce high-energy tube waves in the borehole during completion. Strong decay of the tube-wave energy can be observed in the stimulated wellbore sections, in strong contrast to the not-yet stimulated sections. The tube wave spatial decay rate is a strong indicator of near-wellbore connectivity and hydraulic properties.

 

Flow monitoring

DAS and DTS can capture the vibration and temperature signatures induced by borehole fluid, which leads to applications including production logging, gas lift monitoring, and pipeline monitoring. Working with FAST consortium and Dr. Yilin Fan’s research group, we constructed several flowloop experiment sites to investigate DAS and DTS responses under different phase combinations, flow regimes, and pipe (borehole) inclinations. The project goal is to estimate flow rate and phase volumetric frictions from DFOS data. We are currently constructing a 200-m horizontal flowloop at the Colorado School of Mines Edgar Mine research facility. The flowloop includes a 40-meter borehole section, which provides an ideal testing environment with stable temperature and low environment noise.

 

  Multi-Component e-FWI applications

 

The objective of this work is to improve reservoir characterization by incorporating horizontal component data into elastic Full Waveform Inversion (FWI). Traditional FWI workflows often rely primarily on vertical component and hydrophone data. By integrating horizontal components, the inversion becomes more constrained and better able to resolve parameters such as S-wave velocity. RCP hopes to demonstrate that a multi-component elastic FWI framework can meaningfully improve reservoir characterization beyond what is achievable with conventional acoustic or single-component approaches. Datasets that will be utilized for this research include TGS’ Ultra-Long-Offset data from the Gulf of Mexico and Chevron’s Gorgon Field long-offset seismic data from offshore Australia.

Gorgon Field long offset seismic, offshore Australia, Chevron

Machine Learning and Data Analytics 

 

Over the past decade, the volume and variety of data collected in conventional and unconventional fields has increased tremendously. Although this growth has enabled better insight, processing and analyzing these datasets has become more complex and time consuming. This problem is not unique to oil and gas, which is why technologies related to data analytics, machine learning, and automation have risen in demand over recent years across almost all industries. To increase both the quality and speed of data processing/analysis for geologists, geophysicists, and petroleum engineers, RCP is working to develop new, enhanced workflows that leverage the capabilities of machine learning.

RCP’s current research focuses on the development of advanced computational frameworks for seismic wave propagation and full-waveform inversion (FWI). On the classical computing side, we develop GPU-accelerated elastic FWI solvers using strain-velocity and stress-velocity formulations to enable efficient large-scale simulations for field-data applications. These implementations leverage modern high-performance computing architectures and numerical frameworks such as Devito and SPECFEM to solve the elastic wave equation with high accuracy and scalability. In parallel, we investigate machine learning and quantum computing approaches for FWI. These studies explore how data-driven models and quantum and hybrid quantum-classical algorithms can improve the efficiency of inversion workflows and provide new strategies for solving nonlinear inverse problems. Together, this work aims to advance both the computational performance and algorithmic capabilities of seismic imaging methods for complex subsurface environments.

    Inverted calculated velocity model from iterated velocity analyses. (H. Nguyen, 2025)

    carbon capture utilization and storage for the energy industry

     

    Carbon Capture, Utilization, and Storage, or CCUS, is a key technology for tackling climate change and continued operations of the oil and gas industry. The ambition of net-zero emissions by 2050 is practically impossible to be achieved without CCUS. CCUS technology is to separate CO2 from a gas stream, compress it to dense-phase form, transport it via pipeline and inject it into geological formations, including oil and gas fields, deep saline aquifers, or coal beds. There are now 26 commercial CCUS facilities in operation globally. Funded by DOE, our goal is to initialize large-scale and commercial CCUS projects in Colorado. At this time, we have identified the 10 largest CO2 sources in Colorado and potential storage locations. We are in discussions with several of these operators to conduct a front-end design and economics study on sequestration and EOR options. This is a very new and exciting project, potentially a new industry, and a source of revenues.

    Economics study showing representative values that CO2 EOR can generate up to $10/ton of CO2 profit from coal plants accounting for tax credits and revenues (left). Top 10 CO2 emission sources and sinks in Colorado. CO2 can be captured from the sources and then stored in the nearby basins via short pipeline (right).

     electrical reservoir stimulation for geothermal applications

     

    Utilizing injection and fall-off pressure transient analysis to estimate subsurface permeability before and after electrical reservoir stimulation in a hard rock geothermal reservoir to quantify the magnitude of permeability enhancement. The interpretations will be corroborated using history-matched numerical modeling of fluid flow to reduce uncertainty and provide a defensible assessment of stimulation effectiveness. Electrical Reservoir Stimulation (ERS) is a novel stimulation method developed by Eden Geopower that uses high-voltage electrical pulses delivered through electrodes to fracture hard rocks and potentially increase subsurface permeability. This ERS technology can generate targeted and distributed rock damage and microfracturing that may improve injectivity, while reducing water consumption, curbing carbon emissions, and mitigating the seismicity risks associated with traditional hydraulic fracturing. We also deployed downhole fiber-optic cables to capture strain and temperature changes during the reservoir stimulation.

    Electrical Reservoir Stimulation, Eden Power