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Improving Enhanced Oil Recovery Performance Through Data Analytics and Next-Generation Controllable Completions
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The overarching goals of the project are to 1) implement controllable completions through a rigorously monitored field test in a reservoir that has undergone primary and secondary recovery but has yet to pursue tertiary recovery, 2) apply advanced data analytics and machine learning to evaluate the test performance in tandem with a semiautonomous active control system, and 3) assess various business case scenarios to accelerate the development and application of this system for commercial enhanced oil recovery (EOR).

The project team will achieve these goals through the following project objectives: 1) identify a CO2-EOR pilot test well pattern in the Cedar Hills South Field; 2) install a set of up to ten interval control valves (ICVs) into the CO2 injection well within the identified test pattern; 3) execute a tracer study using ICV interval-specific tracers to quantify connectivity within the reservoir and inform the subsequent operational designs; 4) operate the ICVs during the project period of performance and quantitatively show that the deployment of the ICVs can improve conformance, increase CO2 sweep efficiency, and improve incremental production; 5) collect downhole measurements which, when combined with analytical and numerical simulation models, can provide the empirical data necessary for developing a machine learning approach to a semiautonomous control system; 6) collect baseline and repeat three-dimensional (3D) seismic surveys of the test pattern to characterize the reservoir and track subsurface fluid migration in response to CO2 injection and ICV system operation; and 7) evaluate various business case scenarios using simulation models to quantify key EOR performance metrics and the effect of ICVs on these metrics.


University of North Dakota Energy and Environmental Research Center (UNDEERC) - Grand Forks, ND 58202

Conceptual Tracers
Conceptual ICV pilot test design, including one lateral injection well equipped with an ICV system straddled by two lateral production wells with open-hole completions. The multicolored gas-phase tracer patches indicate different tracer species injected in each ICV interval.

The Energy & Environmental Research Center (EERC) and project partners will field-test an advanced machine learning approach integrating controllable completions (interval control valves [ICVs]) to enable active well control during carbon dioxide (CO2) enhanced oil recovery (EOR).


The outcomes of successful completion of this next-generation approach will be to 1) reduce perceived risks of deploying semiautonomous controllable completions technology; 2) quantify how the approach will lower net CO2 utilization and increase oil recovery with fewer wells, which will lower infrastructure costs and improve overall EOR project economics; and 3) develop economic (business) cases for implementation of this approach applicable to a wider range of reservoirs and fields.

Accomplishments (most recent listed first)
  • Field test site has been established.
  • Preliminary Reservoir Model has been created.
  • Baseline 3D seismic survey of the test pattern was acquired, processed, and interpreted
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DOE Contribution


Performer Contribution


Contact Information

NETL – David Cercone ( or 412-386-6571)
UNDEERC – Nicholas Azzolina ( or 701-777-5120)