The goals of the proposed 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 and develop a semiautonomous active control system, and 3) assess various business case scenarios to accelerate the development and application of this system for commercial EOR.
University of North Dakota Energy and Environmental Research Center (UNDEERC) - Grand Forks, ND 58202
NCS Multistage LLC – Houston, TX 77070
North Dakota Oil and Gas Research Program (OGRP) – Bismarck, ND 58505
North Dakota Geological Survey (NDGS) – Bismarck, ND 58503
Schlumberger – Houston, TX 77070
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.