Modeling the drilling process allows us to understand the physics driving our systems. Proposed tools and procedures can be tested without the time and risks of rig trials. In the near future, it will be inconceivable to put a new tool in the ground or new control system on a rig without fully testing the full system for performance and stability.

ExxonMobil challenged the industry, contributed models, and gathered a coalition of experts to start this effort. Additional models have been submitted by Scientific Drilling, NORCE, Texas A&M, and the University of Calgary, with more coming. MathWorks has helped convert the initial ExxonMobil code to Simulink^®, improve code stability, optimize the execution speed, and document how to use the models. All models, data, and test cases are freely available for academic and commercial use.

The University of Calgary is coordinating the organization’s web and GitHub sites. To join this effort, go to the Open Source Drilling Community and add your contact information to the mailing list on the Contribute tab.

Modeling geological storage of carbon dioxide is characterized by scarce data, large spans in spatial and temporal scales, and delicate balances between different physical flow mechanisms. The MATLAB^® Reservoir Simulation Toolbox (MRST) offers a set of simulators and workflow tools that have been specially designed to meet these challenges. The software combines results from more than a decade of academic research and development in CO2 storage modeling into a unified toolchain that is easy and intuitive to use.

You’ll see a demonstration of functionality from MRST applied to studying hypothetical carbon storage in large-scale aquifer systems from the Norwegian continental shelf. You’ll also discover tools and GUIs which can be used in a workflow from regional scale estimates to a detailed characterization of a specific storage site, including:

• Static capacity estimates
• Basic analysis of CO2 trapping mechanisms at a specific reservoir
• Interactive simulation of CO2 injection where various simulation parameters (well locations, injection rates, and boundary conditions) can be varied through a GUI
• Detailed simulation of a specific CO2 injection site

The reference application is free to download from the MathWorks website.

This talk presents the design and implementation of a nonlinear model-based adaptive robust controller (ARC) for tool motion control, driven by a hydrostatic transmission used in an oil and gas wireline operation. A detailed physical system model was built for controller design and testing. The ARC controller was designed to address both parametric uncertainties and uncertain nonlinearities inherent in the nonlinear system dynamics. The controller software development and testing followed a Model-Based Design procedure. A microservice architecture based on Docker containers was adopted for the controller software which facilitated continuous integration and deployment. The preliminary testing results show the effectiveness of the ARC controller design.