The upstream activities of discovery and extraction of new reserves involve increasingly more complex analyses as resources become sparser and more difficult to reach. Downstream processing requires better analytics and optimization as companies seek to reduce operational costs and the impact on the environment. Engineers and scientists choose MATLAB® and Simulink® products to aid them as they:
In order to discover and access new reservoirs and maximize output from existing reservoirs, oil and gas companies are drilling more complicated, deeper wells in more hostile environments.
Companies worldwide rely on MATLAB and Simulink products for data analysis, mathematical modeling, and parallel computing to develop, integrate, and accelerate the transformation of seismic data, well data, and operational data into detailed models critical for timely decision making. They use these models to identify candidate drilling locations, optimize well placement and extraction processes, and perform reservoir simulations to develop deeper insights into reservoir behavior and productive capacity.
Upstream and downstream processes involve a variety of instruments that collect data for online and offline decisions and supervisory control actions. Process specialists rely on MATLAB as a platform for analyzing static and streaming data sets and turning them into clear and actionable information.
MATLAB, with its ODBC database support and OPC connectivity, helps users tap into multiple data sources and develop sophisticated algorithms and process models for better insight into plant performance. Process engineers can easily share results across the organization, from the control room to headquarters, using deployment and Web access capabilities.
Engineers in the process industry use MATLAB and Simulink to minimize operating cost and increase process efficiency by developing and tuning improved control system strategies. They accomplish this by developing or importing first-principle process models and using system identification to create process models from input-output test data.
With these process models, engineers can quickly design and test various types of control algorithms ranging from PID to model predictive control against process simulations. They can leverage classical control design approaches combined with the use of numerical optimization methods to improve the process system response.