Machine learning for oil & gas engineering – the digital gold mine
With advances in PLC and DCS technology, data collection in oil and gas process control systems has become cheaper and more plentiful in recent years, and this looks set to continue. Within these vast data stores, lies a gold mine of information that can help us better control, diagnose, and understand industrial systems.
This enormous potential has gone largely untapped due to the difficulties involved in translating the right mathematical tools to industry in an easy-to-use form. Particularly noteworthy in this respect are machine learning techniques that have, in fact, been advancing in parallel to this technology and have already proven to be a breakthrough in other applied sciences.
The right machine learning tools – tailored uniquely to address process control issues such as noisy data and feedback delay, and made simple enough to supplement everyday engineering work – could provide very cost-effective, scalable means of improving systems. Stable control, reduced equipment wear-and-tear, and improved reliability, profitability, and sustainability would be within reach.
An approach to harnessing machine learning in improving process control systems is illustrated in the diagram below:
Input and output data from the process is drawn from the PLC or DCS system and machine learning techniques are used to create a best-fit trend or model that captures the key characteristics of the process and the relationships between its inputs and output. This is summarized in simple equations that allow for integration of the model information with the process PID control, for rapid and real-time stabilization of the process.
The challenges of building best-fit models for process control data have always been:
- The mathematics of the model-building must be easy to use and understand.
- Real-world usability
- Models must reflect the real-world properties of the process and capture its trends accurately.
- Models must be able to be integrated into the means of process improvement – in this case, the PID control algorithm.
The new software tool built to address these challenges – Conjecture System Identification, will be launched at AOG 2019. It provides an intuitive interface for building best-fit trends from industrial process data, capturing process dynamics in terms of inputs, output, and time. Conjecture System Identification is an inroad to the digital gold mine of your industrial systems, allowing for a simple digital representation of these systems for better plant diagnosis ,understanding, and improved automated control.
Conjecture Data Solutions at AOG
Conjecture System Identification is built and licensed by Conjecture Data Solutions, a startup and newcomer to AOG focused on creating data-driven software solutions for engineering. Come visit them at Stand H37 within the Instrumentation Control and Automation Zone, where they will demonstrate using real-life mini-plant systems how their best-fit data trending is used to dramatically stabilize PID control, and how this can improve productivity and profitability for your business.