AI IN PRODUCT OPTIMIZATION by Akshay C Gopal

 Product optimization is a common problem in many industries. In our context, optimization is any act, process, or methodology that makes something — such as a design, system, or decision — as good, functional, or effective as possible. Decision processes for minimal cost, best quality, performance, and energy consumption are examples of such optimization. Currently, the industry focuses primarily on digitalization and analytics. This focus is fueled by the vast amounts of data that are accumulated from up to thousands of sensors every day, even on a single production facility. Until recently, the utilization of these data was limited due to limitations in competence and the lack of necessary technology and data pipelines for collecting data from sensors and systems for further analysis. Within the context of the oil and gas industry, production optimization is essentially “production control”: You minimize, maximize, or target the production of oil, gas, and perhaps water. Your goal might be to maximize the production of oil while minimizing the water production. Or it might be to run oil production and gas-oilratio (GOR) to specified set-points to maintain the desired reservoir conditions. In most cases today, the daily production optimization is performed by the operators controlling the production facility offshore. This optimization is a highly complex task where a large number of controllable parameters all affect the production in some way or other. Somewhere in the order of 100 different control parameters must be adjusted to find the best combination of all the variables. Consider the very simplified optimization problem illustrated in the figure below. The fact that the algorithms learn from experience, in principle resembles the way operators learn to control the process. However, unlike a human operator, the machine learning algorithms have no problems analyzing the full historical datasets for hundreds of sensors over a period of several years. They can accumulate unlimited experience compared to a human brain. A machine learning-based optimization algorithm can run on real-time data streaming from the production facility, providing recommendations to the operators when it identifies a potential for improved production. A typical actionable output from the algorithm is indicated in the figure above: recommendations to adjust some controller set-points and valve openings. It also estimates the potential increase in production rate, which in this case was approximately 2 %. This machine learning-based optimization algorithm can serve as a support tool for the operators controlling the process, helping them make more informed decisions in order to maximize production.Fully autonomous operation of production facilities is still some way into the future. Until then, machine learning-based support tools can provide a substantial impact on how production optimization is performed