The oil and gas industry has been going through a tumultuous time of late. With volatile crude oil prices and geopolitical trends putting pressure on supply, it is becoming imperative for oil and gas companies to manage costs through operational effectiveness and minimize any production hurdles due to unplanned downtimes and unforeseen breakdowns.
Before making production decisions, organizations must understand the complex beast that is upstream operations with data points to analyze, including seismic and geological data to understand the ground conditions; oil quality data to determine gas oil ratio, water cut and submergibility; and pump calibration to ensure that it is optimized for the given conditions. Too much pressure on the pump and it is likely to break, too little pressure and it is being underutilized.
Technology is likely to be a top disruptor in the future of oil and gas operations for this very reason. IoT sensor data analytics and machine learning will enhance the machine interface and improve the effectiveness of brown-field setups. But what really comprises of a true intelligence system that is likely to disrupt this highly complex industry?
The new avatar of data analysis
There has never been a dearth of data usage in oil and gas operations. Even before data science became cool, there was a tremendous amount of statistical research that was being utilized to understand seismic and geological data and manage oil field operations efficiently. Data has always been the backbone of decision making in the oil and gas sector.
With the advent of data technologies that can handle scaling and machine learning to help operations teams and scientists make sense of the data, new-age intelligence systems are also starting to become top priorities in the long list of digital transformation initiatives.
Extracting the unknown unknowns
There are a number of prebuilt models that are used to determine the oil quality and calibrate well equipment. By feeding information into these models, field engineers have a good idea of the way the well is operating.
Machine learning starts to surgace the unknown unknowns. Machine learning makes the existing setup more sophisticated by analyzing multivariate patterns and anomalies that can be attributed to past failures. Moreover, the analysis patterns are derived from several years of data to reduce any inherent bias. Machine learning alone cannot answer all analysis questions. It is one piece of the puzzle and enhances existing knowledge acquired through years of research.
Constituents of a new-age intelligence system
The speed at which organizations receive data and conduct analysis is of the utmost importance. Hence, a sophisticated decision system needs to deliver insights quickly and with tremendous accuracy. A disruption in an oil well can cause a revenue loss as high as $1 million per day.
A true decision support system should have IoT infrastructure, real-time monitoring systems, supervised learning models and unsupervised learning models. IoT infrastructure includes low power sensors, gateways and communication setups to ensure that all aspects of well operations are connected and providing information in near real time. Real-time monitoring systems allow constant monitoring of the assets driven by key performance indicators and look for any issues or spikes that can be caught by the naked eye. Typical scenarios that real-time monitoring systems would cover include existing oil production, temperature and pressure of the well pumps and seismic activity around the well site.
Supervised learning models predict for known patterns and issues. These rely on past information of failures and models that have been honed over time in experimental and production setups. Organizations can use models for predictive maintenance of the pumps and pump optimization for higher productivity. Unsupervised learning models look for anomalies and possible signs of degradation. They utilize complex multivariate pattern data to determine correlations and possible deviations from normal behavior. Unsupervised models determine multivariate correlations between productivity and operational parameters using neural networks and identify early signs of pump degradation using time series analysis and anomaly detection to reduce the probability of a pump breakdown.
It is difficult to rely on one type of system. Constant improvements require a combination of human intelligence and machine intelligence. Due to the plethora of prior knowledge available to run oil wells effectively, machine learning and big data technologies provide the right arsenal for these systems to become even more sophisticated. A new-age intelligence system becomes a combination of known knowledge through existing models and unknown patterns derived from machine learning.
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