Authored by : Naveen Sikka, Principal Industry Consultant- Oil &Gas, SAS India.
Companies today are vehemently talking about AI and ML technologies and IT companies, be it a start-up or super majors, are already providing specialized offerings around these technologies. However, the extent and complexity these new age technologies address vary from individual companies’ outlook.
Over the years, the inherent expectations from the oil and gas business is to build an error free application which requires good amount of feature engineering considering multiple variable factors that affect the expected output. This requires large amount of interaction between the business domain and technology domain. The resources involved from each domain should be well versed with the boundaries within which the solutions need to be developed and should focus on providing the relevant data required for doing deep learning. The variety and veracity of data considered for the error-free solutions need considerable time as well as resources. Since the oil and gas industry is deeply interconnected right from the exploration, drilling and production of oil and gas to transportation, refining and distribution of oil therefore data exchanges or data collection and management becomes the most import factor to forecast, predict and optimize the output variables that need business attention in times to come.
The need of the hour is to build AI and ML applications solving real life business issues. The issues faced within oil and gas industry could range from compliance, emissions, supply-demand fluctuations, price volatility, material availability, energy efficiencies, production efficiencies, supply chain etc. A wrong decision can prove to be very costly, but a correct decision is highly valuable.Therefore, it is imperative that emerging technologies in predictive analytics, drones, sensors, cloud-based systems, AI, IoT, robotics, software, and hardware automation need to collaborate to improve operations by reducing costs, and increasing safety, efficiency, and speed of the processes.
The digitization of oil and gas pipelines, refineries, infrastructure, field and exploration sites shall bring all the related data on a single platform. This can detect and convey data such as movements, vibrations, leakages, corrosion, and others. Using advanced data analytics tools shall help in converting field/market data into actionable data within the oil and gas companies that can detect patterns and get insights into potential defects or failures.
Most of the oil and gas companies either in Upstream, Midstream and Downstream domain have been in operation for quite many decades. As industry norms can have it and due to its inherent nature of operations, over the years these companies have invested heavily on system of records.
With skilled manpower availability within the oil and gas industry, in the past complex decision making has been fairly achieved. However over a period, with the ageing manpower, the decision-making trend is shifting towards data backed decisions.Companies use analytical and artificial intelligence solutions to process the data which gives them the ability to make decisions much faster than humans do manually. Advanced data technologies such as AI and ML offer oil and gas companies a means to solve this. To cope with this need it becomes critical for oil and gas companies to invest in emerging technologies for reducing risks, increasing productivity and optimizing costs which are of prime concern – be it Upstream, Midstream or Downstream domain. Modelling and simulation enable the analyst to assess millions of possible scenarios to decide whether the E&P, Transportation, Refining, Distribution and Retail operation can be performed in an efficient and cost effective manner.
If you see a pattern in your data, be it in structured format or unstructured format, these emerging technologies help you in performing the most optimized action within your operations. With AI and ML, the action is not learnt; it is predetermined!
