Disrupting Oil & Gas - With the Big Data Tsunami

Digital disruption in Oil & Gas is about understanding how to harness the changes brought about by digital technologies and incorporating them into the business strategy

Disrupting Oil & Gas - With the Big Data Tsunami

Data is the new ‘oil’ and the Oil & Gas industry is at the cusp of an ongoing digital transformation dealing with its key differentiator ‘Data’. While it can be argued that the Oil & Gas industry has always been about data, recent advancements in Cloud Computing, IoT, Analytics and Automation has brought numerous opportunities for transformation and created business value that was not possible even 5-6 years back.

The rise of digital disruption is not new to the industry, however what is truly transformative is the ability to weave data from multiple sources in near real-time that provides a 360-degree view of operations from Exploration, Production to Refining, Supply Chain and Marketing. This creates numerous opportunities around enhancing productivity, reducing equipment downtime, extending life of oil wells to optimized transportation models to name a few. With the increasing impact of oil prices on the sustainability of the industry, there is a push towards use of Machine learning and Artificial Intelligence (AI) in Oil Exploration. Recently, ExxonMobil has tied up with the Massachusetts Institute of Technology (MIT) for design of AI robots for hydrocarbon seep detection, a technique used to identify and confirm hydrocarbon presence in ocean basins.

 

Transforming Oil with the Data Deluge

Oil & Gas companies are truly disrupting the industry by building digital ecosystems with vendors and suppliers. Let us review the value chain of an integrated Oil & Gas enterprise to visualize the disruptions that digital technologies are bringing.

The industry can be broken into 3 segments – Upstream (companies dealing with exploration, development of oil fields and production operations, for example ONGC), Mid-Stream (companies involved in transportation of crude oil, storage and distribution to downstream industry, for example Transneft) and Downstream (companies that have refining operations or produce Petro-chemicals and distribute and market petroleum products or Petro-chemicals to wholesalers and retailers, for example HMEL)

Automation in Exploration - One of the challenges that the oil industry faces are the risks associated with exploration and the safety issues in the exploration environment while drilling. Automation is transforming drilling by eliminating manual intervention in pipe handling and pressure drilling. Weather monitoring can also be automated by use of sensors which can help understand potential outages due to events like earthquakes or hurricanes.

Exploration Operations Optimization - To reduce the risks associated with the high cost of exploration, companies are continuously looking at ways to optimize exploration operations. Use of Big Data Analytics tools help identify and map new oil reservoirs as well as identify potential drilling locations.

Drilling Optimization - Use of IoT sensors enable the drilling equipment to provide real time data of drill operations that can enable supervisors to monitor drill performance and optimize drill operations through real-time Dashboards.

Risk Assessment - One of the key risks faced in the Upstream business is the viability of oil fields and life of oil fields. Increasing use of Big Data Analytics and Machine Learning is helping Upstream companies manage the exploration better, based on drilling data combined with data from other sources like seismic surveys, geological mapping to manage the exploration within budgets. Even for operational oil wells, techniques for enhancing oil recovery using digital technologies are being leveraged to extend life of wells.

Asset Maintenance - IoT and Big Data Analytics help in collecting and analyzing data from equipment sensors in near real-time which help in planning maintenance schedules and reduce equipment downtime, this results in more optimized operations.

Pipeline Risk Assessment - One of the key risks around transportation of crude oil from production wells to depots and refining plants is leakage/spills. The other areas of risk include seismic risks if the pipeline passes through a seismic zone and internal/external corrosion of pipes over time. With sensors used in leakage and measuring pressure, real time analytics can be performed helping companies plan maintenance and optimize costs.

Transportation Optimization - With crude prices constantly in flux, managing transportation of crude through pipelines, ships or railways becomes crucial. Complex analytical models help derive route planning and mode of transport for optimization of transportation costs.

Storage Optimization - Storage optimization is crucial both for crude oil storage in terminals and for storage of refined products before distribution. Storage models need to factor the throughput from oil fields as well as transportation rates to avoid capacity issues. For finished product storage in storage tanks, mixed products can be stored provided there are no impacts, for example petrol cannot be stored in a tank where residual oil is kept due to the high sulphur content in residual oils. IoT and Big Data Analytics can help mid-stream companies manage their storage effectively.

Demand Modelling - For Downstream companies demand modeling is crucial to ensure they produce the right product mix in the refineries and store products with the desired demand levels. Analytical models based on demand planning and production planning is crucial. 

Price Optimization - Price Optimization and planning is crucial for downstream companies as their profitability is impacted by crude oil prices. With lower crude prices, feedstock costs go down and demand for petroleum products rises, hence pricing models need to factor in both feedstock costs as well a demand for petroleum products. Use of Machine Learning that looks at the end-to-end data can help companies price products optimally.

Market Analytics - A strategy that works in a given market, may not work in another market. Hence market analytics tools are required by large companies that have a global presence, which factor in the dynamic factors like economic demand, product demand and price trends in a given market.

Marketing Efficiency - Downstream companies use Advanced Analytics to analyze the marketing efficiency of distribution channels and ability to create new segments based on understanding of new areas of economic growth and changes in product demand.

As is evident from the transformational impact of digital technologies at each stage of the Oil & gas value chain, companies are increasingly looking at leveraging these technologies not only for productivity enhancements, cost optimization but also as a differentiator in enhancing revenues and meeting compliance needs. The key benefits that digital disruption has provided the Oil & Gas industry can be summarized as:

Automation - Automation in exploration and production has brought about cost optimization and lowered risks. Underwater drones and unmanned submersibles can help monitor equipment under water aiding in the inspection process and help manage equipment maintenance schedules.

Cost Optimization - Digital technologies have helped optimize the cost of exploration, operations and transportation and storage of petroleum products. The end to end visibility across the value chain has been truly disruptive. Cloud computing models have enabled enterprises to offload data sets to service providers and handle large volumes of data at lower costs as well as can use historical data sets in exploration and production.

Decision Making - With Self Service dashboards that provide end-to-end value chain visibility to enterprises, decision-making is based on near real-time data. This provides enterprises to take actions before serious events like weather events, changes in geopolitics or crude oil prices.

Productivity - As we have seen right across the value chain digital technologies have brought significant productivity gains through Automation, Big Data Analytics and IoT. Ability to handle and process unstructured data in form of maintenance reports, weather reports has also enhanced productivity of enterprises which would spend many man hours analyzing such data sets.

Health and Safety - Digital technologies have ensured better health and safety environment during exploration and production. Ability to measure emissions, perform automated inspections, sensor data on equipment performance and has helped manage health and safety indictors and meet compliance needs. AI robots are being used to detect faults in equipment and send alerts in case of gas leakage.

Financial Management - Predictive financial management systems have access to cross functional data sets that help analyze KPIs across multiple dimensions and provide enterprises a better picture of how to remain profitable.

According to Accenture’s Upstream Oil and Gas Survey, 80% of companies are looking to invest the same amount or more in the next 3-5 years. Digital disruption in Oil & Gas is about understanding how to harness the changes brought about by digital technologies and incorporating them into the business strategy of the enterprise. Digital technologies are also creating a need for a digitally enabled workforce and companies need to train manpower to meet the needs of the changing enterprise

Artificial Intelligence robots are being used to detect faults in equipment and send alerts in case of gas leakage

The author is Data Platform Solutions Lead at the Services Integration Hub in IBM and has written three books

 


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