Mohammed Al-Ghazal, Operations Supervisor at Saudi Aramco Unconventional Resources.
A key part of Mohammed Al-Ghazal’s role is exploring opportunities for data analytics in the Oil and Gas sector. At a time when digitalisation continues to transform the face of the industry, it is imperative to extract the true value in data in order to unlock efficiencies.
Oil and gas continues to play an essential role in fueling important functions of our everyday life, including transportation and power generation. To realise the full value of oil and gas exploration and production operations, several petroleum companies have been introducing digital systems to gain efficiency and improve deliverables in terms of cost, time and productivity. Also, some petroleum companies have introduced intelligent systems to support personnel health, safety, security and environmental (HSSE) aspects. Regardless of the particular intended objective behind digitalisation, it is important to extract the most value and understand the unintended risks through data-driven analytics.
Oil and gas operations are often characterised to be complex as they involve extended-infrastructure, remote-location, labour-intensive and capital-intensive applications with downhole uncertainty. Also, the industry has high rate of health incidents, safety incidents, security vulnerability, due to connection to the economy and function of the country, as well as environmental damage. In oil and gas operations, vast amount of data is collected and later interpreted into valuable information that can be acted upon. As the industry shifts paradigms towards more complex fields after successful exploitation of the relatively “easy oil”, effective exploitation of the big data is the need of the hour. One of the strategies to recognise patterns, identify explanatory features’ relationships, test scenarios for comprehensive calculated decisions, make foresights and extract innovative insights is advanced data analytics.
While the oil and gas industry is successful in collecting big data, it is relatively lagging in exploiting this data. Accordingly, our work aims to further investigate the implication of digital data analytics on improving the technical understanding, economics and HSSE performance in the oil and gas industry. Also, it aims to provide a simple, yet fast and cost-effective tool for operators to extract trends and facilitate transparent illustrations to streamline decision-making processes in today’s rapidly changing work environment. Our data-driven work will focus on well drilling and completion operations as they play the lion’s share of the operating budget.
The oil and gas industry needs to learn new ways to extract insights from the growing influx of data and integrate finances in fit-for-purpose, workflow-based analytics. The insights can be used to reduce costs, make explicit objective foresights and improve upstream operations, including well drilling, completion and producing. After all, this data is the language that our petroleum resources use to speak to us and data-driven analytics is our tool to interpret and respond accordingly in today’s data-driven age.
All in all, it is evident that the role of digitalisation is growing in the oil and gas industry despite modest application in the past. In addition, whilst the industry is successful in collecting big data, it is relatively lagging in exploiting these data through data analytics techniques. Also, most of the existing models do not sufficiently integrate economic aspects or HSSE operational objectives. Nonetheless, data-driven analytics can generate more information rapidly, freeing the team to focus on effectively exploiting oil and gas resources.
Additionally, we built machine learning algorithm to streamline error-prone, repetitive, human-intensive tasks.