Introduction

The oil and gas sector, a key part of the global economy, is undergoing immense change with the power of data. Historically dependent on geology and mechanical engineering, the petroleum industry is quickly becoming an industry that leverages data in sophisticated ways.

The oil and gas industry plays a crucial role in powering the global economy, supplying energy that fuels transportation, industries, and households worldwide. 

The use of data science and data analytics in the oil and gas industry is no longer a futuristic element; it’s a real-time objective and part of the value chain improvement process that companies must engage in today to drive new efficiencies, improve employee and community safety, and support robust, informed, and strategic decision-making. 

This transition from a world where raw data is analysed manually, qualitatively, and historically to a world where insights derived from sophisticated algorithms are changing every aspect of the business.

The Role of Data Science in Exploration and Drilling

One of the most expensive activities in the oil and gas industry is exploration. Success in finding essential hydrocarbon reserves hinges directly on the proper interpretation of very large datasets. Data science and the application of data science in the oil and gas business are important in this regard. 

Sophisticated machine learning algorithms learn systematic and evidently complex seismic and geological datasets to find subtle patterns that could be missed in traditional data interpretation. 

By developing true subsurface models or simulations, a data scientist can build a better model to predict the best drilling location for hydrocarbons and mitigate the risk involved in exploration efforts and the high costs with dry well objectives. 

This new applied data science strategy allows us to improve the targeting of reserves from a productivity and profitability perspective.

Optimising Production and Maximising Asset Value

Once a well is producing, the challenge becomes maximising production and minimising downtime. This is where data analytics for oil and gas shines the best. 

Sensors on rigs, pipelines and production systems continuously stream data to be analysed in real time. Perhaps the most prevalent example of data science is predictive maintenance. 

Predictive maintenance models use the data received from sensors to predict when a piece of equipment could shut down or fail. 

This allows companies to schedule maintenance accordingly, increasing planned outages and avoiding costly and possibly dangerous unplanned shutdowns. 

Once data has been collected, data science algorithms can analyse past production data to determine the optimal operational parameters (such as pump speeds and pressures) that can improve hydrocarbon production. 

By continuously monitoring and managing these parameters using real-time data, organisations can dramatically increase production efficiency and the production lives of their assets, i.e., maximising life cycle value.

Enhancing Reservoir Management

Reservoir management is an extremely complicated discipline. The primary goal of reservoir management is understanding how hydrocarbon reservoirs behave over time. 

Albeit we can never completely model a hydrocarbon reservoir in its entirety, data science does provide petroleum engineers with excellent opportunities to build and refine highly sophisticated reservoir models. 

By integrating a multitude of data types, such as seismic surveys, well logs, and past production history, data science models can predict fluid movements, pressure changes, and ultimate recovery factors. 

This improves a company’s ability to understand secondary and tertiary recovery methods, such as water or gas injection, and how to optimise the resources that remain and are recoverable. 

This capability of modelling is an extremely powerful model for companies in the petroleum sector, particularly later in the production phase of strategic planning for managing a reservoir for the long term.

Improving Safety and Environmental Compliance

Safety and environmental protection are among the principal priorities in the oil and gas industry. The role of data science within the oil and gas industry has significant implications for safety and environmental stewardship.

Data from sensors and historical data logs of events allow machine-learning models to predict and alert operators of pending hazards or equipment failures that can evolve into accidents.

The predictive aspect of this practice allows operators to respond appropriately in a timely manner to protect employees and valuable asset bases.

Similarly, data science is being used to manage mitigating environmental consequences of operational activities. For example, data analytics can process burn data in real-time and identify the source of the leak and the other wasteful activities contributing to higher greenhouse gas (GHG) emissions.

This can all help to meet more and more stringent environmental requirements and lead to a greener and more responsible operational framework.

Poor Data Quality and Management: The volume of data the oil and gas industry generates is massive, but much of it is unstructured and of poor quality. Insufficient data governance and engineering processes do not help extract usable data, much less reliable data, for analysis.


Challenges of Adopting Data Science in the Oil and Gas Industry

Talent Gap: There is a shortage of people with the necessary specialised data science capabilities, able to factor in the complexities of one of the most complex enterprises (data of the oil and gas industry). Finding and getting the team assembled can be a challenge.

Culture: A company culture that is solidly biased against change, without top (C-Suite) management support, blocks emergent data-based initiatives.

A full data science course can directly address these deficiencies by providing professionals with the skills in data cleaning and analytics and machine learning that will close the talent gap and support a data-driven culture.

The Future of Data in the Petroleum Industry

Datacentric approach Going from competitive advantage to operational necessity: The oil and gas sector is moving to a data-centric approach as an operational necessity, not a competitive advantage.

Tackling Challenges for Resilience: Companies that address data quality, talent shortages, and cultural aversion to data will be able to harness data analytics in oil and gas to become more efficient, profitable, and resilient.
Data-driven Operations: The future petroleum industry will not use a data-centric approach once in a while as before; business-critical decisions will always be data-driven decisions.