Oil and Gas is no stranger to Digital. The fact is, digital fields have been in existence for some time in many large companies. However, with the current glut that the oil Industry is in, a much stronger business case exists to leverage the data generated from the digital infrastructure optimally.

Now is the perfect time for oil and gas companies to evaluate the opportunity areas that can be transformed by leveraging AI.

Remember this, leveraging AI does not always mean that you end up replacing human expertise entirely with Artificial Intelligence. In most cases, AI will augment natural intelligence to accelerate analysis or improve the quality of human decision-making.

So let us explore some opportunity areas at a high level in this article.

Managing Contracts

The number of contracts and agreements in an average upstream Oil and gas org is in the thousands. And the challenge is- they are all different, often with unique clauses, triggers, and riders. Since legal professionals write them, they are hard to understand, and knowing which of those contracts is approaching key milestones is next to impossible.

Leveraging Deep Learning, you can create centralized “Intelligent” contract hubs to help you manage your contracts’ legal aspects. Imagine using AI to quickly determine which contracts need attention and why and recommend actions to management.

Geological Data Interpretation

The main objective of acquiring and analyzing geological and geophysical (G&G) data is the development of maps to identify areas favorable for the accumulation of hydrocarbons. The G&G data is analyzed to develop a basic knowledge of the geologic history of an area and its effects on hydrocarbon or strategic/critical minerals generation, distribution, and accumulation within the planning area.

The primary source of the data and information used by the Resource Evaluation Program are seismic surveys and wells logs acquired by the oil and gas industry.

As with many other fields, AI applied to seismic data can significantly change how geological interpretation is done. The way I see it, the future of geologic interpretation will be split into more routine, essentially AI-led work and high-end, complex, creative human-led work.

Drilling optimization

Figuring out optimal drilling locations is another example of leveraging AI. Research shows that engineers spend 40% of their time assembling the data to set up a drilling program (Woodside Petroleum study). They need a mind-boggling number of data points like:

• Data from prior drilling campaigns
• Actual costs
• Infrastructure costs
• Infrastructure locations
• Nearby well logs
• Seismic data
• Geological interpretation

AI can help expedite this significantly, leaving only “expert” level tasks to the engineers. AI can take over functions like:

• BHA behavior monitoring
• ROP improvement by managing between
• Frictional drag and load transfer monitoring
• DS vibrations monitoring
• Estimation of hole cleaning efficiency
• Thurst and drilling torque production

Field Service Management

A complex Oil company operations deal with thousands of tickets from field companies for services rendered. Humans have to look at them manually, figure out which site they apply to, assign the right account codes, etc. Compliance reporting for commodities like water and emissions generates its share of the document pile. Often, il companies employ more accounting and water-usage compliance teams than Geologists.

AI can help here by converting field tickets into accurate data quickly and accurately. Using language processing to restore and interpret the text, Identify and extract the correct data, feed that data into the suitable systems, and make decisions to accept or dispute charges.

Digital Twin

The oil and Gas industry is no stranger to Digital Twin. If you visit a Geology/Reservoir team at an Oil and Gas company, you will see Digital, multidimensional simulation models and resource models.

One of the genuinely significant breakthroughs of digital innovation in oil and Gas is the ability to create a fully functioning digital twin of just about any asset or an entire business. These newer versions include many data layers that provide a rich, fully integrated, and analytically deep software version of the asset or business.

Advances in AI and technology now allow Oil and Gas companies to build an end-to-end Digital Twin that can leverage complex modeling of the real-world using AI-based algorithms in real-time.  Data generated by these twins can also feed into Predictive Analytics algorithms for optimal asset utilization. Digital Twins can now be made more robust by including the following:

• The engineering content (diagrams, specifications, and configurations) describes the physical asset digitally for the engineering disciplines.
• The maintenance history (timing, procedures performed, parts installed, and installers) provides insight into the ability of the asset to perform to its potential.
• The physical constraints of the various assets (operating capacities, throughputs, and pressures) restrict how each asset physically behaves; the operating parameters of the assets (input energies, consumables, by-products, and emissions) constrain the asset’s performance.
• The financial aspects of the assets (fixed build cost and operating cost per unit) yield the economics of the business.
• The uncertain elements (customer demand, weather events, or supply disruption) comprise the real-world conditions the business must cope with.