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Top 7 Technologies Automating the Oil & Gas Industry in 2024

Written by
Cem Dilmegani
Cem Dilmegani
Cem Dilmegani

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

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The oil and gas industry (O&G) has always been notoriously slow in adopting new technologies because of regulatory issues, safety measures, and rigid compliance standards. But if the sector can overcome these barriers and leverage automation technologies, such as IoT and RPA, it can: 

  • Lower engineering hours 
  • Lower data interpretation time 
  • Lower maintenance costs 
  • Increase productivity 
  • Lower time-to-market ratio 

In this article, we aim to:

  • Explain what automation in oil and gas is 
  • Why the oil and gas is behind other industries in automation 
  • What technologies can automate oil and gas
  • What the benefits of automation is for the oil and gas industry 
  • What the use cases of automation are in oil and gas

What is automation in oil and gas?

Automation in oil & gas (O&G), also called oilfield automation, is using automation solutions to improve how oil companies extract oil, build pipelines, market their product, stay compliant, and run a more efficient business as a whole, among other use cases.

What is the rate of technological adoption in the oil and gas sector?

In 2022, the automation solutions market in oil and gas industry was worth $17.5B in 2022. It is expected to reach $23B in 2028. 

For context, the market size for AI banking is expected to reach $64B by 2030. One possible theory as to why this 2$T sector lacks behind in terms of automation, forcing Deloitte to give it the lowest “digital maturity ranking” amongst the 17 biggest industries, is the redtapes around it. 

The O&G companies have so far not changed the way they operate because of regulatory, safety, and compliance issues. But things are slowly changing. cannot, or do not want to, change the way they operate because of regulatory, safety, and compliance issues. 

What technologies can automate the oil and gas industry? 

The following are some of the main technologies that can digitally transform the oil and gas industry:

1. IoT

IoT, or the Internet of Things, is the ecosystem of interconnected smart devices that have sensors in them, are capable of monitoring their environment, and their status and findings can be monitored by users

2. RPA & Intelligent Automation

RPA, or robotic process automation, are software robots that can automate the undertaking of mundane and time-consuming tasks, thereby reducing the workload of employees and allowing them to focus on more value-driven tasks for higher efficiency. RPA combined with AI, referred to as intelligent automation, can automate more complex tasks that require human judgment. Check our article on intelligent automation in the oil & gas industry.

3. WLA

Workload automation are tools that can schedule or trigger the execution of tasks for increased punctuality and accuracy.  

4. Process mining 

Process mining is a technology that provides users with an “as-is” image of their processes so that they know where their inefficiencies are, what steps can be automated, along with increased-productivity estimates. 

5. Machine Learning (ML)

Machine learning (ML) is a subset of AI focused on creating algorithm-driven models that can learn and produce more accurate output through repeated interactions with data. 

6. Digital twin of an organization (DTO)

The digital twin of an organization is a virtual replica of a digital or physical process that showcases the potential outcome of a soon-to-be developing project before any actual steps are taken. 

7. Web scraping 

Web scrapers are software robots that can be programmed to scrape websites to search for, and extract, predetermined information, such as prices.  

What are the benefits of automation in oil and gas industry?

A study done by BCG in 2019 argues that automation can bring about the following benefits in the O&G sector (Exhibit 1): 

1. 50-60% reduction in data interpretation time and cost 

Having data pipelines that keep feeding different back-end, AI-driven ERP systems, such as automated accounting software, can ramp up the speed at which data is exchanged, interpreted, and reported to analysts and shareholders. 

2. Up to 70% reduction in engineering hours

Thanks to technologies such as DTO, engineers can create simulated replicas of the ideal processes before physically building them. This feature makes engineering wells, for instance, less wasteful and more efficient by bridging the gap between the theoretical groundwork and the actual structure. 

3. 20-30% faster construction

The engineers have an easier time recognizing the parts of the project, such as pipelines, that are taking longer to construct than others, thanks to sensors linked to construction equipment that monitor the construction progress, and then send the information to the project engineers on visible dashboards. 

The implication is that underperforming/faulty equipment, alongside behind-the-schedule projects, can be pinpointed and investigated, leading to quicker construction.

4. 3-5% increased production 

A more lean and mean production capability will be the end consequence of the aforementioned aspects coming together, such as more effectively analyzing data or fixing any weak points – in well or pipeline construction, drilling, and oil extraction – as they appear.

5. 20-40% reduction in maintenance costs

A last benefit of automation is that maintenance costs are reduced thanks to predictive maintenance capability offered by real-time flow of information from the production silos onto the back office. Because engineers and technicians have the ability to tend to faulty equipment preemptively, the damage would be limited in surface and operational area, thus reducing the overall maintenance costs. 

What are the use cases of automation in the oil and gas industry?

The following are real-life, specific use cases of how digitization can transform the oil and gas industry: 

1. Cost management 

RPA bots can be leveraged to lower the back-end costs of a company. The RTP – requisition to payment – process, for instance, is one domain that can be automated to help reduce the workload of accounting teams. The benefit of delegating the subprocesses within the RTP – such as requisition and invoicing – to RPA bots is that they can do them cheaper and more efficiently. 

Case study: 

A petroleum company wanted to lower its costs to maintain its competitive advantage. By delegating RTP tasks to RPA bots, the proof of concept showed that there was potential for the company to reduce the manual efforts by 65-80%, expedite the RTP process by 4 times, and save 1,700 man-hours annually. 

2. Real-time pricing

Oil prices are notoriously volatile. And with the recent geo-political situation in Russia, they have become more unpredictable than before. Moreover, with recent international efforts to move the world economy away from fossil fuels and towards sustainable ones, O&G firms do not have much time left to maximize on their earnings. 

Automated pricing software leverages web scraping to scrape the sorts of data that affect the price of oil in real-time – supply, demand, prices of derivatives such as futures and options, price of substitute products (such as solar panels or electricity), and more – that allows for a data-driven pricing strategy. 

3. Pipelines and wells’ monitoring 

The pricing software that we mentioned above can provide a more accurate price by factoring in the real-time production levels if it is integrated with IoT sensors that monitor the pipelines and wells’ levels. 

Moreover, these sensors can also take in the heat, vibration, and other factors that can be determinants of the health of pipelines and wells. And in case of any deviations from usual thresholds, predictive maintenance can be undertaken to limit the damage. 

4. Automated reporting 

The price O&G producers charge is highly competitive. (OPEC countries, for instance, follow a uniform pricing strategy that discourages undercharging for market capture). That’s why it’s important for producers to release accurate reports of their production levels, revenues, balance sheets, etc. 

RPA bots are again good candidates for executing such tasks that alleviate the workload off the accounting teams, and curate accurate and timely reports thanks to exchanging data between different ERP systems, and as they appear. 

To learn more about RPA use cases in reporting, click here. 

5. Regulatory compliance 

Since the year 2000, O&G companies have been fined a total amount of $53B, because of 6,067 separate violations. And most of the fines are cited as “environmental violations.” 

O&G companies can partner up with 3rd-party vendors that use automated software to calculate their carbon emissions, use AI/ML models to calculate their greenhouse gas emissions of their supply chain, and keep monitoring the integrated smart devices (e.g. IoT, CCTV cameras, etc) to make sure everything is aligned with environmental and safety regulations.   

For more on oil and gas automation

To learn more about how automation is changing the oil and gas industry, read:

If you believe your business could benefit from business process automation, we have a data-driven list of BPM solutions.

And if you need consultants to advise you on transforming your business, we also have a data-driven list of digital transformation consultants prepared.

We will help you choose the best vendor tailored to your needs:

Find the Right Vendors

This article was originally written by former AIMultiple industry analyst Bardia Eshghi and reviewed by Cem Dilmegani

Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Cem's hands-on enterprise software experience contributes to the insights that he generates. He oversees AIMultiple benchmarks in dynamic application security testing (DAST), data loss prevention (DLP), email marketing and web data collection. Other AIMultiple industry analysts and tech team support Cem in designing, running and evaluating benchmarks.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

Sources:

AIMultiple.com Traffic Analytics, Ranking & Audience, Similarweb.
Why Microsoft, IBM, and Google Are Ramping up Efforts on AI Ethics, Business Insider.
Microsoft invests $1 billion in OpenAI to pursue artificial intelligence that’s smarter than we are, Washington Post.
Data management barriers to AI success, Deloitte.
Empowering AI Leadership: AI C-Suite Toolkit, World Economic Forum.
Science, Research and Innovation Performance of the EU, European Commission.
Public-sector digitization: The trillion-dollar challenge, McKinsey & Company.
Hypatos gets $11.8M for a deep learning approach to document processing, TechCrunch.
We got an exclusive look at the pitch deck AI startup Hypatos used to raise $11 million, Business Insider.

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