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Use case fo predicting overlooked hydrocarbons

Predicting overlooked hydrocarbons



Identify untapped resources that have been previously overlooked

Predicting overlooked hydrocarbons can help minimise the costs associated with exploring higher-risk prospects, making it an important strategy for managing capital and operational expenses.

The world still relies heavily on fossil fuels, and transitioning to green energy sources is a long and challenging process. Additionally, obtaining new exploration licenses has become increasingly difficult due to environmental concerns, making it essential to make the most of existing resources.

Overall, this can play a significant role in meeting the world's energy needs while minimising environmental impact and maximising efficiency.

Why you need AI-driven geoscience software

Increased speed

When you are searching for overlooked exploration opportunities, you need to screen large areas efficiently. Increase speed more than 10X by leveraging AI instead of traditional methods. 

Reduced costs

Traditional methods of identifying missed pay are very time-consuming and expensive. AI software can assist geoscientists to reduce basin screening cycle time from years to months.

Improved accuracy

By using AI models trained on vast amounts of QC'ed data, you reduce the human bias and subjectivity associated with traditional methods, thus increasing the accuracy of the interpretation.


Traditional methods are labor-intensive and require a significant number of qualified human resources such as geologists and petrophysicists.

The identification of overlooked hydrocarbon pay opportunities poses several challenges for energy companies.

Computer processed interpretation of log data for each well can take up to 1-2 days, making scaling up to hundreds or thousands of wells using traditional methods infeasible.

The quality of data is often variable, incomplete, and decentralised, with bad hole conditions and logging tool malfunctions hindering data collection.

Recognising poor data and correcting it requires specialised knowledge, which is becoming scarce as experienced scientists move to other fields.

These challenges and issues related to data quality and storage in isolated systems can cause the loss of vital opportunities.


Predicting overlooked hydrocarbons with EarthNET

In the several thousand wells on the NCS, there are numerous known hydrocarbon accumulations recorded in official databases, e.g. DISKOS. Some explorationists has previously has great success by digging deep into the data and discovering clues indicating hydrocarbon presence in wells that were presumed to be dry. This process has been very labour-intensive and manual, which is why have developed an AI-assisted workflow for predicting overlooked hydrocarbons.

With EarthNET, you can clean the data, predict rock-and fluid properties, and screen more than 5000 wells for overlooked opportunities in 3 just months, whereas traditional methods would take years or even decades to complete.

EarthNET AI Images - Object detection

Base your analysis on the most fundamental ground truth by using rock image data

Use computer vision technology to determine the properties and type of rock samples.

In Earth Science Analytics, we have developed and tested specialised pre-trained AI models to recognise the properties of different types of rocks present in stratigraphic formations. By analysing rock image data using EarthNET Images, you can quickly and accurately identify the properties of the rock samples, such as their composition, grain size and porosity.

This enables geologists and researchers to gain a better understanding of the stratigraphic formations and make informed decisions regarding exploration and extraction activities.

Explore the capabilities of EarthNET AI Images →

Cover large areas efficiently in your screening process by leveraging AI interpretation of wells

Efficiently map the distribution and properties of reservoirs and fluids at basin scale.

EarthNET AI Wells revolutionises your petrophysics by enabling you to predict rock- and fluid properties with AI - faster, better, and cheaper than traditional methods, with uncertainty quantified at each step. Doing a large-scale study screening 5000 wells would take 13-27 years using traditional methods compared to 3 months using EarthNET AI Wells.

Discover EarthNET AI Well →
Earthnet AI Wells - AI interpretation of wells
EarthNET Insights

Use all available data to form the basis for your decision-making

The traditional expert-driven approach to decision-making in exploration and basin screening contexts has proven to be subjective and prone to human biases, resulting in overestimations of hydrocarbon volumes and less accurate geomodels.

With EarthNET Insights, all available data is utilised to deliver fully auditable, reproducible, and cost-effective insights at scale.  It provides a complete and purely data-driven basis for identifying and characterising previously overlooked opportunities. 

Discover EarthNET Insight →

"This technology has come up as a way of extracting more insights from wells that are otherwise forgotten about."


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