Halliburton. has filed a patent for a data clustering process to interpret formation data for well placement models. The process involves generating a facies cluster model using unsupervised computer learning techniques to assess stratigraphy and borehole position. The method aims to improve well operations in subterranean formations. GlobalData’s report on Halliburton gives a 360-degree view of the company including its patenting strategy. Buy the report here.

Smarter leaders trust GlobalData


Premium Insights Halliburton Co - Company Profile

Buy the Report

Premium Insights

The gold standard of business intelligence.

Find out more

According to GlobalData’s company profile on Halliburton, Oil well fracking was a key innovation area identified from patents. Halliburton's grant share as of January 2024 was 52%. Grant share is based on the ratio of number of grants to total number of patents.

Interpreting formation data for well placement using data clustering

Source: United States Patent and Trademark Office (USPTO). Credit: Halliburton Co

The patent application (Publication Number: US20240035366A1) describes a method for performing a well operation associated with a wellbore in a subterranean formation. The method involves obtaining target well data, generating a facies cluster model using a clustering process on the data, and then using this model to perform the well operation. The clustering process utilizes machine learning algorithms such as Self-Organizing Maps, Generative adversarial networks, or K-nearest neighbors. The well operation, which can include drilling the wellbore, involves steering a drill bit based on the facies cluster model, with the option for automatic steering. Additionally, the method allows for real-time execution and visualization of the facies cluster model, as well as the modification of a well plan based on the model.

Furthermore, the patent application also includes an automated directional drilling system and a computer program product that implement the method described above. The system comprises processors that generate the facies cluster model for a high angle or horizontal wellbore and steer the drill bit using this model. The system utilizes unsupervised clustering algorithms and can provide a visual representation of the facies cluster model. The computer program product, stored on a non-transitory computer-readable medium, directs the operation of processors to automatically generate the facies cluster model and guide the well operation in real-time. Overall, the patent application introduces innovative techniques for optimizing well operations in subterranean formations, particularly in high angle or horizontal wellbores, by leveraging machine learning algorithms and real-time data processing.

To know more about GlobalData’s detailed insights on Halliburton, buy the report here.

Premium Insights


The gold standard of business intelligence.

Blending expert knowledge with cutting-edge technology, GlobalData’s unrivalled proprietary data will enable you to decode what’s happening in your market. You can make better informed decisions and gain a future-proof advantage over your competitors.


GlobalData, the leading provider of industry intelligence, provided the underlying data, research, and analysis used to produce this article.

GlobalData’s Company Filings Analytics uses machine learning to uncover key insights and track sentiment across millions of regulatory filings and other corporate disclosures for thousands of companies representing the world’s largest industries. This analysis is combined with crucial details on strategic and investment priorities, innovation strategies, and CXO insights to provide comprehensive company profiles.