Data is one of the most important limiting factors of deep machine learning (ML) model in
drilling applications. Though a big size of historical data can be available, high-quality …

Drilling is challenging in hard and heterogeneous sandstones because drill bits can
experience excessive wear, leading to short footage. Consequently, choosing the correct bit …

As drilling of new oil and gas wells increase to meet energy demands, it is essential to
optimize processes to ensure the health and safety of the crew as well as the protection of …

In a previous study, we reported the development of a bit-agnostic AI deep learning wear
model and showed the successful real-time deployments in multiple field bit runs. To test …

The main objective of this study is to develop a robust AI model to estimate the level of bit
wear during the real-time field deployments. We aim to enhance drilling efficiency by …

Drilling operations rely on learned expertise in monitoring the drilling performance data and
the rock data to assess the dull condition of the drill bit. While human learning can …

Due to highly uncertain underground conditions, most estimations of drilling and geological
parameters must be performed using numerical modeling, thus by fitting empirical models to …

The estimation of bit wear during real-time operation plays a crucial role in bit trip planning
and drilling optimization. Estimates by human learnings can be highly subjective and …

Drilling optimization is a complicated multi-objective processing optimization problem.
During drilling, drillers need to adjust WOB and RPM continuously in a timely manner, not …

Real-time Bit Wear Prediction and Deployment Validation in Challenging Hard and
Heterogeneous Sandstones using 3D Deta Page 1 Saudi Aramco: Public Control ID: 39 - 2023 …