Deep learning for prediction of complex geology ahead of drilling

04/06/2021
by   Kristian Fossum, et al.
0

During a geosteering operation the well path is intentionally adjusted in response to the new data acquired while drilling. To achieve consistent high-quality decisions, especially when drilling in complex environments, decision support systems can help cope with high volumes of data and interpretation complexities. They can assimilate the real-time measurements into a probabilistic earth model and use the updated model for decision recommendations. Recently, machine learning (ML) techniques have enabled a wide range of methods that redistribute computational cost from on-line to off-line calculations. In this paper, we introduce two ML techniques into the geosteering decision support framework. Firstly, a complex earth model representation is generated using a Generative Adversarial Network (GAN). Secondly, a commercial extra-deep electromagnetic simulator is represented using a Forward Deep Neural Network (FDNN). The numerical experiments demonstrate that the combination of the GAN and the FDNN in an ensemble randomized maximum likelihood data assimilation scheme provides real-time estimates of complex geological uncertainty. This yields reduction of geological uncertainty ahead of the drill-bit from the measurements gathered behind and around the well bore.

READ FULL TEXT

page 4

page 9

page 10

research
07/04/2022

Probabilistic forecasting for geosteering in fluvial successions using a generative adversarial network

Quantitative workflows utilizing real-time data to constrain ahead-of-bi...
research
12/06/2018

Generative Adversarial Network based Speaker Adaptation for High Fidelity WaveNet Vocoder

Neural networks based vocoders, typically the WaveNet, have achieved spe...
research
01/20/2021

A Taylor Based Sampling Scheme for Machine Learning in Computational Physics

Machine Learning (ML) is increasingly used to construct surrogate models...
research
01/06/2022

Uncertainty Quantification Techniques for Space Weather Modeling: Thermospheric Density Application

Machine learning (ML) has often been applied to space weather (SW) probl...
research
09/15/2021

An Improved Approach to Orbital Determination and Prediction of Near-Earth Asteroids: Computer Simulation, Modeling and Test Measurements

In this article, theory-based analytical methodologies of astrophysics e...
research
12/26/2018

BlinkML: Efficient Maximum Likelihood Estimation with Probabilistic Guarantees

The rising volume of datasets has made training machine learning (ML) mo...

Please sign up or login with your details

Forgot password? Click here to reset