DSDE: In Practice

TGS, BGP, and CGG signed an agreement with state-owned Staatsolie to acquire, promote, and license multiclient seismic programs, including new 3D acquisition, in the shallow waters offshore Suriname.

The authors describe how tools in machine learning are used to develop data-driven models that can be used for accurate and efficient fatigue-damage prediction for marine risers.

The paper demonstrates the application of an interpretable machine-learning work flow using surface drilling data to identify fracable, brittle, and productive rock intervals along horizontal laterals in the Marcellus shale.

The downtime of manufacturing machinery, engines, or industrial equipment can cause an immediate loss of revenue. Reliable prediction of such failures using multivariate sensor data can prevent or minimize the downtime. With the availability of real-time sensor data, machine-learning and deep-learning algorithms can learn the normal behavior of the sensor systems, dis…

This paper describes an artificial intelligence deep Q network for field-development plan optimization.

In this paper, the authors describe a model that uses augmented artificial intelligence to optimize well spacing by use of data sculpting, domain and feature engineering, and machine learning.

Using a large-sample statistical approach based on publicly available data, the authors of a recent study investigated the potential impact of unconventional oil and gas development on surface water quality.

The authors develop a representative geostatistically based 3D model that preserves geological elements and eliminates uncertainty of reservoir properties and volumetric estimates for a Libyan field.

Satellites provide an invaluable, bird's-eye view of planet Earth. But how—and how quickly—will the data materialize into meaningful actions?

An EU-backed wind foundation design project revealed that predictive digital twin technology can reduce the steel weight and associated costs of offshore foundations.