deep learning

This paper presents a workflow that combines probabilistic modeling and deep-learning models trained on an ensemble of physics models to improve scalability and reliability for shale and tight-reservoir forecasting.

A universal, automated approach to condition-based maintenance of drilling rig mud pumps is developed using acoustic emission sensors and deep learning models for early detection of pump failures to help mitigate and reduce costs and nonproductive time generally associated with catastrophic pump failures.

The authors of this paper describe a procedure that enables fast reconstruction of the entire production data set with multiple missing sections in different variables.

This paper presents a physics-assisted deep-learning model to facilitate transfer learning in unconventional reservoirs by integrating the complementary strengths of physics-based and data-driven predictive models.

This article explains what deep learning is and how it works and presents an example use case from the energy industry.

This article presents a deep-learning approach, the long short-term memory network, for adaptive hydrocarbon production forecasting that takes historical operational and production information as input sequences to predict oil production as a function of operational plans.

The authors of this paper describe a project that demonstrated the feasibility of using deep-learning and machine-learning approaches to introduce camera-based solids monitoring to the drilling industry.

Large geological models are needed for modeling the subsurface processes in geothermal, carbon-storage, and hydrocarbon reservoirs. The size of these models contributes to the computational cost of history matching, engineering optimization, and forecasting. To reduce this cost, low-dimensional representations need to be extracted. Deep-learning tools, such as autoenc…

The authors discuss the development of a deep-learning model to identify errors in simulation-based performance prediction in unconventional reservoirs.