Schlumberger Technology Corporation

Sensia LLC

Sensia Netherlands B.v.

Erik Burton

Andrey Sergeevich Konchenko

Emilien Dupont

Velizar Vesselinov

Jose Celaya Galvan

Crispin Chatar

Soumya Gupta

Ivan Alaniz

John Z Pang

Mugdha Bhusari

Jose Ramon Celaya Galvan

A method including generating, by a navigation service, a route for navigating from a route origin to a route destination using a private roads repository. The method includes identifying a ghost origin and a ghost destination of a ghost road along the route. The method includes sending, using an application programming interface of a base roads engine, a first request for a route from the ghost origin to the ghost destination. The method includes receiving, from the base roads engine in response to the first request, a replacement section from the ghost origin to the ghost destination. The method includes replacing, in the route, the ghost road with the replacement section to create an updated route including segments. The method includes generating an estimated travel time from the route origin to the route destination over the segments of the updated route. The method includes presenting the estimated travel time.

System for offsite navigation

A method for rapid region wide production forecasting includes identifying base data of a well in a plurality of wells of a region; selecting, using the base data and from a set of a models comprising a rich machine learning model, a location based machine learning model, and a decline curve model, a well model; and generating, based on the selecting, a forecasted production of the well using the base data and the well model. The method further includes aggregating a plurality of forecasted productions of the plurality of wells, the plurality of forecasted productions including the forecasted production, to generate a region forecast using the rich machine learning model, the location based machine learning model, and the decline curve model; and presenting the region forecast.

Rapid region wide production forecasting

A method includes receiving data where the data include data for a plurality of factors associated with a plurality of wells; training a regression model based at least in part on the data and the plurality of factors; outputting a trained regression model; and predicting production of a well via the trained regression model.

Resource production forecasting

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Menlo Park, CA, United States of America

Erik Burton