Schlumberger Technology Corporation

Sensia LLC

Sensia Netherlands B.v.

Andrey Sergeevich Konchenko

Erik Burton

Jose Celaya Galvan

Emilien Dupont

Sergey Doronichev

Ivan Alaniz

Siau Ban Teh

Rod W Shampine

Velizar Vesselinov

Crispin Chatar

Bruce MacKay

Kreso Kurt Butula

Soumya Gupta

Sergey Dmitrievich Parkhonyuk

Markus Schlafli

Jared Bruns

Vincent Dury

John Z Pang

Ewan Maxwell

Evgeny Pavlovich Korelskiy

Apoorva Dubey

Torbjoern Vik

Valeriy Anatolievich Pavlov

Dmitriy Monakhov

Tong Zhang

Kim Swift

Salvador Ruiz-Esparza

Mugdha Bhusari

Indranil Roychoudhury

Jose Ramon Celaya Galvan

Prasham Sheth

Jared Krichko

Anatoly Aseev

A method may include receiving, via one or more processors, a set of image data representative of equipment configured to distribute a gas. The method may then involve determining a type of equipment depicted in the first set of image data, retrieving a leak detection model corresponding to the type of equipment depicted in the first set of image data, and determining that a gas leak is present on the equipment based on the set of image data and the leak detection model. After determining that the gas leak is present, the method may include sending a notification to a computing device in response to detecting the gas leak.

Detecting gas leaks from image data and leak detection models

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

Methods, apparatuses, and computer-readable media are set forth for visualizing and interacting with well production data in a three-dimensional or four-dimensional environment, e.g., using a volumetric well production display representation representing a well in an oilfield and including a plurality of display characteristics configured to display historical production data for the well over a time period.

Volumetric well production user interface components

A navigation service generates a route for navigating from a route origin to a route destination using a private roads repository. A ghost road is along the route, and a ghost origin and a ghost destination of the ghost road is identified. Using an application programming interface of a base roads engine, a request for a route is sent from the ghost origin to the ghost destination. From the base roads engine and in response to the request, a replacement section from the ghost origin to the ghost destination is received. The ghost road is replaced with the replacement section to create an updated route, which is presented.

A method including obtaining a first image of a geographical location and a physical object located therein. Pixels in the first image are assigned to corresponding points in a Cartesian coordinate system defined specifically for the first image. The resulting virtual map includes a virtual representation of the physical object, a set of Cartesian coordinates assigned to a set of pixels in the pixels, and a set of Cartesian rotation angles assigned to the set of pixels. A first transform is calculated that scales the Cartesian coordinate system to a physical scale of the geographical location. A data structure stored in a data repository is formed by applying the first transform to the virtual map. The data structure includes the set of pixels, a set of scaled Cartesian coordinates, the set of Cartesian rotation angles, and a physical scale virtual representation of the physical object.

Data structures for augmented reality planning of geographic locations

A horizontal well may be refractured or reactivated by generating a geomechanical model to estimate the stress level in the formation; identifying zones with high, medium, and low stress levels in the formation; isolating existing fractures in the horizontal well; injecting a fracturing fluid into the well to create at least one new fracture in a zone with a low stress level and packing the created fracture with proppant to increase the stress level in the zone; isolating the at least one newly created fracture; initiating refracturing in the zone with the high and/or medium stress level; identifying a location of the refracturing crack in the zone with the high and/or medium stress level; and developing the refracturing crack and packing it with proppant.

Method of refracturing in a horizontal well

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

Andrey Sergeevich Konchenko