International Business Machines Corporation

Diagnocat Inc.

LLC “Gazpromneft Science & Technology Centre”

Maria Golitsyna

David Echeverria Ciaurri

Vladimir Leonidovich Aleksandrovskiy

Evgeny Sergeevich Shumilov

Matvey Dmitrievich Ezhov

Vladimir M Babin

Artyom Semenikhin

Oleg S Ushmaev

Ramil R Yaubatyrov

Maxim Gusarev

Alexander Pozdneev

David Kremer Garcia

Nikolay G Glavnov

Mamat Shamshiev

A method for automated segmentation of volumetric images comprising the steps of: receiving a coarse volumetric image comprising a jaw/tooth structure in terms of voxels; defining each voxel a distinct anatomical identifier based on a probabilistic distribution for each of a tooth/non-tooth class; applying the defined coarse image through a coarse model for a coarse output; combining coarse output with the coarse volumetric image; and generating a probabilistic segmentation from applying the combined coarse output/image through a fine model; and converting the probabilistic to a polygonal mesh for each defined class applying a volume-to-mesh algorithm.

Probabilistic segmentation of volumetric images

Disclosed is a system and method for classifying a tooth condition, comprising: a localization layer; a classification layer; a processor; a non-transitory storage element coupled to the processor; encoded instructions stored in the non-transitory storage element, wherein the encoded instructions when implemented by the processor, configure the automated parsing pipeline system to: localize a present anatomical landmark in a patient's oral or maxillofacial region depicted in a parsed image frame by the localization layer, said landmark comprising of a feature or features with anatomical or pathological significance; and classify a dental or maxillofacial condition based on measuring any kind of relation between two or more features within and/or between landmarks by the classification layer.

System and method for classifying a tooth condition based on landmarked anthropomorphic measurements

Well settings in mature oil field may be configured by generating progressively, geological reservoir models by fitting a set of initial geological reservoir models that generate forecasts of fluid injection and production at a well as a function of time, with a subset of historical well data to produce a set of intermediary geological reservoir models, and fitting the set of intermediary geological reservoir models to a next subset of the historical well data. A set of diverse geological reservoir models may be determined from the geological reservoir models. A mid-term schedule of well settings associated with the well may be generated based on the set of diverse geological reservoir models and an economic model that comprises mid-term estimation of oil sale price and of production costs. The mid-term schedule of well settings may be actuated to control the fluid injection and production at the well.

System and tool with increased forecast accuracy to configure well settings in mature oil fields

Configuring well settings may include determining schedules over a mid-term horizon for well settings for all or a subset of wells in an oil field. The schedule may be generated so that a performance metric for the considered horizon is maximized. An economic metric, for example, a net present value, and production metrics, for example, cumulative oil production, are considered. Minimum acceptable short-term and long-term metrics, for example, short-term and long-term cumulative field oil production, may be included as target/constraints.

System and tool to configure well settings for hydrocarbon production in mature oil fields

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Moscow, Russia

Maria Golitsyna