International Business Machines Corporation

Repsol, S.a.

Resol S a

Eduardo Rocha Rodrigues

Renato Luiz De Freitas Cunha

Bruno Silva

Jose Maria Segura Serra

Ana Paula Appel

Ulisses Mello

Khalid Ahmed

Paula Aida Sesini

Marco Aurelio Stelmar Netto

Carlos Henrique Cardonha

Marco A S Netto

Carmen Nilda Mena Paz

Mohamed Ahmed Hegazy

Pablo Enrique Vargas Mendoza

Vitor Henrique Leal Mesquita

Nubia Aurora Gonzalez Molano

Roberto Federico Ausas

Freddy Ernesto Mackay Espindola

Mookanahallipatna Ramasesha Lakshmikantha

Lucas Correia Villa Real

Priscilla Barreira Avegliano

Ankur Narang

Alecio Pedro Delazari Binotto

Lucas Goncalves Franco

Lakshmikantha Mookanahallipatna Ramasesha

José María Segura Serra

Kamal Das

Lashmikantha Mookanahallipatna Ramasesha

Marco As Netto

Mookanahallipatna Ramasesha Lashmikantha

Leopoldo Luis Cabo Penna Franca

Luis Cezar Menezes Tavares De Lacerda

Renato De Pierri

Renato Luis De Freitas Cunha

Ulisses T Mello

Nubia Aurora González Molano

One or more processors may detect that an extreme event that occurred in a first region. One or more processors may determine that the extreme event creates a drift in a probability distribution that is an output of a model associated with the first region. One or more processors may retrain the model associated with the first region using data associated with the extreme event. One or more processors may identify a second region similar to the first region according to a defined correlation threshold. One or more processors may generate data representing potential extreme scenarios for the second region based on the detected extreme event in the first region. One or more processors may retrain a model associated with the second region based on the generated data representing potential extreme scenarios.

Recalibration of risk related models impacted by climate

The present disclosure provides for crop yield estimation by identifying, via image processing, a field in which a crop is grown; identifying a plurality of regions within the field; identifying, by processing growth metrics via a model, a plurality of data collection points in the plurality of regions, wherein a given data collection point of the plurality of data collection points within a given region of the plurality of regions is identified by multivariate analysis as representative of growing conditions in the given region; receiving in-field data linked to the data collection points of the plurality; and predicting a yield for the crop in the field based on the in-field data.

Crop yield estimation

A microservice and the recipe for that microservice are automatically created by modifying a local environment in an integrated development environment executing on a computing system to construct a desired microservice, recording commands entered while modifying the local environment, computing a list of changes from the recorded commands that change the local environment and compiling the list of changes into a recipe comprising commands and dependencies sufficient to assemble an operating system and software files that are sufficient to instantiate the desired microservice.

Automated microservice creation based on user interaction

A method includes confirming when a vehicle accesses a region of interest, obtaining data associated within the region of interest, and determining, based on analytics performed on the data obtained of the region of interest, whether one or more anomalies are present at the region of interest. Obtaining data includes at least one of collecting, with an imaging sensor associated with the vehicle, image data of a given point of interest within the region of interest or collecting, with a motion sensor associated with the vehicle, motion data of the vehicle in a given fragment of interest within the region of interest. The confirming, obtaining and determining steps are performed by at least one processing device comprising a processor operatively coupled to a memory.

Determining the state of infrastructure in a region of interest

A job executing on a computer system according to a scheduler configuration may be monitored. Responsive to determining that the job completed executing, a difference between an allocated resource allocated to the job and the job's actual resource usage may be computed. Responsive to determining that the difference meets a criterion, the scheduler configuration may be reconfigured to reduce the allocated resource for a submitted job associated with a given user.

Automatic reconfiguration of high performance computing job schedulers based on user behavior, user feedback, and job performance monitoring

Resource usage is monitored for a hybrid cloud computing environment having on-premises computing resources and off-premises computing resources, for at least a first time period. For the time period, the amount of the on-premises computing resources and off-premises computing resources are related to the demand for computing services from the environment. Future demand for computing services from the environment is dynamically predicted based on the relating step and a learned function of the demand and/or a projection of future business growth for a business utilizing the on-premises and off-premises computing resources. Based on the predicted future demand, the amount of the on-premises computing resources is dynamically optimized for a future time period, so as to minimize costs for the future time period; and new amounts of on-premises computing resources and off-premises computing resources are provisioned for the future time period, in accordance with the optimizing step.

Hybrid cloud operation planning and optimization

A method for automatically generating scheduling algorithms, including determining a scheduling policy for a plurality of jobs to be executed on a computer system, where the scheduling policy specifies an execution order of a plurality of jobs; using the scheduling policy in a production environment for a period of time, and collecting data indicative of a business impact of each job executed during the period of time; generating a list of all pairwise comparisons of business impact between the plurality of jobs, together with outcomes of the comparisons; marking each pair for which the comparison outcome is inconsistent with the relative execution order of the pair of jobs according to a predefined criteria to create a reinforcement learning batch; and using the reinforcement learning batch to adjust a decision criteria used to determine the scheduling policy.

Automated generation of scheduling algorithms based on task relevance assessment

A computer-implemented method, a computer program product, and a computer system for infrastructure resource provisioning using trace-based workload temporal analysis. An infrastructure provider receives, from an on-premises infrastructure, scheduler traces of the on-premises infrastructure, one or more scenarios, one or more goals, and one or more constraints of resource deployment on the on-premises infrastructure. The infrastructure provider performs trace-based workload temporal analysis, based on the scheduler traces, the one or more scenarios, the one or more goals, and the one or more constraints. The infrastructure provider generates one or more options of provisioning infrastructure resources, based on the trace-based workload temporal analysis. The infrastructure provider provisions the on-premises infrastructure with the infrastructure resources for running high performance computing (HPC) applications, based on a selection from the one or more options by an on-premises infrastructure administrator.

Infrastructure resource provisioning using trace-based workload temporal analysis for high performance computing

A system, method and program product for managing hydrocarbon energy production. A hydrocarbon field modeler models physical characteristics of a hydrocarbon energy field. A load predictor predicts processing workload in modeling the hydrocarbon energy field, and identifying a balanced modeling unit distribution across multiple processors simulating field production. A load distribution unit distributes the modeling units across the processors for a balanced modeling unit distribution. The load predictor and load distribution unit proactively shifts loads to maintain workload balanced throughout the simulation.

Managing hydrocarbon energy production while proactively maintaining a balanced workload

A job in a high performance computing system is executed according to a current scheduler configuration. The job is monitored to determine the job's actual resource usage, the job's performance metric and the job's completion. Responsive to determining that the job completed executing, a difference is computed between allocated resources allocated according to the current scheduler configuration at job submission for execution on the high performance computing system and the job's actual resource usage at the job's completion, wherein the difference is computed for each of N number of jobs that complete executing on the high performance computing system, wherein all of the N number of jobs are associated with a given user. Responsive to determining that the difference for said each of N number of jobs meets a criterion, the current scheduler configuration is automatically reconfigured to reduce the allocated resources for the jobs associated with the given user.

A method and computer program product for managing hydrocarbon field production, e.g., petro-chemical reservoir production. The hydrocarbon field is modeled using the finite volume method (FVM) model and the finite element method (FEM). Centroids are located in each FVM cell and each FEM element and overlapping cells are identified. After determining the distance between centroids for overlapping cells, fluid characteristics are mapped to the FEM element centroids, weighted inversely for distance between the respective centroids. A permeability/conductivity weighted average is determined for pore pressure and temperature of sub-volumes clustered around each FEM element node. Field production may be adjusted in response to FEM element node characteristics.

Method of managing petro-chemical reservoir production and program product therefor

Assessing the impact of existing fractures and faults for reservoir management, in one aspect, may comprise employing a numerical mesh to generate a geomechanical model, the numerical mesh representing a geological reservoir and its surrounding regions, the numerical mesh comprising delimitation associated with regions and layering of geology without constraining the numerical mesh to explicitly represent a fault or fracture, initializing the geomechanical model to define initial stress-strain compatible with measured stress in well locations associated with the geological reservoir, generating a fluid-flow model employing the numerical mesh, solving for a coupled solution of the fluid-flow model and the geomechanical model, and employing the solved fluid-flow model and the geomechanical model to assess the impact.

Method to assess the impact of existing fractures and faults for reservoir management

Coupling fluid-flow model and geomechanical model for integrated petroleum systems, in one aspect, may comprise analyzing historical data associated with a reservoir to determine one or more triggering events that trigger abrupt changes in the state of stress of the reservoir solid framework and in the pore pressure. One or more time steps are defined based on the determined triggering events. The fluid-flow model and the geomechanical model are coupled at the one or more defined time steps, e.g., one-way or two-way. Number of iterations may be calculated automatically for the two-way coupling to converge.

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Sao Paulo, Brazil

Eduardo Rocha Rodrigues