Cloud Resource Allocation for Reservoir Simulation Runtime

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Solution Overview

Problem

In the oil and gas industry, reservoir simulations often face challenges with limited budgets and tight deadlines, requiring efficient resource allocation in cloud computing environments, where manual configuration and deployment of resources can lead to suboptimal performance and increased costs due to under- or unutilized resources.

Innovation Solution

The implementation of techniques to model and predict simulation runtimes based on a small sample of time steps, allowing for dynamic reallocation of resources during the simulation process, optimizing resource usage and reducing costs by adjusting RAM allocation and reconfiguring resources based on changing simulation parameters.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If manual configuration and deployment of cloud resources is used for reservoir simulations, then resource allocation can be adjusted flexibly, but suboptimal performance and increased costs occur due to under- or unutilized resources

Engineering Contradiction:

Improvemanual configuration flexibilityVSAvoidsimulation completion efficiency

Core Design Contradiction:

Ease of operationVSProductivity

Solution Approach 1:

The system enables self-service through automated runtime modeling and resource optimization. The runtime model automatically learns from simulation data and predicts optimal resource allocation without manual intervention, while the system self-adjusts cloud resource deployment based on real-time simulation progress and predicted runtime, eliminating the need for manual configuration while maximizing resource utilization efficiency

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system implements feedback mechanisms by continuously monitoring simulation performance metrics and using the runtime model to predict outcomes. This feedback loop allows the system to automatically adjust resource allocation based on actual simulation behavior, ensuring optimal performance without manual intervention and preventing both under- and over-utilization of resources

Inventive Principle:

Principle #23Feedback

2Reliability

If specialized training and knowledge is required to configure and deploy resources, then resource deployment can be optimized, but deployment time increases and interruptions occur when manual redeployment is needed

Engineering Contradiction:

Improveresource configuration accuracyVSAvoiddeployment and reconfiguration time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs self-configuration by automatically generating optimal cloud resource deployment parameters based on the runtime model's predictions. This eliminates the need for specialized manual configuration while maintaining high reliability, as the system autonomously determines the optimal resource allocation without human intervention or training requirements

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The runtime model performs preliminary analysis of simulation requirements before actual execution, predicting optimal resource allocation in advance. This preliminary action allows the system to pre-configure resources optimally, avoiding the need for time-consuming manual reconfiguration during simulation execution

Inventive Principle:

Principle #10Preliminary action

3Ease of operation

If cloud computing resources are deployed without understanding of time or cost to complete simulation, then resource deployment is simplified, but budget overruns and deadline misses occur

Engineering Contradiction:

Improveresource deployment simplicityVSAvoidsimulation completion time

Core Design Contradiction:

Ease of operationVSLoss of time

Solution Approach 1:

The system provides continuous feedback through the runtime model, which predicts simulation completion time and resource utilization metrics. This feedback enables automated adjustment of resource allocation to stay within budget constraints and meet deadlines, maintaining operational simplicity while ensuring time and cost efficiency

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system dynamically adjusts resource allocation based on real-time simulation progress and runtime model predictions. This dynamic optimization allows the system to automatically adapt resource deployment to changing simulation requirements, ensuring both simplicity of operation and adherence to time and budget constraints without manual intervention

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS20210019189A1Methods and systems to determine and optimize reservoir simulator performance in a cloud computing environment

Publication Date: 2021.01.21 LANDMARK GRAPHICS CORP

AI summary

Disclosed are systems and methods for allocating resources for executing a simulation. These include receiving a simulation for execution, calculating an initial runtime of an initial time step of the simulation, determining a total runtime of the simulation based on the initial runtime, selecting a runtime model based on the initial time step, total runtime, or a parameter of the simulation, identifying, based on the selected runtime model, an allocation of a resource providing an increase in runtime speed, allocating the identified resource, and executing the simulation using the allocated resource.