Data Services Platform for Well System Algorithm Management

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Solution Overview

Problem

Developing, deploying, and managing data processing algorithms for real-time data from well systems in oil and gas operations is costly, inefficient, and time-consuming due to the need for handling latency and completeness tradeoffs, algorithm rewriting for different constraints, and managing multiple versions across entities and environments.

Innovation Solution

A data services platform that integrates prebuilt and reusable algorithms into a framework, allowing developers to create and deploy data processing algorithms seamlessly, with features like I/O management, version management, and visualization, decoupling software development from algorithm development, and enabling real-time data ingestion and processing.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If custom data processing algorithms are developed for each application, then specific processing requirements are met, but development cost and time increase significantly

Engineering Contradiction:

Improvealgorithm customizationVSAvoiddevelopment time

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The patent implements a reusable algorithm framework where core data processing algorithms are designed to handle multiple applications and data types. The framework provides universal functions for time-series data processing, event detection, and analytics that can be applied across different well systems and applications, eliminating the need to develop separate algorithms for each use case.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system pre-configures algorithm templates with common processing logic, data validation rules, and performance optimizations before deployment. These pre-built algorithms are ready to be instantiated and customized for specific applications, significantly reducing development time compared to building algorithms from scratch.

Inventive Principle:

Principle #10Preliminary action

2Adaptability or versatility

If data processing algorithms are rewritten for different constraints, then specific application requirements are satisfied, but maintenance complexity increases

Engineering Contradiction:

Improveconstraint adaptationVSAvoidalgorithm management complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The algorithm framework is segmented into modular components including data ingestion modules, processing modules, output modules, and configuration modules. Each component can be independently configured and maintained, allowing constraints to be adjusted in isolation without affecting the entire algorithm system.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system manages different application constraints through configurable parameters rather than rewriting algorithm logic. Algorithms accept parameter inputs that define specific constraints, data sources, and processing rules, allowing the same algorithm core to adapt to different requirements through parameter adjustment alone.

Inventive Principle:

Principle #35Parameter changes

3Adaptability or versatility

If multiple versions of algorithms are managed across entities and environments, then customization needs are met, but version control and deployment become inefficient

Engineering Contradiction:

Improvealgorithm versioningVSAvoiddeployment efficiency

Core Design Contradiction:

Adaptability or versatilityVSProductivity

Solution Approach 1:

The framework uses template-based algorithm copies where a master algorithm definition serves as a template that can be instantiated multiple times across different entities and environments. Changes to the master template automatically propagate to all instances, ensuring consistency and simplifying version control while allowing local customizations through configuration parameters.

Inventive Principle:

Principle #26Copying

4Speed

If real-time data processing is implemented with strict latency requirements, then data timeliness is improved, but data completeness may be compromised

Engineering Contradiction:

Improvedata processing speedVSAvoiddata completeness

Core Design Contradiction:

SpeedVSLoss of information

Solution Approach 1:

The algorithm framework dynamically adjusts processing parameters based on incoming data characteristics and system load conditions. It can switch between different processing modes (e.g., strict real-time, near-real-time, batch) and adjust window sizes, sampling rates, and aggregation levels to balance latency requirements against data completeness for different applications.

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS20240426209A1Data services platform for managing data processing models and real-time data from well systems

Publication Date: 2024.12.26 HALLIBURTON ENERGY SERVICES INC

AI summary

Systems, methods, and apparatus, including computer programs encoded on computer-readable media, for implementing a data services platform for real-time data obtained from well systems are disclosed. The data services platform may receive a first data processing algorithm for processing real-time data from one or more well systems. The first data processing algorithm may be created using a template associated with the data services platform. The data services platform may integrate the first data processing algorithm into a framework of the data services platform that provides a plurality of data management functions for managing the real-time data. The data services platform may receive a configuration for the first data processing algorithm that includes one or more parameters associated with the real-time data from at least a first well system, and may configure the framework that provides the plurality of data management functions with the one or more parameters.