Data Lake Refinement for Exploration Production Consistency
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The oil and gas industry faces challenges in making exploration and production data consistent and accessible, leading to inefficient decision-making and production inefficiencies. Additionally, the growth of cloud-based computing complicates the packaging of insights for delivery to customers.
Innovation Solution
The implementation of a method that ingests exploration and production data into a data lake, applies transformations to the data, and tracks these transformations, allowing for the reproduction of data changes and enabling more efficient data consumption by clients.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If data is stored in original format in data lake, then data accessibility is improved, but data consistency deteriorates
Solution Approach 1:
The patent segments data management into distinct layers: raw data storage (data lake) and transformed data storage (data warehouse). This segmentation allows original format data to be preserved for accessibility while transformed consistent data is maintained separately for reliability, resolving the contradiction between accessibility and consistency.
Solution Approach 2:
The patent introduces transformation processes and metadata tracking as intermediaries between raw data and consumed data. These intermediaries ensure data consistency by applying standardized transformations while maintaining traceability, allowing both accessibility and consistency to coexist.
2Reliability
If transformations are applied to data, then data quality is improved, but processing time increases
Solution Approach 1:
The patent applies transformations preliminarily during data ingestion into the data lake, rather than waiting until data is needed. This preliminary action ensures data quality is established upfront, reducing processing time when data is subsequently accessed or analyzed.
Solution Approach 2:
The patent implements continuous transformation processes that operate on incoming data streams in real-time. This continuity eliminates batch processing delays and ensures data quality maintenance without significant time loss, as transformations occur continuously rather than intermittently.
3Loss of information
If data tracking is implemented, then data provenance is improved, but system complexity increases
Solution Approach 1:
The patent implements lightweight metadata copies that track data provenance information without duplicating the entire data processing system. These metadata copies record transformation histories and data lineages, providing provenance tracking with minimal added system complexity.
4Power
If cloud-based computing grows, then computing power is improved, but data packaging difficulty increases
Solution Approach 1:
The patent creates a universal data packaging framework that works across diverse cloud computing environments. This framework provides standardized interfaces and transformation processes that can be applied universally, reducing packaging difficulty despite growing cloud computing power and heterogeneity.
Data Source
AI summary
Methods, apparatus, systems, and computer-readable media are set forth for receiving data from a client device, the data associated with an operation occurring at an exploration and production system, ingesting the received data into a data lake, applying one or more transformations to the ingested data prior to consumption of the data, and tracking the one or more transformations made to the ingested data.