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AWS Solutions Library

Guidance for Well Construction Operator Analytics on AWS

Overview

This Guidance shows how drillers and builders of well systems can improve how they gather, access, and use their operational data. Well system construction data is often siloed between the oilfield equipment and services (OFS) industry that produces the data, and the operators who will consume and analyze that data. Not only do the operators experience challenges in obtaining data from OFS, the data they do receive is unreliable, and requires lengthy integration and analysis. This Guidance solves for those challenges by helping operators gather data from a multitude of OFS companies, securely store, and then process the data—all in a single environment. Operators can monitor, visualize, and analyze their operation's data to improve their construction efficiency.

How it works

These technical details feature an architecture diagram to illustrate how to effectively use this solution. The architecture diagram shows the key components and their interactions, providing an overview of the architecture's structure and functionality step-by-step.

Download the architecture diagram

Well-Architected Pillars

The architecture diagram above is an example of a Solution created with Well-Architected best practices in mind. To be fully Well-Architected, you should follow as many Well-Architected best practices as possible.

Amazon S3 and QuickSight were selected for this Guidance because of the capabilities those managed services offer to help operators run and monitor their operational systems effectively when it comes to data.Specifically, QuickSight allows operators to build operational dashboards to track Amazon CloudWatch metrics, which describe the operational health of content delivery services, such as CloudFront, or metrics about objects stored in Amazon S3. These services natively integrate with CloudWatch, which helps operators to seamlessly centralize logs and metrics.

Read the Operational Excellence whitepaper

Lambda@Edge, a feature of CloudFront, Amazon AppFlow, and AWS Secrets Managerall work together to help operators maintain the integrity of their data, manage user permissions, and establish controls to detect security events. With Lambda@Edge, operators can enforce custom authorization flows before a request can enter the AWS environment. It also segments data uploaded from different sources into different Amazon S3 prefixes to enforce data isolation boundaries. Amazon AppFlow uses Secrets Manager to store sensitive information required to connect to a third-party application, such as passwords and authentication tokens.

Read the Security whitepaper

The capabilities of Amazon S3, AWS Glue, and Athena enhance thereliability of operator's workloads as these services support a distributed system design. For example, operators' query data stored in Amazon S3 with Athena is based on table definitions in AWS Glue. These Regional AWS services automatically scale across multiple independent failure zones to preserve application availability in the event of a rare, but possible, Availability Zone failure.

Read the Reliability whitepaper

This Guidance enhances the performance efficiency for operators through a structured and streamlined allocation of resources. For instance, it walks operators through the process to partition data in the AWS Glue table based on context added by Lambda@Edge, such as which company uploaded the document and which asset the document relates to. Partitioning this data optimizes Athena query time by reducing the volume of data scanned for each query.

Read the Performance Efficiency whitepaper

This Guidance uses Amazon S3 for persistent data storage. That is, an Amazon S3 Lifecycle policy automatically moves objects into the Amazon S3 Intelligent-Tiering storage class, reducing storage expenses by automatically moving objects to the cost-optimal storage classes based on access patterns.

Read the Cost Optimization whitepaper

The Lambda function that processes files scales up based on how quickly files are added to the Amazon S3 file ingestion bucket. After those files are processed, the Lambda function scales back down. This automatic scaling feature of Lambda right-sizes compute usage based on demand, which minimizes compute usage. Preventing the over-provisioning of compute reduces energy usage, minimizing the workload’s environmental impact.

Read the Sustainability whitepaper

Disclaimer

The sample code; software libraries; command line tools; proofs of concept; templates; or other related technology (including any of the foregoing that are provided by our personnel) is provided to you as AWS Content under the AWS Customer Agreement, or the relevant written agreement between you and AWS (whichever applies). You should not use this AWS Content in your production accounts, or on production or other critical data. You are responsible for testing, securing, and optimizing the AWS Content, such as sample code, as appropriate for production grade use based on your specific quality control practices and standards. Deploying AWS Content may incur AWS charges for creating or using AWS chargeable resources, such as running Amazon EC2 instances or using Amazon S3 storage.

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