ISO/IEC JTC 1 SC 42 Artificial Intelligence - Working Group 4
Use Cases & Applications
   12/06/2019

Editor's comments and enhancements are shown in green. [ Reviewed]

The quality of use case submissions will be evaluated for inclusion in the Working Group's Technical Report based on the application area, relevant AI technologies, credible reference sources (see References section), and the following characteristics:

  • [1] Data Focus & Learning: Use cases for AI system which utilizes Machine Learning, and those that use a fixed a priori knowledge base.
  • [2] Level of Autonomy: Use cases demonstrating several degrees (dependent, autonomous, human/critic in the loop, etc.) of AI system autonomy.
  • [3] Verifiability & Transparency: Use cases demonstrating several types and levels of verifiability and transparency, including approaches for explainable AI, accountability, etc.
  • [4] Impact: Use cases demonstrating the impact of AI systems to society, environment, etc.
  • [5] Architecture: Use cases demonstrating several architectural paradigms for AI systems (e.g., cloud, distributed AI, crowdsourcing, swarm intelligence, etc.)
  • [6] Functional aspects, trustworthiness, and societal concerns
  • [7] AI life cycle components include acquire/process/apply.
These characteristics are identified in red in the use case.

No. 36 ID: Use Case Name: Powering Remote Drilling Command Centre
Application
Domain
Manufacturing
Deployment
Model
Cloud services
StatusIn operation
ScopeOil and Gas Upstream (Deployed in 150 Oil Rigs and 2.5 Billion+ Data Points each)
Objective(s)Automatic generation of Daily Performance Report, reduction in overall drilling time, cut down Invisible Loss Time and improve rig asset management
Short
Description
(up to
150 words)
It is important for a drilling contractor to have real time monitoring of rig parameters to optimize operations. The customer lacked granular insights during drilling, could not ascertain the root cause of non-productive time, and manual interpretation of signals led to missing of anomalies further degrading performance.
Complete Description Cerebra product extracted and ingested different types of signals from surface and downhole sensors to perform near real-time processing. More than 170 vital signals every second from each oil rig were processed by Cerebra to provide near real time insights into drilling operations. This was achieved by handling Data Format and Data Extraction standards and Cerebra’s Visualization Studio provides the flexibility of generating customized asset utilization reports, thus helping the oilfield engineers to understand the root causes of non-productive time and better utilize the assets on field. Rig specific utilization reports, and weekly and monthly utilization reports helped to plan drilling operations improving drilling efficiency.
StakeholdersOil and Gas Upstream sector; Environment, Humans
Stakeholders'
Assets, Values
Systems'
Threats &
Vulnerabilities
Challenges to accountability, security threats
Performance
Indicators (KPIs)
Seq. No. Name Description Reference to mentioned
use case objectives
1 Invisible Loss Time Indicates the lost time of the asset in being idle or off or unplanned downtime Asset Utilization Reports indicate the effectively utilized time there indicating the lost time and their causes
2 Overall drilling time The time spent on one drilling job inclusive of the all downtimes Real Time visibility into operations gives the operations early warnings to take actions immediately.
3 Initial success rate After initial training, the success rate needs to be acceptable such that the system can be put in the production line.
AI Features Task(s)Knowledge processing & discovery
Method(s)Utilization and Performance Evaluation
HardwareApplication Server: 64 GB RAM/ 16 Core / 500 GB HDD, Data Server: 128 GB RAM/ 16 Core, 3 TB HDD
Topology
Terms &
Concepts Used
ISO 13379, 13381, 13374, 14224, 17359
Standardization
Opportunities
Requirements
  • Mandate of the key sensors based on the type of equipment
    Based on the type of equipment, the makers need to have the basic set on sensors imbibed onto the system. E.g. for a pump – it is important to measure the input flow and output flow rates, vibrations, rotation speed, lube oil temperature and pressure. This will guide the equipment manufactures to provide their customers and their data products to capture the minimum required data and understand the equipment performance.
  • Mandate for the organizations to expose the minimum and key parameters.
    The equipment owners need to enable the basic set of sensors for the equipment health and performance which are required for monitoring the asset from any failures.
  • Standards for data formats
    Each organization has a different way of capturing data and storing them in different formats. Due to which the solutions are not scalable across organizations though the product behind them is same. It takes customised efforts each time.
  • Guidelines for deciding the sampling frequency based on the type of data
    We see a need to have a specific set of guidelines to capture data at a minimum required sampling frequency. For e.g. a vibration sensor should capture data at least at 1 ms.
  • Guidelines for feature engineering
    There must be guidelines as to how the features need to be engineered for AI models. Lack of this would lead to more black box models not explaining how the models behave the way they do.
  • Guidelines for standardization of event types and codes
    There are multiple events which occur for an asset or in a manufacturing plant. Guidelines would help people capture the data in a similar fashion helping the industry to benchmark against one another and at industry level we can understand, which events are the most critical.
  • Guidelines for standardization of fault and error codes for an equipment or process
    Similar to events, it is also useful to capture fault, failure and error codes in a standard way.
  • Process guidelines for event related data (maintenance and work orders)
    Guidelines would help people capture the data in a similar fashion helping the industry to benchmark against one another and at industry level we can understand, which events are the most critical.
Challenges
& Issues
Compliance of organizations
Societal Concerns Description Promoting sustainable industries, and investing in scientific research and innovation, are all important ways to facilitate sustainable development.
SDGs to
be achieved
Industry, Innovation, and Infrastructure
Data Characteristics
Description Data from an Oil & Gas Rig
Source Drilling Equipment
Type Time-Series Sensor Data
Volume (size)
Velocity 2.5 Billion+ Data Points each day
Variety Machine Data
Variability
(rate of change)
Quality
Scenario Conditions
No. Scenario
Name
Scenario
Description
Triggering Event Pre-condition Post-Condition






Training Scenario Name:
Step No. Event Name of
Process/Activity
Primary
Actor
Description of
Process/Activity
Requirement






Specification of training data
Scenario Name Evaluation
Step No. Event Name of
Process/Activity
Primary
Actor
Description of
Process/Activity
Requirement






Input of Evaluation
Output of Evaluation
Scenario Name Execution
Step No. Event Name of
Process/Activity
Primary
Actor
Description of
Process/Activity
Requirement






Input of Execution
Output of Execution
Scenario Name Retraining
Step No. Event Name of
Process/Activity
Primary
Actor
Description of
Process/Activity
Requirement






Specification of retraining data
References
No. Type Reference Status Impact of
use case
Originator
Organization
Link
1 Web Page Upstream Sensor Data + Big Data Analytics = Game Changer in Oil n Gas industry Published Use case take from this case study Flutura Business Solutions Pvt. Ltd. Link
2 Web Page Cerebra creating game changing impact on upstream outcomes Published Use case take from this case study Flutura Business Solutions Pvt. Ltd. Link

  • Peer-reviewed scientific/technical publications on AI applications (e.g. [1]).
  • Patent documents describing AI solutions (e.g. [2], [3]).
  • Technical reports or presentations by renowned AI experts (e.g. [4])
  • High quality company whitepapers and presentations
  • Publicly accessible sources with sufficient detail

    This list is not exhaustive. Other credible sources may be acceptable as well.

    Examples of credible sources:

    [1] B. Du Boulay. "Artificial Intelligence as an Effective Classroom Assistant". IEEE Intelligent Systems, V 31, p.76-81. 2016.

    [2] S. Hong. "Artificial intelligence audio apparatus and operation method thereof". N US 9,948,764, Available at: https://patents.google.com/patent/US20150120618A1/en. 2018.

    [3] M.R. Sumner, B.J. Newendorp and R.M. Orr. "Structured dictation using intelligent automated assistants". N US 9,865,280, 2018.

    [4] J. Hendler, S. Ellis, K. McGuire, N. Negedley, A. Weinstock, M. Klawonn and D. Burns. "WATSON@RPI, Technical Project Review".
    URL: https://www.slideshare.net/jahendler/watson-summer-review82013final. 2013