An Interactive 5-Day Training Course
Drilling Data Management & Quality Control
Clean, Usable Drilling Data
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Duration
5 Days
Price
$ 3,000
Starting From
01 Nov 2027
Drilling Data Management & Quality Control
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Drilling Data Management & Quality Control - DC-DDM-PEA27
| Code | Date | Time | Duration | Location | Currency | Early Bird Fee Per Person |
|---|---|---|---|---|---|---|
| DC-DDM-PEA27 | 01 - 05 Nov 2027 | 10 AM CST | 4 Hours / Day |
Online |
USD |
3000 |
Need this for a group? We deliver the same course in-house — face-to-face at your location or online — tailored to your assets and team level. Contact info@peassociations.com
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Drilling Data Management & Quality Control
This course covers drilling data management and quality control. Participants study data sources and structures, standards and taxonomies, daily reporting data, time and depth coding, validation rules, master data and well identifiers, databases and architecture, historical data clean-up, analytics readiness, governance and the use of data for performance and planning.
Description
Drilling organisations generate large volumes of data and use surprisingly little of it. Daily reports, real-time streams, service company deliverables, surveys, logs and cost records exist in different systems with different identifiers, inconsistent coding and no agreed definitions, which makes analysis across wells slow and unreliable.
This course covers how to fix that. It starts with drilling data sources and structures, then covers standards, taxonomies and operation coding, with emphasis on the time and depth coding that underpins performance analysis. It covers validation rules and automated quality checks, master data, well identifiers and the problems caused when they are inconsistent, and database architecture and storage. It then covers historical data clean-up, preparing data for analytics and machine learning, data governance including ownership and stewardship, and the use of well-managed data for performance benchmarking, planning and lessons learned.
Operation coding is where performance analysis lives or dies. If one rig codes an activity as drilling and another as flat time, comparing them is meaningless; if trouble time is coded inconsistently, non-productive time analysis is unusable. A clear taxonomy, applied consistently and checked, is the single highest value piece of drilling data management.
Well identifiers cause more problems than they should. The same well appears under different names in the drilling system, the cost system, the survey database and the geological database, and joining those records becomes manual work. A master identifier, maintained and used everywhere, removes a large fraction of the effort in any cross-system analysis.
Analytics readiness is mostly data work. Machine learning on drilling data fails more often from inconsistent labels, missing context and unvalidated measurements than from algorithm choice. Organisations that want analytics value usually need to spend their first effort on data quality, not on models.
By the end of this training, participants will be able to:
- Identify drilling data sources, structures and their uses.
- Apply data standards and taxonomies to drilling operations.
- Design and apply consistent time and depth operation coding.
- Build validation rules and automated quality checks.
- Manage master data, well identifiers and cross-system mapping.
- Design database architecture and storage for drilling data.
- Plan and execute historical data clean-up.
- Prepare drilling data for analytics and machine learning.
- Establish data governance, ownership and stewardship.
- Use managed data for benchmarking, planning and lessons learned.
The course is instructor-led and practical. Participants work with real drilling data sets containing the usual problems, building coding structures, validation rules and clean-up workflows. Governance and standards are covered with examples from operating companies. Participants are encouraged to bring their own data problems for discussion.
Organisations sending participants to this training will:
- Make drilling data usable for analysis across wells and campaigns.
- Reduce manual effort in reporting and analysis.
- Improve the reliability of performance benchmarking.
- Prepare the organisation for analytics and machine learning.
- Establish governance that keeps data quality from degrading.
Participants will:
- Understand drilling data sources and their problems.
- Be able to design coding structures and validation rules.
- Be able to clean and prepare historical data.
- Understand master data and identifier management.
- Be able to establish and maintain data governance.
- Drilling engineers and performance engineers
- Drilling data and information management staff
- Digital and analytics teams
- Reporting and planning staff
- Service company data personnel
- Managers responsible for drilling data
Module 1: Drilling Data Sources
- Daily reports and morning reports
- Real-time data streams
- Service company deliverables
- Surveys, logs and subsurface data
- Cost and procurement data
Module 2: Standards and Taxonomies
- Industry data standards
- Operation and activity taxonomies
- Code definitions and documentation
- Adopting and adapting standards
- Change control on taxonomies
Module 3: Time and Depth Coding
- Coding structures for drilling operations
- Productive, flat and non-productive time
- Depth-based coding
- Consistency between rigs and crews
- Auditing coded data
Module 4: Validation and Quality Control
- Validation rules and automated checks
- Range, consistency and completeness checks
- Flagging and correcting errors
- Quality metrics and reporting
- Preventing bad data at source
Module 5: Master Data and Identifiers
- Well and wellbore identifiers
- Cross-system mapping
- Reference data management
- Equipment and contractor identifiers
- Maintaining master data over time
Module 6: Databases and Architecture
- Data storage options
- Structured and unstructured data
- Integration architecture
- Access and performance
- Retention and archiving
Module 7: Historical Data Clean-Up
- Assessing historical data quality
- Prioritising clean-up effort
- Clean-up workflows and tooling
- Documenting assumptions and corrections
- Knowing when to stop
Module 8: Analytics Readiness
- Data requirements for analytics
- Labelling and context
- Feature construction from drilling data
- Common causes of analytics failure
- Pilot data preparation
Module 9: Governance and Use
- Data ownership and stewardship
- Policies and standards
- Roles and accountability
- Benchmarking and planning applications
- Lessons learned systems
- Measuring data management performance
Upon successful completion of this training course, delegates will be awarded an official Certificate of Completion issued by the Petroleum Engineers Association (PEA), an ISO 9001:2015 certified training organization. The certificate carries 10 Credits and formally records the total learning hours completed.
Each certificate is signed by the Course Facilitator and the CEO of the Petroleum Engineers Association, and serves as verifiable proof of professional training that delegates can present to employers and professional bodies worldwide.
Frequently Asked Questions
All course bookings made through PEA are strictly non-refundable. By registering for a course, you acknowledge and accept that all fees are payable in full and are not subject to refund under any circumstances, including changes in personal or professional commitments or partial attendance.
PEA reserves the right to make reasonable adjustments to course content, trainers, or schedules where necessary, without entitling delegates to a refund. Comprehensive details of each course — including objectives, target audience, and content — are clearly outlined before enrolment, and it is the responsibility of the delegate to ensure the course's suitability prior to booking.
For any inquiries related to cancellations or bookings, please contact our support team, who will be happy to assist you.
