An Interactive 5-Day Training Course
Drilling Analytics & Digital Twin
Analytics and Models for Drilling
Secure your spot now!
Duration
5 Days
Price
$ 3,500
Starting From
31 Jan 2027
Drilling Analytics & Digital Twin
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Drilling Analytics & Digital Twin - DC-DAT-PEA27
| Code | Date | Time | Duration | Location | Currency | Early Bird Fee Per Person |
|---|---|---|---|---|---|---|
| DC-DAT-PEA27 | 31 Jan - 04 Feb 2027 | 10 AM CST | 4 Hours / Day |
Online |
USD |
3500 |
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Drilling Analytics & Digital Twin
This course covers drilling analytics and digital twin applications. Participants study analytics workflows for drilling data, performance and non-productive time analysis, benchmarking methods, physics-based models as digital twins, real-time model calibration, hydraulics and torque and drag twins, scenario simulation, integration with operations and the assessment of analytics value.
Description
Drilling analytics turns the data a drilling organisation already collects into decisions: where time is lost, which practices work, which wells underperform and why. A digital twin extends this by running calibrated models of the well alongside the real operation, so that measured and predicted behaviour can be compared continuously and deviations identified early.
This course covers both. It starts with analytics workflows for drilling data and the performance analysis that most organisations need first: time breakdown, flat time, non-productive time and benchmarking across wells, rigs and crews. It covers the statistical care benchmarking requires and the presentation of results that change behaviour. It then covers digital twins built on physics-based models, hydraulics, torque and drag and wellbore stability, the calibration of those models against real-time data, and their use for deviation detection and scenario simulation. The course closes with integration into drilling operations and decision processes, and the honest assessment of what analytics and twins deliver.
Performance analysis beats prediction in most organisations. Knowing that twelve per cent of the last campaign's time went into one recurring flat time activity is immediately actionable; a model predicting rate of penetration usually is not. The analytics that change behaviour tend to be simple and well presented rather than sophisticated.
Benchmarking needs statistical honesty. Comparing rigs or crews without accounting for well difficulty, depth, hole size and formation produces rankings that reflect the wells rather than the performance, and once people see that, the whole exercise loses credibility. Normalisation and stated uncertainty are what make benchmarks trusted.
A digital twin is a calibrated model, not a dashboard. Its value lies in predicting what should be happening, comparing that against what is happening and flagging the difference. A hydraulics twin that predicts standpipe pressure within a narrow band will show a developing washout or pack-off before the driller sees it, and that is the capability worth building.
By the end of this training, participants will be able to:
- Build analytics workflows for drilling data.
- Analyse time breakdown, flat time and non-productive time.
- Benchmark performance across wells, rigs and crews with proper normalisation.
- Present analytics results in forms that change behaviour.
- Build physics-based models suitable for digital twin use.
- Calibrate models against real-time drilling data.
- Apply hydraulics and torque and drag twins for deviation detection.
- Use twins for scenario simulation and planning.
- Integrate analytics and twins into drilling operations and decisions.
- Assess the value delivered and avoid analytics that go unused.
The course is instructor-led and practical. Participants analyse real campaign data to find where time is lost, build benchmarks with normalisation, and work with calibrated hydraulics and torque and drag models to detect deviations in real cases. Cases cover analytics programmes that changed operations and those that did not. Participants are encouraged to bring their own data for discussion.
Organisations sending participants to this training will:
- Find and remove recurring time losses across campaigns.
- Benchmark performance credibly and act on the results.
- Detect developing problems early through model deviation.
- Use scenario simulation in planning and operations.
- Avoid analytics investment that produces unused dashboards.
Participants will:
- Be able to analyse drilling performance across wells.
- Be able to benchmark with proper normalisation.
- Understand digital twins and model calibration.
- Be able to use models for deviation detection.
- Be able to assess analytics value honestly.
- Drilling engineers and performance engineers
- Real-time operations staff
- Digital and analytics teams
- Drilling managers and superintendents
- Service company technical staff
- Well planners using performance data
Module 1: Drilling Analytics Workflows
- Data sources and preparation
- Analysis questions worth asking
- Workflow structure and repeatability
- Tooling options
- Common analytics failures
Module 2: Performance Analysis
- Time breakdown by activity
- Flat time analysis
- Rate of penetration analysis
- Tripping and connection performance
- Identifying the largest losses
Module 3: Non-Productive Time Analysis
- Classifying and coding non-productive time
- Cause analysis across wells
- Cost quantification
- Recurring versus one-off losses
- Prioritising improvement effort
Module 4: Benchmarking
- Normalisation for well difficulty
- Comparing rigs, crews and campaigns
- Statistical significance and sample size
- Technical limit and best composite well
- Avoiding misleading comparisons
Module 5: Presenting Analytics
- Presenting results to drilling teams
- Visualisations that work
- Avoiding blame in performance reporting
- Driving action from analysis
- Sustaining attention over a campaign
Module 6: Physics-Based Models for Twins
- Hydraulics models
- Torque and drag models
- Wellbore stability models
- Model inputs and uncertainty
- Model selection for twin use
Module 7: Calibration and Deviation Detection
- Calibrating models against real data
- Expected ranges and tolerances
- Detecting deviation and its causes
- Alert design and thresholds
- Operator interpretation
Module 8: Scenario Simulation
- Simulating planned operations
- What-if analysis for parameter changes
- Contingency evaluation
- Planning applications
- Limits of simulation accuracy
Module 9: Integration and Value
- Integrating analytics into daily operations
- Interfaces with real-time centres
- Roles and ownership
- Measuring value delivered
- Sustaining analytics beyond the pilot
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.
