An Interactive 5-Day Training Course
Machine Learning for Drilling Engineering
Models for Drilling Data
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Duration
5 Days
Price
$ 3,000
Starting From
24 Jan 2027
Machine Learning for Drilling Engineering
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Machine Learning for Drilling Engineering - DC-MLD-PEA27
| Code | Date | Time | Duration | Location | Currency | Early Bird Fee Per Person |
|---|---|---|---|---|---|---|
| DC-MLD-PEA27 | 24 - 28 Jan 2027 | 10 AM CST | 4 Hours / Day |
Online |
USD |
3000 |
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Machine Learning for Drilling Engineering
This course covers applied machine learning for drilling. Participants study problem framing, drilling data preparation and labelling, feature engineering from rig data, regression and classification methods, anomaly detection, rate of penetration and failure prediction applications, real-time deployment, model validation, and the limits of machine learning in drilling.
Description
Machine learning suits drilling problems where large volumes of data exist and patterns are hard to capture with physics alone: predicting rate of penetration, detecting developing stuck pipe, anticipating equipment failure, classifying operations automatically and flagging anomalies across many wells. It does not replace drilling engineering, and knowing where it helps is most of the skill.
This course covers applied practice. It starts with problem framing and whether a machine learning approach is appropriate, then covers drilling data preparation: labelling, time alignment, handling of real-time streams and the splitting of data that avoids leakage. It covers feature engineering from rig data, then the main method families: regression for rate of penetration and parameter optimisation, classification for event and failure prediction, and anomaly detection for early warning. It then covers real-time deployment and inference at the rig, model monitoring and drift, validation practice and the failure modes that produce impressive results and useless models, and the combination of physics-based and data-driven approaches.
Rate of penetration prediction is the most attempted and most over-claimed application. Models trained on one field with specific bits, formations and rigs often perform well in validation and poorly elsewhere, because the relationship they learned was specific to that context. Testing on held-out wells rather than held-out rows is what exposes this.
Event prediction depends on labels. Predicting stuck pipe requires knowing exactly when previous events began, which is rarely recorded accurately: the daily report gives the date, not the hour, and the developing conditions preceded the recorded event by hours. Label quality limits model quality more than algorithm choice.
Deployment is a separate problem from modelling. A model that runs in a notebook on historical data is not the same as one running on a live data stream with gaps, sensor failures and latency, producing alerts that a driller will act on. Most drilling machine learning projects stop before this step, which is why so few are in routine use.
By the end of this training, participants will be able to:
- Frame drilling problems as machine learning tasks and judge when to use them.
- Prepare drilling data sets, including labelling and time alignment.
- Split data correctly to avoid leakage across wells and time.
- Engineer features from rig and real-time drilling data.
- Apply regression methods to rate of penetration and parameter problems.
- Apply classification methods to event and failure prediction.
- Apply anomaly detection for early warning across wells.
- Deploy models to real-time data streams and handle data gaps.
- Monitor model performance and manage drift and retraining.
- Validate models honestly and combine physics-based and data-driven approaches.
The course is instructor-led and hands-on, using Python and real drilling data. Participants build models for rate of penetration, event prediction and anomaly detection, then test them on held-out wells and examine why performance drops. Considerable attention is given to leakage, labelling and validation. Basic Python familiarity is assumed.
Organisations sending participants to this training will:
- Apply machine learning to drilling problems where it genuinely helps.
- Avoid investing in models that cannot work in the field.
- Improve early detection of developing drilling problems.
- Build realistic expectations of what machine learning delivers.
- Prepare data and infrastructure for deployed models.
Participants will:
- Be able to frame and build machine learning models for drilling.
- Be able to prepare and label drilling data correctly.
- Be able to validate models and detect leakage.
- Understand deployment and model monitoring.
- Know when physics-based methods are the better answer.
- Drilling engineers
- Drilling performance and real-time engineers
- Data scientists working with drilling data
- Digital and analytics teams
- Service company technical staff
- Engineers with Python experience moving into analytics
Module 1: Framing Drilling Problems
- Where machine learning helps in drilling
- Defining the target and the decision it supports
- Label availability and quality
- Comparison with physics-based methods
- Setting realistic expectations
Module 2: Drilling Data Preparation
- Real-time and daily report data
- Time alignment and resampling
- Labelling events and failures
- Handling sensor gaps and failures
- Train, validation and test splitting by well and time
Module 3: Feature Engineering
- Features from drilling parameters
- Rolling statistics and trend features
- Mechanical specific energy and derived features
- Formation and well context features
- Feature selection and importance
Module 4: Regression Applications
- Rate of penetration prediction
- Parameter optimisation models
- Tree-based and ensemble methods
- Evaluation metrics and baselines
- Generalisation across fields
Module 5: Classification and Event Prediction
- Stuck pipe and pack-off prediction
- Equipment failure prediction
- Class imbalance handling
- Prediction windows and lead time
- Converting predictions into alerts
Module 6: Anomaly Detection
- Unsupervised methods for drilling data
- Detecting deviation from normal operation
- Multivariate anomaly detection
- Alert thresholds and false positive management
- Operator trust and alarm fatigue
Module 7: Real-Time Deployment
- From notebook to live data stream
- Inference on streaming data
- Handling latency, gaps and bad data
- Integration with rig displays and workflows
- Operational constraints
Module 8: Monitoring and Drift
- Monitoring model performance in operation
- Detecting drift
- Retraining strategy
- Versioning and governance
- When to retire a model
Module 9: Validation and Hybrid Approaches
- Leakage and how it happens in drilling data
- Validating on held-out wells
- Interpreting results honestly
- Combining physics and machine learning
- Case studies of successes and failures
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.
