The most successful machine learning applications in production are narrow and well defined. Predicting ESP failure within a time window from operating data is a defined classification problem with a measurable outcome. Estimating well rate between tests from pressure and lift data is a regression problem that can be validated against the next test. Detecting wells behaving unlike their own history is anomaly detection. Each has a clear question, available labels and a way to check the answer.

Failures come from the data side far more often than the algorithm side. Labels are wrong because failure dates in the maintenance system are the date of the report rather than the failure. Features leak information from the future. Training and test sets share wells, so the model memorises wells rather than learning the pattern. Class imbalance means a model that predicts no failure for everything scores well. Every one of these produces impressive validation numbers and useless field performance.

The honest position is that machine learning adds most value where there is plenty of consistent data and a repetitive decision, and least where data is sparse and physics is well understood. Combining the two, using physics-based models where they work and machine learning for what they miss, is usually better than either alone. This course teaches that judgement alongside the methods.

By the end of this training, participants will be able to:

• Frame production problems as machine learning tasks and judge when to use them.
• Prepare production data sets, including labelling and handling of gaps and noise.
• Engineer features from production, surveillance and lift data.
• Apply regression methods to estimate rates and performance.
• Apply classification methods to predict failures and events.
• Apply clustering and anomaly detection across well populations.
• Apply time series methods to production data.
• Validate models correctly and avoid leakage, overfitting and imbalance errors.
• Deploy models and monitor their performance in operation.
• Combine physics-based and data-driven approaches appropriately.

Organisations sending participants to this training will:

• Predict equipment failures early enough to act.
• Estimate production where measurement is missing or infrequent.
• Detect anomalies across large well populations automatically.
• Avoid investing in models that cannot work in the field.
• Build realistic expectations of what machine learning delivers in production.

Participants will:

• Be able to frame and build machine learning models for production problems.
• Be able to prepare and label production data correctly.
• Be able to validate models and detect leakage and overfitting.
• Understand failure prediction and anomaly detection in practice.
• Know when a physics-based approach is the better answer.

• Production engineers
• Well surveillance and optimisation engineers
• Data scientists working with production data
• Artificial lift and reliability engineers
• Digital and data teams supporting operations
• Reservoir engineers interested in data-driven methods

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.