AI-driven Production Optimization and Surveillance

Module 1: Production Surveillance Problem Definition and AI Use Cases

Translating production losses, deferment, water handling, solids, and intervention questions into analytics use cases

Separating forecasting, anomaly detection, classification, diagnosis, and optimization problems

Defining success criteria that engineers and operations teams can validate

Prioritizing use cases by value, data readiness, operational risk, and implementation complexity

Module 2: End-to-End Architecture for AI-enabled Production Optimization

Architecture from field sensors, SCADA, historians, production allocation, lab data, and work management systems

Data pipelines for ingestion, cleansing, feature stores, model execution, alerting, and dashboard delivery

Roles and handover points between production, operations, IT, data science, maintenance, and facilities teams

Practical controls for cybersecurity, access, auditability, and operational approval workflows

Module 3: Data Preparation from SCADA, Historians, and Production Systems

Aligning tag data, well tests, choke settings, pressures, temperatures, flow rates, and production histories

Handling missing values, bad sensors, shutdown periods, manual entries, outliers, and changing sampling intervals

Building operating context features such as facility mode, artificial lift status, routing, constraints, and interventions

Creating labelled events for water breakthrough, solids symptoms, degradation, maintenance, and operating changes

Module 4: Time Series Foundations and Baseline Surveillance Models

Using trends, rolling statistics, rate of change, seasonality, and operating envelopes for baseline surveillance

Building decline, forecast, and threshold models before advanced machine learning is introduced

Comparing statistical baselines with supervised learning, unsupervised detection, and hybrid engineering rules

Validating models against known events, field notes, production tests, and engineering judgement

Module 5: Production Degradation Forecasting and Early Warning Signals

Recognizing early degradation through pressure response, rate instability, drawdown changes, and lift performance shifts

Forecasting decline and separating reservoir behavior from equipment, facility, and operating mode effects

Designing early warning indicators that reduce false alarms and support timely engineering review

Interpreting model confidence, feature contribution, and recommended investigation actions

Module 6: Water Breakthrough and Solids Accumulation Prediction

Preparing water cut, salinity, pressure, rate, choke, and well test indicators for breakthrough forecasting

Detecting solids buildup through flow instability, pressure losses, separator trends, erosion signals, and intervention history

Building prediction windows that support surveillance meetings and field response planning

Connecting forecasts to sampling, inspection, pigging, chemical treatment, and well intervention decisions

Module 7: Intervention Frequency, Maintenance, and Production Regime Optimization

Linking model outputs to cleaning, stimulation, sand management, artificial lift adjustment, and maintenance decisions

Balancing intervention frequency against production recovery, operational cost, risk, and facility availability

Using scenario analysis to compare choke strategies, routing options, lift settings, and constraint relaxation

Documenting decision rules so optimization recommendations remain traceable and operationally acceptable

Module 8: ML in Production: Monitoring, Drift, Explainability, and Dashboard Integration

Monitoring model performance, data drift, sensor drift, alert quality, and changing field operating conditions

Explaining predictions with feature importance, event timelines, confidence bands, and engineering narratives

Designing dashboard views for engineers, control room teams, supervisors, and management users

Planning SCADA, historian, BI, and workflow integration with escalation rules and human approval gates

Module 9: Practical Case Study Workshop and Implementation Roadmap

Working through cases on degradation detection, water breakthrough, solids buildup, and intervention prioritization

Building a use case canvas with data sources, features, model approach, validation method, and expected decisions

Reviewing governance, model ownership, field acceptance, training needs, and change management requirements

Creating an implementation roadmap from pilot analytics to dashboard handover and operational adoption