Module 1: Foundations of Petroleum Data and Analytics

• Overview of the petroleum sector value chain (Upstream, Midstream, Downstream)
• Understanding typical data types: Time-series (SCADA), Depth-based (Logs), and 3D Seismic
• Key performance indicators (KPIs): Oil/Gas/Water production, Water Cut, Recovery Factor
• Introduction to Python packages essential for the sector (Welly, lasio, Striplog)
• Practical session: Initializing the Python environment and loading a basic well production history time series.

Module 2: Python Environment Setup and Core Libraries

• Setting up the analytical environment (Anaconda, Jupyter Notebooks)
• Mastering Pandas for managing large, multi-indexed production and sensor data
• Leveraging NumPy for fast, vectorized calculations common in engineering formulas
• Data structure conversion and best practices for code efficiency
• Practical session: Writing Python functions to calculate daily oil, gas, and water rates from cumulative production data using Pandas.

Module 3: Data Acquisition and Preparation for E&P Datasets

• Techniques for reading specialized well log data formats (LAS files)
• Handling time-depth conversion and aligning disparate time-series data streams
• Addressing data quality issues: Sensor drift, spikes, and extended downtime periods
• Data imputation and interpolation strategies for consistent log and production records
• Practical session: Using the lasio library to load multiple well log files and normalizing the curves for consistent analysis.

Module 4: Exploratory Data Analysis (EDA) of Production Trends

• Creating informative time-series plots to visualize production decline and seasonality
• Generating cross-plots (e.g., porosity vs. permeability) for reservoir characterization
• Using statistical distributions to analyze the variability of reservoir parameters
• Identifying and segmenting wells based on production profile shapes and decline rates
• Practical session: Generating a visualization report showing oil, gas, and water production trends for a set of wells, highlighting key events like workovers.

Module 5: Well Performance Modeling and Decline Curve Analysis (DCA)

• Theoretical foundation of Arps' Decline Models (Exponential, Hyperbolic, Harmonic)
• Implementing automated curve fitting using optimization techniques in Python (SciPy)
• Calculating key DCA parameters: Initial rate (), Decline rate (), and Economic Limit ()
• Estimating reserves (EUR) and projecting future cash flow based on the decline model
• Practical session: Applying a Hyperbolic Arps model to a real-world well's production data and forecasting the Estimated Ultimate Recovery (EUR).

Module 6: Advanced Time Series Forecasting for Oil and Gas Prices

• Stationarity testing (ADF) and differencing to prepare price data for modeling
• Implementing Autoregressive Integrated Moving Average (ARIMA) and SARIMA models
• Utilizing advanced models like GARCH for modeling price volatility and risk
• Integrating macroeconomic exogenous variables (e.g., global demand) into forecasting models
• Practical session: Building and evaluating a SARIMA model to forecast the monthly price of WTI crude oil, incorporating lag features.

Module 7: Geospatial Analytics for Field and Asset Mapping

• Handling coordinate systems and converting geographic data (latitude/longitude)
• Using libraries like GeoPandas and Folium for mapping well locations, pipelines, and lease boundaries
• Visualizing production volume and reservoir properties spatially
• Calculating proximity analysis for infrastructure planning and safety zones
• Practical session: Mapping a set of well locations on an interactive map, coloring the markers based on cumulative production volume.

Module 8: Integrated Reservoir Data Analytics

• Data preparation for petrophysical analysis: Calculating shale volume, porosity, and water saturation
• Implementing facies analysis using supervised and unsupervised learning on log data
• Creating well-to-well correlation panels and pseudo-well logs using Python
• Interpreting pressure transient analysis (PTA) data for reservoir characterization
• Practical session: Using log data to calculate key petrophysical parameters and visualizing them in a depth plot.

Module 9: Drilling and Completion Performance Optimization

• KPIs for drilling: Rate of Penetration (ROP), tripping time, and non-productive time (NPT)
• Data cleaning and analysis of daily drilling reports and WITSML data
• Identifying factors influencing ROP using regression analysis
• Developing predictive models to forecast drilling time and cost for future wells
• Practical session: Analyzing a dataset of drilling operations to identify the primary drivers of Non-Productive Time (NPT).

Module 10: Machine Learning for Production Prediction (Regression)

• Feature engineering: Creating predictive features from reservoir, geological, and completion data
• Implementing advanced regression models (Random Forest, Gradient Boosting) to predict initial production (IP)
• Hyperparameter tuning and cross-validation for robust production forecasting
• Model interpretability: Using SHAP values to explain which features drive production success
• Practical session: Training an XGBoost model to predict a well's 6-month cumulative oil production based on static reservoir properties.

Module 11: Machine Learning for Subsurface Classification

• Applying supervised classification algorithms (SVM, K-Nearest Neighbors) to classify lithology from well logs
• Unsupervised clustering (K-Means) for identifying natural reservoir rock types (facies)
• Evaluating classification models: Confusion Matrix, Accuracy, and Classification Report
• Using machine learning to detect anomalies or faults in seismic or sensor data
• Practical session: Implementing a K-Means clustering algorithm on normalized log curves (e.g., Gamma Ray, Resistivity) to segment the data into different rock types.

Module 12: Power BI Desktop and Data Modeling Essentials

• Connecting Power BI to various data sources typical in the sector (OSIsoft PI, historians, SQL databases)
• Using the Power Query Editor (M language) for data transformation and shaping
• Designing star and snowflake schemas for efficient oilfield data modeling
• Establishing relationships between Production, Well Header, and Economic tables
• Practical session: Importing simulated SCADA data and well header information into Power BI and creating a robust data model with appropriate relationships.

Module 13: Advanced DAX for Oilfield Metrics and KPIs

• Deep dive into the CALCULATE function for context modification and complex filtering
• Time intelligence functions for comparing current production to previous periods or forecasts
• Creating rolling averages for smoothing production trends and identifying true decline rates
• Developing measures for key financial metrics like operating cost per barrel equivalent
• Practical session: Writing advanced DAX measures to calculate the 3-month rolling average production rate and the production variance compared to the previous quarter.

Module 14: Developing Interactive Production Dashboards

• Best practices for dashboard design: Layout, color selection, and visual hierarchy for operational reports
• Utilizing various Power BI visuals: Maps, Gauges, Line Charts, and custom visuals
• Implementing interactive slicers, drill-through actions, and bookmarks for dynamic analysis
• Integrating Python visuals (Matplotlib/Seaborn) directly into Power BI reports
• Practical session: Building a dynamic Power BI dashboard to monitor daily well performance, water cut, and remaining reserves.

Module 15: Financial Analytics and Economic Limit Modeling

• Understanding the key financial inputs (Capital Expenditure, Operating Expenditure, Royalty)
• Calculating Net Present Value (NPV) and Internal Rate of Return (IRR) of oil and gas assets
• Modeling the project cash flow and determining the economic limit of a well
• Creating sensitivity analysis tables and visuals to assess the impact of price fluctuations
• Practical session: Developing a Python function to calculate the NPV of a well using forecasted production and price data.

Module 16: Risk and Uncertainty Analysis (Monte Carlo Simulation)

• Introduction to probabilistic methods for reserve and resource estimation
• Defining input variable distributions (e.g., porosity, saturation) for Monte Carlo simulation
• Running Monte Carlo simulations in Python to generate , , and  scenarios
• Visualizing uncertainty using histograms and cumulative distribution functions (CDFs) in Power BI
• Practical session: Implementing a Monte Carlo simulation in Python to estimate the probabilistic reserve range  to ) for a new oil field development.

Module 17: Midstream and Downstream Logistics Analytics

• Data analysis for pipeline operations: Throughput, pressure, and temperature monitoring
• Implementing models for leak detection and anomaly flagging in transport networks
• Optimization techniques for refinery scheduling and feedstock allocation
• Using time series to forecast demand and optimize inventory levels in the supply chain
• Practical session: Analyzing simulated pipeline sensor data to identify and visualize pressure anomalies indicative of potential equipment failure.

Module 18: Capstone Project and Stakeholder Communication

• Defining a final project scope covering a real-world petroleum analytics challenge
• Integrating Python-based DCA or ML model outputs into a Power BI report
• Developing a final presentation focused on clear data storytelling and business recommendations
• Review and critique of analytical reports and dashboard design effectiveness
• Practical session: Completing and presenting the final capstone project, demonstrating the end-to-end analytical workflow.

Requirements

• Participants should be reasonably proficient in English.
• Applicants must live up to Armstrong Global Institute admission criteria.

Terms and Conditions

1. Discounts: Organizations sponsoring Four Participants will have the 5th attend Free

2. What is catered for by the Course Fees: Fees cater for all requirements for the training – Learning materials, Lunches, Teas, Snacks and Certification. All participants will additionally cater for their travel and accommodation expenses, visa application, insurance, and other personal expenses.

3. Certificate Awarded: Participants are awarded Certificates of Participation at the end of the training.

4. The program content shown here is for guidance purposes only. Our continuous course improvement process may lead to changes in topics and course structure.

5. Approval of Course: Our Programs are NITA Approved. Participating organizations can therefore claim reimbursement on fees paid in accordance with NITA Rules.

Booking for Training

Simply send an email to the Training Officer on training@armstrongglobalinstitute.com and we will send you a registration form. We advise you to book early to avoid missing a seat to this training.

Or call us on +254720272325 / +254725012095 / +254724452588

Payment Options

We provide 3 payment options, choose one for your convenience, and kindly make payments at least 5 days before the Training start date to reserve your seat:

1. Groups of 5 People and Above – Cheque Payments to: Armstrong Global Training & Development Center Limited should be paid in advance, 5 days to the training.

2. Invoice: We can send a bill directly to you or your company.

3. Deposit directly into Bank Account (Account details provided upon request)

Cancellation Policy

1. Payment for all courses includes a registration fee, which is non-refundable, and equals 15% of the total sum of the course fee.

2. Participants may cancel attendance 14 days or more prior to the training commencement date.

3. No refunds will be made 14 days or less before the training commencement date. However, participants who are unable to attend may opt to attend a similar training course at a later date or send a substitute participant provided the participation criteria have been met.

Tailor Made Courses

This training course can also be customized for your institution upon request for a minimum of 5 participants. You can have it conducted at our Training Centre or at a convenient location. For further inquiries, please contact us on Tel: +254720272325 / +254725012095 / +254724452588 or Email training@armstrongglobalinstitute.com

Accommodation and Airport Transfer

Accommodation and Airport Transfer is arranged upon request and at extra cost. For reservations contact the Training Officer on Email: training@armstrongglobalinstitute.com or on Tel: +254720272325 / +254725012095 / +254724452588