Module 1: Introduction to Petroleum Sector Data Analytics

• Overview of the petroleum industry and data analytics
• Importance of data in the petroleum sector
• Key metrics and KPIs in the petroleum industry
• Data collection methods and sources
• Practical session: Introduction to Python and Power BI

Module 2: Data Preprocessing and Cleaning

• Data importing and exporting in Python
• Data cleaning techniques
• Handling missing data and outliers
• Data transformation and normalization
• Practical session: Cleaning and preprocessing petroleum sector data

Module 3: Exploratory Data Analysis (EDA)

• Introduction to EDA and its importance
• Descriptive statistics and data summarization
• Data visualization techniques in Python
• Identifying patterns and trends in data
• Practical session: Performing EDA on petroleum sector data

Module 4: Data Visualization with Power BI

• Introduction to Power BI and its features
• Creating interactive dashboards
• Designing effective visualizations
• Advanced visualization techniques
• Practical session: Building dashboards for petroleum sector data

Module 5: Predictive Modeling

• Introduction to predictive modeling
• Linear regression and logistic regression
• Decision trees and random forests
• Model evaluation and validation
• Practical session: Building predictive models for petroleum sector data

Module 6: Time Series Analysis

• Introduction to time series analysis
• Time series decomposition
• Forecasting techniques
• Evaluating forecast accuracy
• Practical session: Analyzing time series data in the petroleum sector

Module 7: Reservoir Analytics

• Importance of reservoir analytics in the petroleum industry
• Reservoir simulation techniques
• Predicting reservoir performance
• Analyzing reservoir data
• Practical session: Reservoir data analysis using Python and Power BI

Module 8: Production Analytics

• Role of production analytics in the petroleum sector
• Analyzing production data
• Identifying production bottlenecks
• Optimization techniques for production processes
• Practical session: Production data analysis in Python and Power BI

Module 9: Big Data Analytics in the Petroleum Sector

• Introduction to big data and its applications
• Big data tools and technologies
• Handling large-scale petroleum sector data
• Implementing big data analytics solutions
• Practical session: Big data analytics in the petroleum sector

Module 10: Environmental Analytics

• Importance of environmental analytics in the petroleum industry
• Monitoring environmental impact
• Analyzing environmental data
• Compliance with environmental regulations
• Practical session: Environmental data analysis in Python and Power BI

Module 11: Health and Safety Analytics

• Role of health and safety analytics in the petroleum sector
• Analyzing health and safety data
• Predicting and preventing incidents
• Enhancing safety protocols
• Practical session: Health and safety data analysis in Python and Power BI

Module 12: Supply Chain Analytics

• Importance of supply chain analytics in the petroleum industry
• Analyzing supply chain data
• Optimizing supply chain processes
• Predicting supply chain disruptions
• Practical session: Supply chain data analysis using Python and Power BI

Module 13: Financial Analytics

• Role of financial analytics in the petroleum sector
• Analyzing financial data
• Cost analysis and optimization
• Predictive financial modeling
• Practical session: Financial data analysis in Python and Power BI

Module 14: Customer Analytics

• Importance of customer analytics in the petroleum industry
• Customer segmentation techniques
• Analyzing customer behavior and preferences
• Enhancing customer satisfaction and loyalty
• Practical session: Customer data analysis using Python and Power BI

Module 15: Drilling Analytics

• Role of drilling analytics in the petroleum sector
• Analyzing drilling data
• Optimizing drilling operations
• Predictive modeling for drilling performance
• Practical session: Drilling data analysis in Python and Power BI

Module 16: Maintenance Analytics

• Importance of maintenance analytics in the petroleum industry
• Predictive maintenance techniques
• Analyzing maintenance data
• Reducing downtime and maintenance costs
• Practical session: Maintenance data analysis in Python and Power BI

Module 17: Compliance Analytics

• Role of compliance analytics in the petroleum sector
• Analyzing compliance data
• Ensuring regulatory compliance
• Enhancing audit processes
• Practical session: Compliance data analysis in Python and Power BI

Module 18: Case Studies and Real-World Applications

• Case studies of successful petroleum sector data analytics projects
• Lessons learned and best practices
• Challenges and opportunities in petroleum sector data analytics
• Future trends and innovations
• Practical session: Applying learned concepts to real-world case studies

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