Data Analytics in Petroleum Engineering – MCQs 50 Score: 0 Attempted: 0/50 Subscribe 1. Which of the following best describes data analytics in petroleum engineering? (A) Manual interpretation of well logs (B) Use of advanced computational methods to extract insights from petroleum data (C) Only visualization of production curves (D) Simple mathematical averaging 2. Which of the following is an example of structured data in petroleum engineering? (A) Well log data (B) Production history (C) Pressure and temperature measurements (D) All of the above 3. Which is an example of unstructured data in petroleum engineering? (A) Core photographs (B) Seismic reports in PDF format (C) Drilling videos (D) All of the above 4. Big Data in petroleum engineering is often described by the “4Vs”. What do they stand for? (A) Volume, Velocity, Variety, Veracity (B) Value, Velocity, Volume, Verification (C) Variables, Value, Variety, Volume (D) Volume, Variance, Validity, Value 5. Which programming language is most widely used in petroleum data analytics? (A) FORTRAN (B) Python (C) COBOL (D) Pascal 6. Machine learning applications in reservoir engineering primarily focus on: (A) Well testing (B) Decline curve analysis (C) Reservoir property prediction (D) All of the above 7. Which of the following is an application of predictive analytics in petroleum engineering? (A) Estimating remaining reserves (B) Forecasting production decline (C) Predicting equipment failures (D) All of the above 8. Time-series analysis is commonly applied to: (A) Production data (B) Core analysis (C) Fluid PVT reports (D) Drilling mud composition 9. Which machine learning algorithm is commonly used for classification problems in well logs? (A) K-means clustering (B) Decision Trees (C) Linear Regression (D) Gradient Descent 10. Which is an unsupervised learning technique useful in facies classification? (A) Logistic regression (B) K-means clustering (C) Linear regression (D) Random forest 11. Which AI method is most suitable for automated fault detection in seismic interpretation? (A) Neural Networks (B) Support Vector Machines (C) Regression Analysis (D) Decision Trees 12. In petroleum drilling, real-time data analytics is often used for: (A) Predicting stuck pipe events (B) Monitoring mud properties (C) Detecting kick and blowout risks (D) All of the above 13. Which visualization technique is most useful for analyzing decline curve trends? (A) Scatter plots (B) Semi-log plots (C) Pie charts (D) Histograms 14. Data-driven decline curve analysis can improve upon traditional methods by: (A) Reducing subjective bias (B) Automating curve fitting (C) Handling large production datasets (D) All of the above 15. Which of the following is NOT a benefit of data analytics in petroleum engineering? (A) Faster decision-making (B) Improved accuracy in predictions (C) Complete elimination of uncertainty (D) Optimized resource allocation 16. In data cleaning, which method is commonly used to handle missing values? (A) Ignoring missing values (B) Imputation with mean/median (C) Predictive modeling (D) All of the above 17. Outlier detection in drilling data helps in identifying: (A) Sensor errors (B) Abnormal events like kicks (C) Unexpected formation behavior (D) All of the above 18. Which type of analytics focuses on answering “What will happen?” (A) Descriptive analytics (B) Diagnostic analytics (C) Predictive analytics (D) Prescriptive analytics 19. Which type of analytics provides recommendations for optimal actions? (A) Prescriptive analytics (B) Descriptive analytics (C) Diagnostic analytics (D) Predictive analytics 20. Feature engineering in well log analysis is important because it: (A) Improves model accuracy (B) Reduces noise in data (C) Captures hidden relationships (D) All of the above 21. Principal Component Analysis (PCA) is commonly used for: (A) Increasing data dimensionality (B) Reducing data dimensionality (C) Predicting production (D) Reservoir simulation 22. Which of the following is a key challenge in petroleum data analytics? (A) Data heterogeneity (B) Data quality issues (C) High computational requirements (D) All of the above 23. Reservoir simulation models can be enhanced using: (A) Data assimilation techniques (B) Machine learning models (C) History matching with analytics (D) All of the above 24. Which of the following is an example of supervised learning in petroleum analytics? (A) Predicting porosity from logs (B) Clustering well facies (C) Detecting anomalies in drilling data (D) Visualizing seismic maps 25. In predictive maintenance of equipment, data analytics is mainly applied to: (A) Pumps (B) Compressors (C) Drilling rigs (D) All of the above 26. Which deep learning technique is most useful for seismic image interpretation? (A) Convolutional Neural Networks (CNNs) (B) Recurrent Neural Networks (RNNs) (C) Support Vector Machines (SVMs) (D) Linear Regression 27. Which type of reservoir data can be classified as “high-velocity data”? (A) Daily production reports (B) Real-time drilling sensor data (C) Seismic acquisition results (D) Core photographs 28. Which open-source library is most popular for petroleum data analytics in Python? (A) NumPy and Pandas (B) TensorFlow (C) Matplotlib (D) All of the above 29. Predictive analytics in enhanced oil recovery (EOR) can help in: (A) Screening suitable EOR methods (B) Forecasting incremental recovery (C) Optimizing injection strategies (D) All of the above 30. Which of the following describes data fusion in petroleum engineering? (A) Combining seismic, well logs, and production data (B) Ignoring outliers in datasets (C) Storing raw data in databases (D) Removing duplicate entries 31. Which optimization algorithm is commonly used in production allocation? (A) Genetic Algorithms (B) Gradient Descent (C) Linear Programming (D) All of the above 32. Natural Language Processing (NLP) in petroleum analytics is applied to: (A) Core photographs (B) Drilling reports in text format (C) Well log LAS files (D) Production curves 33. Which database type is suitable for storing unstructured petroleum data? (A) Relational databases (B) NoSQL databases (C) Data warehouses (D) SQL-based databases 34. Which data visualization dashboard is commonly used in petroleum analytics? (A) Tableau (B) Power BI (C) Spotfire (D) All of the above 35. Which statistical method is commonly applied to reservoir pressure decline analysis? (A) Regression analysis (B) Monte Carlo simulation (C) Hypothesis testing (D) Logistic regression 36. Which advanced technique is often used for uncertainty quantification in petroleum projects? (A) Monte Carlo simulation (B) Decision trees (C) Gradient boosting (D) PCA 37. Which of the following is NOT typically an application of petroleum data analytics? (A) Production optimization (B) Drilling risk prediction (C) Weather forecasting (D) Reservoir property estimation 38. In data analytics, “history matching” refers to: (A) Matching simulation models with past production data (B) Comparing seismic surveys over time (C) Re-logging the same well (D) Matching laboratory data with theory 39. Which ML technique is suitable for predicting reservoir permeability? (A) Linear regression (B) Neural networks (C) Random forests (D) All of the above 40. Which of the following is an example of diagnostic analytics in petroleum engineering? (A) Identifying causes of production decline (B) Forecasting future decline (C) Recommending workover operations (D) Estimating ultimate recovery 41. In real-time drilling, anomaly detection systems rely heavily on: (A) Classification models (B) Clustering algorithms (C) Outlier detection methods (D) All of the above 42. Which optimization technique is often applied in field development planning? (A) Genetic algorithms (B) Linear programming (C) Particle swarm optimization (D) All of the above 43. Which of the following is a key benefit of cloud computing in petroleum data analytics? (A) Scalability (B) Remote accessibility (C) High storage capacity (D) All of the above 44. Real-time production monitoring systems are often integrated with: (A) SCADA systems (B) Cloud-based dashboards (C) AI-driven alerts (D) All of the above 45. Which of the following techniques is useful for probabilistic reserve estimation? (A) Monte Carlo simulation (B) Linear regression (C) Decision trees (D) PCA 46. Ensemble machine learning methods in petroleum engineering are used to: (A) Combine multiple models for better accuracy (B) Replace physical reservoir simulations entirely (C) Eliminate data uncertainty (D) Automatically generate core samples 47. Which AI approach is useful in optimizing hydraulic fracturing designs? (A) Genetic algorithms (B) Neural networks (C) Data-driven simulations (D) All of the above 48. Which of the following is most important for building reliable petroleum data analytics models? (A) Quality of data (B) Quantity of data (C) Computational power (D) Only model complexity 49. Which concept in petroleum data analytics refers to continuous learning and model updating? (A) Static modeling (B) Adaptive analytics (C) Regression analysis (D) PCA 50. The ultimate goal of applying data analytics in petroleum engineering is to: (A) Replace engineers with machines (B) Optimize hydrocarbon recovery and minimize risks (C) Reduce storage cost of data (D) Only visualize large datasets FOUNDATIONAL SUBJECTS (Year 1 – Year 2)Engineering Mechanics (Statics & Dynamics) – MCQsFluid Mechanics – MCQsThermodynamics – MCQsComputer Programming (e.g., MATLAB, Python) – MCQsIntroduction to Engineering – MCQsGeology for Engineers – MCQsTechnical Communication – MCQs CORE PETROLEUM ENGINEERING SUBJECTS (Year 2 – Year 4)Introduction to Petroleum Engineering – MCQsPetroleum Geology – MCQsDrilling Engineering – MCQsReservoir Engineering – MCQsProduction Engineering – MCQsPetroleum Fluid Properties – MCQsWell Logging and Formation Evaluation – MCQsPetroleum Economics – MCQsPetroleum Refining and Processing – MCQs Natural Gas Engineering – MCQsEnhanced Oil Recovery (EOR) – MCQsWell Testing – MCQsReservoir Simulation – MCQsDrilling Fluids and Cementing – MCQsOffshore Petroleum Engineering – MCQsHealth, Safety and Environment (HSE) – MCQsPetroleum Project Management – MCQsCorrosion Engineering – MCQsArtificial Lift Techniques – MCQsPetrophysics – MCQs LABORATORIES & PRACTICALS (Theory-based MCQs can be made from these)Drilling Fluids Lab – MCQsCore Analysis Lab – MCQsReservoir Simulation Lab – MCQsRock and Fluid Properties Lab – MCQsWell Logging Lab – MCQs ELECTIVES (Optional/Advanced)Energy Transition and Sustainability – MCQsUnconventional Resources (Shale, Tight Gas, etc.) – MCQsData Analytics in Petroleum Engineering – MCQsGeographic Information Systems (GIS) – MCQsPetroleum Law and Policy – MCQsPipeline Engineering – MCQsRenewable Energy Integration – MCQs
