Module Database Search
| MODULE DESCRIPTOR | |||
|---|---|---|---|
| Module Title | |||
| Energy Data Management and Analytics for Oil and Gas Practitioners | |||
| Reference | CB3053 | Version | 1 |
| Created | November 2023 | SCQF Level | SCQF 9 |
| Approved | March 2020 | SCQF Points | 15 |
| Amended | ECTS Points | 7.5 | |
| Aims of Module | |||
|---|---|---|---|
| This course will enable students to master the fundamental principles and practices of the energy data management and analytics lifecycle. By harnessing the power of analytics and visualisation, students will be prepared to leverage data to improve efficiency and informed decision-making in the oil and gas context. The course emphasises data quality, governance, and specialised insights into managing subsurface exploration and production data. It will equip students to become experts who can confidently contribute to the success of their organisations in developing effective energy data strategies and applying analytics for data exploration and decision-making. | |||
| Learning Outcomes for Module | |
|---|---|
| On completion of this module, students are expected to be able to: | |
| 1 | Understand the fundamentals of energy data management and its importance in oil and gas. |
| 2 | Apply data analytics techniques to extract valuable insights from energy data. |
| 3 | Create compelling data visualisations to communicate insights to stakeholders. |
| 4 | Comprehend the data management lifecycle and its role in data-driven decision-making in oil and gas. |
| 5 | Implement data quality and governance best practices to ensure data reliability |
| 6 | Manage subsurface exploration and production data efficiently, enhancing exploration and production efforts |
| Indicative Module Content |
|---|
| Fundamentals of Energy Data Management; Data Management lifecycle; Energy Data Analytics and Visualisation; Managing Subsurface Exploration and Production Data; Data Quality and Governance |
| Module Delivery |
|---|
| Online Learning. |
| Indicative Student Workload | Full Time | Part Time |
|---|---|---|
| Contact Hours | N/A | 12 |
| Non-Contact Hours | N/A | 138 |
| Placement/Work-Based Learning Experience [Notional] Hours | N/A | N/A |
| TOTAL | N/A | 150 |
| Actual Placement hours for professional, statutory or regulatory body |
| ASSESSMENT PLAN | |||||
|---|---|---|---|---|---|
| If a major/minor model is used and box is ticked, % weightings below are indicative only. | |||||
| Component 1 | |||||
| Type: | Coursework | Weighting: | 100% | Outcomes Assessed: | 1, 2, 3, 4, 5, 6 |
| Description: | Individual Portfolio Assessment. | ||||
| MODULE PERFORMANCE DESCRIPTOR | |
|---|---|
| Explanatory Text | |
| The module is assessed by one component: C1 - Coursework - 100% weighting. Module Pass Mark = Grade D | |
| Module Grade | Minimum Requirements to achieve Module Grade: |
| A | Excellent - Outstanding Performance |
| B | Commendable/Very Good - Meritorious Performance |
| C | Good - Highly Competent Performance |
| D | Satisfactory - Competent Performance |
| E | Borderline Fail - Failure Open to Condonement |
| F | Unsatisfactory - Fail |
| NS | Non-submission of work by published deadline or non-attendance for examination |
| Module Requirements | |
|---|---|
| Prerequisites for Module | None. |
| Corequisites for module | None. |
| Precluded Modules | None. |
| INDICATIVE BIBLIOGRAPHY | |
|---|---|
| 1 | Brown M. Data Mining for Dummies. Hoboken, NJ: John Wiley & Sons; 2014. |
| 2 | HAWTIN, S., 2013. The management of oil industry exploration and production data. London: CreateSpace. |
| 3 | MOREIRA DA SILVA, M., 2020. Power and gas asset management. Cham: Springer. |
| 4 | LADLEY, J., 2020. Data governance how to design, deploy and sustain an effective data governance program. 2nd Ed. London: Academic Press. |
| 5 | WARE,C.,2019.Information Visualization: Perception for Design. 4th ed. Morgan Kaufmann. |
| 6 | Acharya S, Chellappan S. Pro tableau: a step-by-step guide: Apress, 2017. |