Optimization of oil and gas production life cycle through data management

Abstract: Oil and Gas (oilfield) organizations operating locally (in South Africa) and globally are challenged due to economic demands, energy demand increase, regulations change, advanced technologies and complexity in new oil fields discoveries. Due to these challenges, organizations understand the importance of making informed decisions throughout the life cycle of oil and gas production. The usage of a variety of data in making decisions has always been the way of doing business. However, the quality of data used is critical and requires effective Data Quality Management (DQM) processes. Due to the need for sustainability and the competitive nature of the business, organizations are rapidly introducing Data Management (DM) technologies and tools that focus on enhancing business performance. The employed data come in structured and unstructured formats. It is, therefore, critical for any organization to manage such data effectively since poor management of data can lead to poor business performance. Data Governance (DG) is considered a key and initial stage for DM and DQM. It is considered as a base to ensure effective Change Management (CM) and Performance Improvement (PI). This research focuses on confirming and demonstrating that decision making in the Oil, and Gas industry is data driven, and that Data Governance is the main key in Data Management and Data Quality Management throughout the life cycle of oil and gas production. It involved the development of a theoretical based Data Governance framework. The framework was tested and justified through exploratory approach, using EP as a case study. The testing and justification of the framework focused on how EP adopts and apply the proposed Data Governance framework in governing data. Using the developed framework to test the status of Data Governance in the organization, the researcher discovered that Data Governance is a challenge. The main challenge is related to the poor integration of tools, technology, people and processes to effectively manage data and advance business goals. This challenge was identified using different measures such as Data Governance Business Case, Data Quality Attributes, Organization, People, Tools and Technology, and Performance Monitoring Feedback and Improvement as illustrated in the framework. Preliminary results of the research show that the use of this framework within the organization will minimize Data Governance challenge impacts and also ensure consistency in data management throughout the life cycle of oil and gas production.M.Eng. (Engineering Management