| Summary: | Due to explosive growth of data and information, exploration and production manager's strategic planning, decision-making process, development and operational control activities have become complicated. One of the causative factors of this complexity is insufficient, improper and untimely information flow among different operational units. Exploration and production are type entities of any petroleum producing company. Data structures of petroleum exploration and production entities are often in heterogeneous form. Data integration and information sharing are key issues of petroleum exploration and production business. For the purpose of generating an integrated business data model, often, exploration, drilling, production, technical, coordination, personnel and administration; and finance and accounting are typical business entities considered for data integration. Our present study confines to exploration and production data management systems only. Though, presence of a complete petroleum system is very much warranted, but sustained petroleum production depends on the existence of reservoir qualities and their spatial extents. If any one of the elements of the petroleum system is missing, it is a mere waste of time and money to venture in those sedimentary basins. However, petroleum system is often poorly understood or inadequately integrated. Authors examine this issue with different perspective and view that integrated management of exploration and production data can seriously be handled by data warehousing technology. In this paper, an attempt is made to integrate the exploration and production data using ER (entity-relationship) and EER (extended entity-relationship) modelling approaches for warehousing purposes. The logical models illustrated in the current research, make possible, developing future implementation data models. The present study will facilitate the analysis of resources data for better understanding of data integration process in the oil and gas companies. Data warehousing will further assist us in defining better data mining models for knowledge building and thus for effective exploration operational decision-making process.
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