Figure 10 A conceptual schema in E’/R (for E&P Oil & Gas Company)
Related Figures (10)
Vaishnavi and Kuechler (2004) illustrate research design methodologies and demonstrate in the industria applications. Due to complex nature of the oil and gas company’s organizational hierarchies (Nimmagadda and Rudra 2005a, Nimmagadda and Rudra 2005b, Nimmagadda and Rudra 2004c, Nimmagadda and Rudra 2004d and Rudra and Nimmagadda, 2005) , many functions, and their associated activities with operationa units (Fig. 1), at times, are difficult to perform and achieve company’s ultimate goals. There is huge demand of structured data and information in large industrial organizations (Winter and Strauch, 2003). Petroleum exploration and production companies are such organizations, where there is immense scope o adapting new technologies and solving problems associated with organizing complex heterogeneous exploration and production data. Crucial decisions made on budgeting for exploration and drilling operations depend on the supply of accurate, timely structured data and information. Manager, as a system designer and or decision maker, needs to obtain, store, process, retrieve and display accurate and precise information. This requires an integrated data supporting system, which allows smooth flow of processed information among different operational units. Oil and gas company as an integrated system, consists of several operational units, identified as sub-systems and again each sub-system into many smaller units as shown in Fig. 1. Organizational data represented in matrix form (Fig. 2) designate that all the functions and operational activities of the oil and gas company that can systematically be organized and needed for mapping and modeling purposes. Conceptual models are prepared using all business entities. It is the responsibility of data analyst of oil and gas industry to identify all the entities, their relationships among different sub-type operational business unit entities. If necessary, relationships may have to be normalized, so that entities involved in the mapping process are conforming to relationships of other associated entities. As an example, Fig. 2 is the process of identifying and analyzing company’s business entities of both specialized and generalized types. As shown in Fig. 3, different business entities that represent an integrated oil and gas company and possessing hierarchical, relational and networking nature of structural relationships have been demonstrated. Authors examine various super-type entities of exploration and production in the next section. Figure 2: Functions and activities of the oil and gas company, identifying business entities As examined in Figs.2 and 3, various business entities are building blocks of an integrated business syste1 scenario showing how complex view of business entities of oil and gas company can simply be segregate into generalized and specialized entities. Figure 3: Meta data model presentation showing super type and subtype business entities D’Orazio and Happel (1996) and Rob and Coronel (2004) discuss several database designs demonstrating different types of data models in industrial scenarios. An entity relationship model has been drawn among the identified business sub-type entities. As depicted in Fig. 3, designer uses bottom-up as a specialized entity and top-down as a generalized entity. Exploration as a generalized business entity is further divided into smaller units as specialized entities. An attempt has been made to interrelate the super type with its associated sub-type entities as shown in Fig. 4 through common attributes of all entity types. R1 — R10 are relationships assembled among business entities, represented as associative entities. While mapping entity- relationships, data analyst incrementally builds ER models further to extend associative relations as entities through extended entity-relationship (EER) mapping approach. The precise definitions of generalized and specialized entities, ER and EER will be discussed in the forth-coming sections. So) inear_tvna Figure 4: An entity relationship (ER) model showing specialization and generalization Figure 5: (a) Entity relationship (ER) models of exploration activity and (b) drilling activity Geophysics, geology, VSP, reservoir are key operational activities, having one-to-many relationships among exploration activities. There are attributes derived for each and every specialized entity as well as in the generalized exploration entity. As an example, petroleum system is a part of exploration entity, again inherent as an associative sub-type entity. A brief description of this petroleum system entity is discussed. Figure 6: (a) Entity relationship (ER) model of production business activity and (b) technical business activity Wells and geological formations tested, type of activation of sick well, secondary recovery type are key attributes, besides, shot location, latitude, longitude and elevation of the current producing well data. Men, machinery money and other constraints, are identified as entities with corresponding attributes. Shot location, latitude, longitude and elevation are key data attributes that can be associated with other business subsystems. Logical model has been generated involving these entities and relationships as shown in Fig. 6a. Data mapping is done through one-to-one and one-to-many relationships among these entities. Unlike generalization, specialization is top-down process in which, one or more subtypes of the super-type and formation of super-type/subtype relationships have been examined. As shown in Fig.7, an entity type named, ‘drilled well’ has several attributes. An attribute called, formation is multi-valued, because there may be more than one formation with an associated formation name with its ID. Drilled Well is a generalized with specialized exploratory or development subtypes. ER logical model suggests that formation and its ID are also associated with specialized subtype entities. A new relationship may be evolved because of these associated relationships between entities. Figure 7: Specialization of a “Drilled Well” entity As shown in Fig.8a, total relationships are denoted by putting a dot (.) on the side of the relationship facing the constrained entity type. It is likely to have relationships that are total on both sides. This extension not only provides more expressiveness in database design, but also provides mechanism to enforce updates. If the constrained entity is updated, the relationships are also updated and vice versa. If an employee is hired to work in an oil and gas company, employee’s name must be entered in the database with the work to which he or she is associated. If the driller is deleted, some other driller must be associated with the assignment; otherwise entity “well” must be deleted from the database. Similar situation can be narrated for a petroleum system, reservoir qualities and quantities are entered in the database periodically. Figure 8: (a) Oil and gas company business entities showing total relationships and constraints and (b) aggregations at type levels entity-relationship (EER) model, the basic ER model is extended to include the super type/sub-type relationships and remove the inconsistencies that arise while linking the specialized and generalized data structures. Figure 9: (a) Business entities showing generalization entity and (b) its properties with multiple classifications in E’/R Figure 11: A process model of subsystems concept a warehouse environment. Data pertained to petroleum system elements in complex geological settings of different sedimentary basins of Middle East (Seber, et al. 2000), East and West Africa and Asia-Pacific regions can logically and physically organized in the form of a meta-model for accommodating in a warehouse. This contribution will facilitate the effective data mining of warehoused exploration and production data by users responsible for decision making in the oil and gas industry.
