StatoilHydro and other oil companies are introducing Integrated Operations (IO) in the management of their oil and gas assets. According to OLF, IO has an estimated value potential of 300 billion NOK on the Norwegian Continental Shelf. IO includes increased use of real-time data, more collaboration in multidisciplinary teams, and increased automation. With the increased use of real-time data, operators may experience information overload.This thesis focuses on the ``Daily Production Optimization'' (DPO) work process at StatoilHydro. A structured workflow application, called SemTask, is presented. Its main purpose is to help operators overcome the information overload problem by suggesting which data sources to use in different situations. It supports operators in executing the DPO work process, ensuring that the right tasks are done at the right time.SemTask uses ontologies in OWL to describe workflows, data sources, and workflow execution states. The workflow ontology is based on the OMG-standard Business Process Modeling Notation (BPMN). The ontologies form an extendable, flexible model. We propose using rules in Jena, a Semantic Web framework, to implement a workflow execution engine. A prototype that shows that this leads to an elegant solution was created.The SemTask model, and how it deals with the DPO scenario as well as possible further extensions, is discussed.SemTask offers an extendable solution for data source suggestions and active help to operators during execution of workflows in Integrated Operations.