Production optimization for mature hydrocarbon fields face several challenging tasks, including the long process of decision making when inactive and low performing wells are being evaluated. The lack of necessary data integration and evaluation of valuable strings, wells, reservoirs can vary between departments making it challenging to bring a prompt and standardized solution.
Therefore, an automated decision-making system has been developed with an open source software with capturing the logics and innovations of subject matter experts and managers. The current assessment is focusing on the automated re-evaluation of inactive strings, low performance wells as well as interventions candidates.
Automation process initially starts with proper data analysis and integration. The advisory system consists of unique, fully automated workflows consuming several data sources. Essential part of data integration is to find the most relevant input parameters, which can be used by the optimization system, and provide recommendations to the end user. A hierarchical control system is added in parallel to data import, providing a long-term and easily sustainable data set for evaluation of wells, reservoirs and fields. This approach leads to a long-term and easily sustainable solution platform, which can be updated and extended whenever new resources are available.
A scoring system is provided encountering the historical and current performance of the existing entities like equipment, wells and reservoir including problems faced during operations. Individual well performance (including down-time, losses, status) is assessed by identifying contributing factors of production decline and failures using data-driven machine learning and fundamental analytic models.
Nagaraju Reddicharla (ADNOC Onshore)
Shamma Saeed Alshehhi (ADNOC Onshore)
Subba Ramarao Rachapudi (ADNOC Onshore)
Indra Utama (ADNOC Onshore)
David Gönczi (Datagration)
Michael Stundner (Datagration)
Georg Schweiger (Datagration)
Document ID: SPE-203022-MS