End-to-End Reservoir Surveillance Optimization Through Automated Value of Information Assessments

Abstract

Abstract Effective reservoir management requires continuous surveillance to monitor the reservoir's performance and optimize production. To facilitate this, we propose a new solution that automates the scanning and identification of wells that require surveillance based on the reservoir surveillance guidelines. This approach also assesses the value of information (VoI) provided of the commonly used surveillance methods in the industry. Our proposed solution promises to optimize reservoir surveillance and provide valuable insights to operators for better reservoir management. Our solution consists of a range of reservoir surveillance methods commonly used in the industry. It features two automated components: the first part identifies wells that meet reservoir surveillance guidelines criteria using advanced algorithms. The second component assesses the VoI using a comprehensive decision tree that varies depending on the method. These decision trees incorporate historical data and operational information pertinent to the reservoir to build different probability models. Solution analyzes these probabilities along with economics data to determine the VoI for each well. It is capable of analytical or simulation-based assessments. The output is a ranked list of wells with a corresponding VoI for the surveillance method of interest. The comprehensive and efficient end-to-end reservoir surveillance optimization solution underwent rigorous testing on a real reservoir with a large number of wells. An automated and thorough screening process was executed, and only the wells that met the reservoir surveillance guideline's criteria were selected for assessment using various VoI workflows, including the acquisition of production logs, reservoir saturation logs, and corrosion logs, as well as pressure and rate data such as flowing and static pressures, pressure gradients, multiple rate tests, productivity index, and tracer data. The solution provided a ranked list of wells for each reservoir surveillance method, accompanied by a quantitative VoI value. This methodology enables the prioritization of wells for surveillance operations that yield the highest value from data acquisition in a rapid, structured, and consistent manner, while also reducing biased assessments and minimizing costs associated with performing unnecessary surveillance operations. Furthermore, this solution facilitates prior and posterior impact analysis of the acquired data and enables quantification of the "actual" VoI of the acquired data. With these capabilities, the end-to-end reservoir surveillance optimization solution offers a comprehensive approach to reservoir management that can enhance decision-making processes and increase the efficiency of surveillance operations. The novel aspect of this solution is its systematic evaluation of the VoI for each reservoir surveillance method, which is supported by automated screening and assessment capabilities powered by advanced algorithms. This feature allows for the accurate assessment of a large number of wells, resulting in the identification and ranking of the most important wells for reservoir surveillance operations. As a result, costs can be reduced by avoiding unnecessary measurements and by focusing on wells that improve reservoir understanding and model quality.