Abstract
In the oil and gas industry, vast numbers of simulations are commonly utilized to evaluate production scenarios for field development and business analysis and optimization. However, the process of compiling the obtained prediction profiles and of performing the required analysis is often carried out manually, which is not only time-consuming but also prone to errors. In this work, we present a framework aimed at automating the streamlined generation and analysis of the aforementioned profiles within a fully digital environment.
The workflow introduced conducts a comprehensive analysis of the input and output simulation files for each scenario. This includes temporal analysis and processing, such as the reporting of well drilling and activation, and installation of artificial-lift systems over time. Standardization of certain features is crucial to ensuring smooth automation. The framework allows for concurrent evaluation of multiple scenarios, making it suitable for distributed computing. It is important to note that profile archiving facilitates the creation of interactive dashboards for subsequent analysis and optimization. These dashboards can prove to be valuable tools in achieving desired business outcomes.
The proposed methodology was implemented using Python to extract and manipulate generic simulation input and output data and generate the prediction profiles. A standardization stage was performed beforehand by collaborating with the involved teams. The profiles had to be outputted in a pre-specified format and were subject to a series of validation tests. In all cases, the generated profiles were successfully validated. The computation time for a single profile, which typically takes several hours or even days, was reduced to approximately one hour and sometimes even less than that. It should be noted that parallel computing of all profiles can introduce an additional speed-up factor, reducing the wall-clock time frequently by at least one order of magnitude. The profiles were saved in a repository where data visualization and business-intelligence software could be utilized to extract various types of information for sound and robust decision-making, for example, through recommender systems that optimize capital expenditures and other costs.
The digitalization, integration and analysis of simulation output from a large number of scenarios can be a rather challenging enterprise due to the sheer volume and diversity of data generated by simulations. To overcome these challenges, advanced data analytics can be applied within a high-performance computing environment to extract meaningful insights and drive effective decision-making, enabling companies to stay competitive in an increasingly dynamic and uncertain market.
