Automation of Electrofacies Identification - A Case for Digitalization

Abstract

Abstract The 21st century is a period of rapid advancement in technology and the ever adapting and perhaps proactive companies will be the companies of the future. Advancement in science and in this case 3D reservoir modelling requires the automation of repetitive and mundane tasks to liberate human resources for evaluations and actual problem solving. 3D reservoir modelling is the digital representation of the subsurface in other to predict the behaviour of fluids in the reservoir, and a facies model is key to building a robust 3D reservoir model, as it provides the geometry and relationship of the reservoir sub units. The identification of facies in reservoirs as an input to building reservoir models is done using several data types including core data, seismic data, and well logs. This study focuses on the use of well logs in the identification of facies (electrofacies). Electrofacies are facies defined using a set of well-log responses that characterize a lithologic unit and distinguishes it from others. Standard Industry practice has established the use of gamma ray log signature as predictors of facies and it is this relationship that is explored in this study. In 3D reservoir modelling, the process of facies identification and painting the facies into discrete logs is a repetitive and sometimes harrowing process - especially in a field with several stacked reservoirs and well penetrations. This process takes up valuable time in 3D reservoir modelling and the monotony can also lead to errors that may have a significant impact on the facies model eventually built. At present, the automation available in modelling tools is only able to identify facies using absolute log values; this only discriminates between sandstone and shale in clastic reservoirs. In this study, I present an excel-based application that can ‘recognize’ log shapes and as such, can identify up to five distinct facies: Channel Sands, Upper Shoreface, Lower Shoreface, Heteroliths and Shale in shallow marine settings. This application has been deployed to build a 3D static model for field development planning for the Debby field, Onshore Niger Delta. Significant results were achieved with execution of facies identification and discrete log creation taking less than 5% of the time it would typically take for manual electrofacies definition. This ensured more time availability for important and productive tasks such as uncertainty analysis, sensitivity analysis, and creation of more recovery scenarios.