Delving into Artificial Intelligence World: Pioneering Machine Learning Platforms to Predict Reservoir Properties

This is the authors' abstract. We don't add key points for this paper.

Digitalization is no longer a word or an ambition that stretch far beyond our reach and understanding. The world in fact is rapidly embracing these changes in daily life. This adaptation is also happening in the oil and gas industry. The oil and gas industry has been adapting and adopting digitalization in many aspects such as migrating hard copy data to soft copy, meetings being held online and classes notes available online, just to name a few. One of the activities in oil and gas is acquiring well log data which are well logs and core data to be specific. There are accumulated challenges faced by the petrophysics and other related department regarding these data. One of the challenges is managing the massive well logs data. Well log data is recorded through two type of conveyances which are logging while drilling and wireline logging. There are various types of data recorded to aid the well log interpreter and team understanding about the reservoirs. These data are recorded in different format and handling the mass data has always been an extensive task by itself. Core data on other hand is limited as compared to well log data. This limitation more often than not hindering the process of decoding the unseen subsurface reservoir. Although these data are important for the team, the whopping costs of acquiring them often drives the team to make wise decisions and prioritize on the importance of the data. There is also integrity issue encountered by the current team on the core data quality as the core was obtained few years back. Another challenge that is worth mentioning is the lack of systematic data format, name, and convention for the data. These challenges call on a solution which can effectively utilize artificial intelligence, ERMAI and COREAI which will be introduced in this paper.