Evolving Well Stimulation Optimization Tool with OliFANT: A Pilot Machine Learning Project to Boost National Oil and Gas Production - Utaman, Williams; Gabriella, Indira Frida; Kitra, Seraphine Jeanetra (Indonesian Journal of Energy, 2023)
In Indonesia, for a half decade, the decrease of oil and gas production from 2016 is 4.23% and 3.53% respectively (ESDM, 2021). This production decrease has a domino effect on the investment loss. According to the International Trade Administration, investment in Indonesia’s oil and gas industry in 2019 reached around US$ 12 billion, which was decreasing from around US$ 16 billion in 2016. Such loss is a serious disaster, thus applying digital transformation such as machine learning to the most-used method, well stimulation, is immediately needed. Unfortunately, the implemented well stimulations nowadays are prone to short-lived effects due to the unreliable selection methods, as they do not have any integrated database. This research, as the pilot project, focuses on field data collected in West Indonesia from sandstone and carbonate lithologies, and the type of stimulation used is acidizing. This tool, OliFANT, defines the success of stimulation based on the productivity index before and after stimulation. The method uses geostatistical approaches and optimizing decline curve analysis for analysing and modelling spatially correlated data. The accuracy of the model is validated at a minimum of 75%, which shows its high reliability. It can also forecast the duration effect of the stimulation, additionally it provides the estimation of profit scenarios. The proposed machine learning model adopts an empirical working principle by utilizing reservoir parameters and test data of stimulation, which are inputted into a user-friendly interface after filling in a comprehensive database. In conclusion, the main benefits of using this tool are cutting evaluation time and achieving higher cost-efficiency. This software can be continuously improved by adding more data to widen the variety of the methods. Considering that each field has different types of properties, this tool is built to be adaptable to every reservoir condition. Over and above that, this tool can be implemented for other stimulated wells and be modified for other methods and operations, such as drilling and workover. In the future, it can be a one-stop solution for stimulation plan validation, where data-driven solutions pave the way for success.