The revolution of tight-oil development in the United State (US) has led to a substantial increase in oil production the last decade. It was remarkable that the growth of US oil production was not affected significantly by the oil prices crash in mid-2014 and the rates of production were sustained at high levels which was an indication of the efficiency of US tight-oil. In this paper, the factors contributing to this efficiency were investigated.
This paper focuses on one of the main US tight-oil regions: the Permian Basin. The factors contributing to the US tight-oil efficiency were identified by analyzing field and operational data like oil production volumes, fracking proppant amount and well locations. In addition, US national oil production and employment numbers were used. The data used in this paper is limited to the ten-year period between the year of 2008 to 2018. This gives an objective analysis of the results in the heart of the US tight-oil revolution and eliminates the effect of unordinary time of COVID-19 pandemic.
Three factors were identified as the main contributors to the US tight-oil efficiency. The first factor is "well treatment effectiveness". Data from the Permian Basin showed an increase of produced oil volumes with less amount of fracking proppant needed over time. The second factor is "targeted deposits optimization". The performance of the horizontal wells in the Permian Basin were improving over time as wells were converging to certain surface locations. The third factor is "labor-saving innovations". The number of employees in the oil and gas extraction industry were decreasing even with high production rates and rig-count numbers. This observation was general for the US national data which can be correlated indirectly to tight-oil and the Permian Basin data.
Understanding factors contributing to the success of the US tight-oil development can help players in the industry around the world to set the best strategy for continuing the current trend in tight-oil performance. In addition, the analysis presented in this paper illustrate a framework that can be applied in different areas using combination of multiple data sources to identify major observations using data analytics techniques.