Review of Stimulation and Completion Activities and Trends in the United States Shale Plays: Permian Basin Case Study

The hydraulic fracturing designs, drilling, and completion trends are undergoing continuous change as the oil and gas industry pushing the boundaries of the traditional designs to increase wells productivity and meet the energy demand. The lateral length in addition to the amount of proppant and water used in well stimulation have witnessed a paradigm shift over the past few years. This paper represents a large-scale descriptive analysis of the stimulation and completion trends in the Permian Basin between the first quarter of 2013 and the second quarter of 2018. The aim of this analysis is to shed a light on the trends in completion and stimulation and to show the statistical values of each parameter over the studied period. The data sources have been compiled from two sources. FracFocus is the first source for stimulation parameters. It is publically accessible but needs rigorous data processing to render useable parameters to be integrated in this study. The second data source is DrillingInfo which is a commercial database. The completion and production data in this study were obtained from there and integrated with the FracFocus data. The compiled data base for the Permian Basin has more than 15,000 wells covers the Midland, Delaware, and the Central Basin Platform. Analyzing the trends in the presented visualizations elucidate an obvious growing trend over time in the amount of stimulation proppant and water. The overall trends of stimulation and completion in the Permian Basin are presented here in addition to the trends in pad drilling and how the density of the wells in a single pad site has been increasing over the past few years. Large data set utilization will demonstrate a sensible and comprehensive representation of the trends in the studied area. This work will give the readers an established baseline and will serve as a reference point to many of the completion and stimulation parameters by comparing the statistical values such as the median, mean, and standard deviation of each parameter of interest during the analyzed period