Spatial data exists practically everywhere, including the oil and gas industry. Several factors drive the distribution of the location of oil and gas wells: performance of existing wells, available acreage, need for operators to maintain a certain amount of production and to stay competitive. Some of the important parameters to consider in the design of a completion job for an unconventional oil and gas well are the length of lateral (and by extension perforated interval), number of stages, total pounds of proppants, total volume of fluid pumped, injection pressure and injection rate. In big data analytics and building of a regression model to capture the effects of these parameters on oil production, the practice has been to analyze wells in similar formations or similar basins, even when these wells are miles apart. Due to the presence of spatial autocorrelation and non-stationarity in such data, the recommended practice should be to take these spatial dependencies into account by using geographically weighted regression (GWR). In this paper, we present an application of GWR in location-based regression modeling to capture the effect of these completion parameters on the first six months of oil production in 5700 wells in the Bakken and Three Forks formation in North Dakota. GWR builds different models for every location, leading to a spatial distribution of variable coefficients. This model is well suited to capture both local and global variations in our dependent variable. We also compare the results obtained with that of three other models: multiple regression model, artificial neural network model and universal kriging. Just like the use of kriging, GWR model resulted in a much-improved prediction of oil production as captured by the goodness-of-fit diagnostics (R squared, AIC, and RMSPE), compared to the other two non-location-based models. We recommend the use of the GWR model in the prediction of oil or gas production when spatial non-stationarity exists.