1. A method, comprising: gathering a first data collection regarding a plurality of completed horizontal wellbores, the first data collection comprising gas chromatograph measurements of alkanes, mud log data, rate of penetration, average mud weight, total gas, bottomhole temperature, vertical depth, azimuth, well inclination, or lateral orientation or any combination thereof; gathering a second data collection regarding an uncompleted horizontal wellbore, the second data collection comprising gas chromatograph measurements of alkanes, mud log data, rate of penetration, average mud weight, total gas, bottomhole temperature, vertical depth, azimuth, well inclination, or lateral orientation or any combination thereof; applying a first portion of the first data collection to a plurality of artificial neural networks stored onto a non-transitory computer readable medium, wherein the first portion is gathered from a first set of completed horizontal wellbores from the plurality of completed horizontal wellbores, wherein the first set of the plurality of completed horizontal wellbores is less than all of the plurality of completed horizontal wellbores; applying one or more genetic algorithms stored onto the non-transitory computer readable medium to train each of the plurality of artificial neural networks in order to generate a plurality of predictive models, each of the plurality of predictive models providing an estimate of wellbore production based on the first data collection; identifying a best predictive model from the plurality of predictive models by applying a second portion of the first data collection to the plurality of predictive models, wherein the second portion is gathered from a second set of completed horizontal wellbores from the plurality of completed horizontal wellbores, wherein the second set of completed horizontal wellbores is different from the first set of completed horizontal wellbores; applying the gathered second data collection regarding the uncompleted horizontal wellbore and a plurality of available wellbore completion parameters to the best predictive model to identify a set of wellbore completion parameters from the plurality of available wellbore completion parameters that optimizes estimated wellbore production based on the best predictive model; outputting the set of wellbore completion parameters identified using the best predictive model in human-intelligible form; and hydraulically fracturing the uncompleted horizontal wellbore based on the set of wellbore completion parameters identified using the best predictive model.