Production Data Analysis is a practice wrought with inconsistencies. In the application of any single model, the quantity of answers arrived at by experienced evaluators is often equal to the number of evaluators analyzing the data. The cause of such inconsistency is bias on the part of evaluators. While the colloquial use of bias typically implies systematic error, in this paper we define bias as an expression of belief by the evaluator. With the lack of recognition of bias, no manner exists in which to gauge its accuracy. A method that requires explicit expression of one's bias in rate-time decline behavior can provide an objective manner in which to evaluate it.
In this work, we present a machine learning method to forecast production in unconventional, liquids-rich shale and gas shale wells. Gong et al  developed a method for probabilistic decline curve analysis using Markov-chain Monte Carlo simulation (MCMC) as a means to quantify reserves uncertainty, incorporate prior information (i.e. bias), and to do so quickly. However, their approach resulted in limited use of discrete P10, P50, & P90 production forecasts, as these often did not align with production data. We extend their approach by a) utilizing the Transient Hyperbolic Model (THM) to represent the various flow regimes present in unconventional wells, b) a methods for construction of representative "percentile neighborhood" forecasts, c) construction of Type Curves from an analyzed well set, and d) a modified likelihood algorithm to improve the accuracy of discrete forecasts. The accuracy and calibration of the method is demonstrated by an analysis of 136 wells in the Elm Coulee Field of the Bakken. Quantification of change in rate-time behavior due to completion design, and the inference physical behavior and properties, is demonstrated using a tight oil play in the Cleveland sand formation of the Anadarko Basin, and a shale play in the Wolfcamp formation of the Permian Basin.
We show that this implementation of supervised machine learning, in combination with well-calibrated bias, improves the estimation of uncertainty of the distribution of forecasts. Additionally, hindcasts performed at various time intervals results in accurate Mean 5 year cumulative production. We observe that the "percentile neighborhood" forecasts are reasonable fits of production data comparable to those that may be created by a human evaluator, and that the type curve computed is representative of the decline behavior of the wells upon which it is based. We conclude that, given the speed and accuracy of the process, machine learning is a reliable technology as defined by the SEC, and can replace the process of manual production forecasting by human evaluators for most unconventional wells with consistent trends of production history.