The current methodologies for describing type wells can often be biased toward the producing well data set from which the analogue is being described, and furthermore, may also be biased toward the empirical data. For an evaluation team, this can be problematic because the well set is biased to the current producing conditions of the wells, the completion techniques utilized within the dataset, the current effective well spacing, and the geological environment that the dataset has tested. The generation of predictions outside of the observed parameters will be bounded by the solutions already presented. This bias will create either an over or under prediction of flow rates and the estimated ultimate recovery (EUR) of reserves. Furthermore, the reliance on purely empirical data divorces the evaluation from the elements of physics, i.e. Darcy's Law, that control the production and the ultimate recovery. This bias may then ultimately lead to reduced value and the loss of reserves due to the inefficient recovery of hydrocarbons. It is imperative that techniques be developed that can combine the physics of a petroleum system, the empirical production data, and the bias within the empirical well set.
This paper will set forth a methodology and workflow that will attempt to reconcile production, physics, and uncertainties that are inherent in the production of hydrocarbons from multi-fractured horizontal wells. This methodology will make use of Monte Carlo techniques to build probability distribution functions of common inputs and reservoir engineering software that combines rate transient analysis and analytical or numerical models, to better describe a type well and the range of possible outcomes in a type well area. The goal of this paper is to walk through a typical example evaluation that teams face when investigating a new production horizon and to utilize the probabilistic methodology described herein to determine a range of outcomes that will capture the expected performance of that particular reservoir.