Creating More-Representative Type Wells
Authors: Freeborn Randy , Russell Boyd 
Published: April 2016

This paper presents original research on how to improve the predictive ability of type wells used in evaluating unconventional-resource-drilling programs by extending traditional Monte Carlo calculations. The paper addresses three critical questions engineers must answer before constructing a type well: which well to use in the construction, the relative importance (weighting) appropriate for each well, and how to adapt the results to reflect certainty (e.g., P10, P50, or P90).

The proposed method involves determining the aggregated distribution of estimated ultimate recovery (EUR) for the specified number of wells by running a statistically significant number of Monte Carlo trials. From this distribution, one can determine mean EURs for the desired type-well certainties, such as P50 or P90. Additional Monte Carlo trials yielding the desired mean EUR will help determine which wells to average. Monte Carlo sampling results in several hundred trials that match the desired EURs. The relative frequency of well selection from these trials defines the weighting factor and thus the relative importance of each well. Type wells result from a weighted averaging of history and production from the selected wells.

Engineers can use this new methodology to prepare production-profile forecasts for the evaluation of multiwell unconventional-resource-drilling programs. They can also gain an understanding of the effect that aggregation will have on their evaluation work.

Our research concludes that current type-well-construction practices may not be appropriate for evaluating future drilling because the production profiles for the wells used to build the type well may differ from the production profiles of the planned wells. This paper presents a new method to obtain more-representative type wells. The method permits defining any uncertain parameters, such as EUR, net present value (NPV), or payout time, and then building a type well for various measures of probability of attaining that parameter.

This paper presents new concepts that contribute to the technical knowledge base.

We present a method to calculate probabilistic type wells dependent on the likelihood of drilling wells that make up the distribution of EUR (or other parameters). The method combines the concepts of aggregation and Monte Carlo simulation to calculate weighting factors for use when averaging rate/time profiles. We introduce the paradigm that one can build probabilistic type wells from distributions of parameters other than EUR. EUR is only suitable for determining reserves. When conducting an evaluation, one may want a more-relevant type well; for instance, one that examines the ability to self-finance by representing the probability of recovering a percentage of capital expenditure (Capex) in the first year. We present a method to scale rate/time profiles from representative wells to the fracture geometry and reservoir quality of future drilling prospects. These scaled wells are for use in building type wells. The scaling algorithms also prove useful in estimating fracture geometry and reservoir quality where it is unknown.