Abstract

This paper presents a rigorous method to scale rate-time profiles of multi-fractured horizontal wells (MFHW) to a set of reference reservoir and completion properties. Scaling is required for development of accurate production forecasts and typical well production profiles (type wells) with minimum uncertainty.

We use a modified version of commonly-used type curves, notably the Wattenbarger type curve, to fit production data. The fit requires that production profiles exhibit a negative half slope during transient linear flow followed by negative unit slope during boundary-dominated flow on a rate vs. material balance time plot. The horizontal and vertical displacement required to fit observed data to the type curve define the scaling factors for individual wells.

We present a set of equations to scale a given well's production profile to that of a reference well with specified effective permeability, fracture length, lateral length, net-pay thickness, drawdown, and fracture stage spacing. Just as we can scale a group of wells to common reference conditions, we can also rescale to predict the performance of a well with specified properties, such as average properties determined from wells analyzed or wells with completion designs different from those analyzed. While it is common and clearly important to normalize (scale) rate profiles for lateral length, we demonstrate that it is also crucial to scale production profiles in rate and time to account for differences in permeability, fracture spacing and thus, the duration of flow regimes. We provide examples of successful scaling based on publically reported production data from Marcellus, Barnett, Niobrara, Midland Basin (Wolfcamp) and Eagle Ford resource plays.

Most of the methods used to assess the performance of MFHW's in resource plays rely on having a statistically significant number of analogs. However, datasets of sufficient size are often either unavailable or limited by the large variety of completion designs and well performance characteristics. Our approach to scale production can dramatically increase the number of analogs available to characterize a geologically similar area and thus reduce the uncertainty in production forecasts.

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