Hybrid Approach Proves Effective in Multiwell Forecasting in Unconventionals

Abstract

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper URTeC 3855422, “RGNet for Multiwell Forecasting in Unconventional Reservoirs,” by Zhenyu Guo, SPE, and Sathish Sankaran, SPE, Xecta Digital Labs, and Ying Li, The University of Tulsa. The paper has not been peer reviewed. The aim of the complete paper is to propose a hybrid approach that combines physics with data-driven approaches for efficient and accurate forecasting of the performance of unconventional wells under codevelopment. The method the authors propose is the reservoir graph network (RGNet) model. By reducing system complexity while maintaining fundamental physics, the model provides an efficient and accurate way to model, history-match, and predict unconventional wells. Compared with a full-physics model that takes from hours to days to run, the described model only takes from seconds to minutes. Developing unconventional reservoirs is a complex process involving well targeting, timing, spacing, and completion design for horizontal wells with hydraulic fractures. Among commonly used methods, decline curve analysis (DCA) can execute quick well-performance analysis without considering the complex physics in unconventional reservoirs piece by piece. The convenient aspect of DCA is that practitioners can obtain forecast results quickly by providing historical production data. However, because DCA has some strong assumptions on operational conditions and does not incorporate the key physics of the flow, it may fail to predict accurately in many situations. As a simple, fast tool for analyzing the capacity of a reservoir, rate transient analysis (RTA) has been used to model and forecast unconventional wells. Both pressure and rate data are considered to generate solutions that are more credible than those generated by DCA, where only rate information is used. The drawback of RTA is related to significant assumptions used to derive analytical solutions (e.g., homogeneous reservoir and simple planar fractures with uniform properties). Moreover, it is not an easy task to acquire some of the information required by RTA. Full-physics reservoir simulation models are used as the most-rigorous approach to understand dynamics of unconventional reservoirs because they allow for different complexities for modeling unconventionals. However, because it takes tremendous effort for information gathering, geological/fracture modeling, and history matching, applying this approach to large-scale reservoirs is not tractable. Based on the idea of diffusive time of flight (DTOF), the RGNet model was developed for reservoir modeling, history matching, forecasting, and optimization. It can model multiple wells with communications. In this work, the authors incorporate pressure dependency of pore volumes and transmissibility into RGNet while modeling unconventional reservoirs. The basic idea of the described model comes from the DTOF transformation that converts the 3D diffusivity problem into a 1D problem. The complete paper provides several equations that enable this conversion.