Characterization of tight-gas sand reservoirs from horizontal-well performance data using an inverse neural network

Characterization of a tight-gas sand formation using data from horizontal wells at isolated locations is challenging due to the inherent heterogeneity and very low permeability characteristics of this class of resources. Furthermore, characterizing the uncontrollable hydraulic-fracture properties along the horizontal wellbore requires financially demanding and time-consuming operations. In this study, a reservoir characterization model for tight-gas sand reservoirs is developed and tested. The model described is based on artificial neural networks trained with a large number of numerical-simulation scenarios of tight-gas sand reservoirs. The model is designed in an inverse-looking fashion to estimate the reservoir and hydraulic-fracture characteristics, once known initial conditions, controllable operational parameters, and observed horizontal-well performance are input. Validation with blind cases by estimating reservoir and hydraulic-fracture characteristics resulted in an average absolute error of 20%. The model was also tested successfully with published data of an average-performing well in the Granite Wash Reservoir. A graphical-user-interface application that enables using the model in a practical and efficient manner is developed. Practicality of the model is also demonstrated with a case study for the Williams Fork Formation by obtaining probabilistic estimates of reservoir/hydraulic-fracture characteristics through Monte Carlo simulation that incorporates the ranges of observed production performance

Development of a data-driven forecasting tool for hydraulically fractured, horizontal wells in tight-gas sands

Tight-gas sand reservoirs are considered to be one of the major unconventional resources. Due to the strong heterogeneity and very low permeability of the formation, and the complexity of well trajectories with multiple hydraulic fractures; there are challenges associated with performance forecasting and optimum exploitation of these resources using conventional modeling approaches. In this study, it is aimed to develop a data-driven forecasting tool for tight-gas sands, which is based on artificial neural networks that can complement the physics-driven modeling approach, namely numerical-simulation models. The tool is designed to predict the horizontal-well performance as a proxy to the numerical model, once the initial conditions, operational parameters, reservoir/hydraulic-fracture characteristics are provided. The data-driven model, that the forecasting tool is based on, is validated with blind cases by estimating the cumulative gas production after 10 years with an average error of 3.2%. A graphical-user-interface application is developed that allows the practicing engineer to use the developed tool in a practical manner by visualizing estimated performance for a given reservoir within a fraction of a second. Practicality of the tool is demonstrated with a case study for the Williams Fork Formation by assessing the performance of various well designs and by incorporating known uncertainties through Monte Carlo simulation. P10, P50 and P90 estimates of the horizontal-well performance are quickly obtained within acceptable accuracy levels