Hybrid Methods for Analysis of Fractured Well Production from Liquids Rich Duvernay Shale

Abstract

Objectives/Scope: In order to maximize the recovery of hydrocarbons from liquids rich shale reservoir systems, the cause and effect relationships between production and the stimulation methods need to be clearly understood. In this study, we integrate a production data regression approach with flow simulation methods to understand the fractured well production behavior and field wide well performance in a liquids rich petroleum system in the Duvernay Basin. Methods, Procedures, Process: Statistical models assume no physical relationship between the model parameters and the response variable, which in this case is produced volumes over a period of time. On the other hand, simulation studies incorporate physical mechanisms of flow to model and predict the production behavior. The simulation models, however, fall short of incorporating all the mechanisms contributing to the production behavior in the complex shale gas reservoir. Thus there is a need for integration of statistical approaches of understanding production behavior along with physics based model and simulation approach. Results, Observations, Conclusions: Multivariate linear regression analysis of the 6 month produced volume and its relationship with parameters such as fracture fluid volumes used, proppant weight placed, and number of stages fractured provides a model with reasonably good correlation. The 6 month produced volumes correlate with large proppant weights, lower fluid placements and greater density of fracture stages. Use of Random Forests machine learning algorithm on the dataset confirms that the total proppant placed, well length completed with fractures have high importance coefficients. In order to examine the well performance using full physical models, fractured well simulations were performed on particular wells using the trilinear model. The trilinear model predictions were compared against other production analyses and the regression model results for consistency. The models showed that in the absence of stress dependent permeability, the production forecast was much higher. Thus, stress dependent permeability appears to be an important factor in the modeling and prediction of production from liquids rich shale reservoirs. Novel/Additive Information: In this study we describe a method to understand the production data from a liquids rich shale reservoir, by integrating multivariate linear regression analysis, machine learning algorithms along with physical model simulations. The results are novel and offer a method to validate either approach to understand cause and effect relationships. This approach may be classified as a new hybrid modeling approach that may potentially be used to optimize stimulation techniques in liquids rich shale reservoirs.