A Data-Driven Reduced-Order Model for Estimating the Stimulated Reservoir Volume (SRV)

Abstract

The main goal of hydraulic fracturing stimulation in unconventional and tight reservoirs is to maximize hydrocarbon production by creating an efficient stimulated reservoir volume (SRV) around the horizontal wells. To zreach this goal, a physics-based model is typically used to design and optimize the hydraulic fracturing process before executing the job. However, two critical issues make this approach insufficient for achieving the mentioned goal. First, the physics-based models are based on several simplified assumptions and do not correctly represent the physics of unconventional reservoirs; hence, they often fail to match the observed SRVs in the field. Second, the success of the executed stimulation job is evaluated after it is completed in the field, leaving no room to modify some parameters such as proppant concentration in the middle of the job. To this end, this paper proposes data-driven and global sensitivity approaches to address these two issues. It introduces a novel workflow for estimating SRV in near real-time using some hydraulic fracturing parameters that can be inferred before or during the stimulation process. It also utilizes a robust global sensitivity framework known as the Sobol Method to rank the input parameters and create a reduced-order (mathematically simple) model for near real-time estimation of SRV (referred to as DSRV). The proposed framework in this paper has two main advantages and novelties. First, it is based on a pure data-based approach, with no simplified assumptions due to the use of a simulator for generating the training and test dataset, which is often the case in similar studies. Second, it treats SRV generation as a rock mechanics problem (rather than a reservoir engineering problem with fixed fracture lengths), accounting for changes in hydraulic fracture topology and SRV changes with time. A dataset from the Marcellus Shale Energy and Environment Laboratory (MSEEL) project is used. The model’s input parameters include stimulation variables of 58 stages of two wells. These parameters are stage number, step, pump rate and duration, proppant concentration and mass, and treating pressure. The model output consists of the corresponding microseismic (MS) cloud size at each step (i.e., time window) during the job. Based on the model, guidelines are provided to help operators design more efficient fracturing jobs for maximum recovery and to monitor the effectiveness of the hydraulic fracturing process. A few future improvements to this approach are also provided.