Application of Hybrid Physics-Based and Data-Driven Fracture Propagation Modeling for Characterizing Hydraulic Fracture Geometry in Unconventional Reservoirs
Authors: Aldhayee Khaled , Wu Kan 
Published: October 2023

Multistage hydraulic fracturing is essential to unlock the potential of unconventional reservoirs and produce them economically. Data acquisition technologies, such as Distributed Acoustic Sensing (DAS), have been revolutionized in the last decade for real-time downhole monitoring of hydraulic fracturing jobs providing invaluable information related to stimulation and completion efficiency. The primary objective of this work is to utilize an integrated artificial intelligence (AI) assisted workflow that incorporates the field data acquired from different sources into physics-based fracture propagation model, which can automatically calibrate the uncertain input parameters, quantify the associated uncertainties, and ultimately provide more reliable fracture geometries.

The AI-assisted workflow incorporates the obtained data from different sources to a hydraulic fracturing simulator. The framework starts with identifying the uncertain parameters that have significant impact on the target objectives. These objectives consist of surface treating pressures, in-well fluid distributions across the clusters obtained from the installed high-frequency DAS (HF-DAS) in the treatment well, and cross-well fracture hits that are characterized by the installed low-frequency DAS (LF-DAS) in the monitoring well. The target objectives are simultaneously and automatically matched through the calibration of hydraulic fracturing simulator by developing highly efficient and accurate machine learning (proxy) models, which are integrated with a multi-proxy-based Markov Chain Monte Carlo (MCMC) algorithm to generate the history matching solutions and posterior distributions of the uncertain parameters that quantify the uncertainty of the resultant fracture geometry and assess the stimulation/completion efficiency in the treatment well.

The established workflow is applied to a treatment well that consists of 19 fracturing stages, which demonstrates its capability in handling a problem that exhibits high-dimensionality and multiple objectives by automatically matching all objectives successfully for all the stages. The developed proxy models have high predictability and generalizability and are used in conjunction with MCMC to generate the history matching solutions. The generated solutions aid in diagnosing the stages that have low cluster efficiency and poor fluid distribution by investigating the posterior of the related completion parameter such as perforation diameter if the stage suffers from perforations erosion. The calibrated parameters and fracture geometries can then be used to optimize the well spacing, completion design, pumping schedule, fracturing fluids and proppants to achieve desirable results.

This work emphasizes on the importance of information obtained from fracture diagnostic techniques to be incorporated into the physics-based models by presenting a systematic hybrid approach of data and physics that leads to a better understanding of fracture propagation, and subsequently maximizing well production.