DISTINGUISHED SEMINAR SERIES: Kan Wu, Texas A&M University

Hydraulic Fracture Geometry Characterization in Unconventional Reservoirs: Numerical Modeling and Data Interpretation

Multi-stage hydraulic fracturing with horizontal drilling is a key technique to economically unlock hydrocarbon in unconventional reservoirs. Despite their successful applications, accurate characterization of the complex hydraulic fracture geometry that is created in unconventional reservoirs remains challenging due to the complexities of stress shadow effects and hydraulic fractures interacting with the rock fabric, as well as heterogeneities of rock properties and stress state. The hydraulic fracture geometry significantly influences the stimulated well performance, and a better understanding of its geometry is crucial for the optimization of completion designs. Hence, a better understanding of the multiple hydraulic fracturing propagation process and the generated fracture geometry is of critical importance in unconventional reservoir development. Numerical modeling is one of the primary methods that are frequently applied to understand and characterize fracture geometries. Low Frequency Distributed Acoustic Sensing (LF DAS) is a promising fracture diagnostic technique to measure strain change induced by fracture opening and monitor fracture propagation. In this presentation, I will overview our numerical modeling work and introduce a new algorithm for data interpretation of LF DAS. In our group, we have developed a high-fidelity model to simulate 3D hydraulic fracture propagation and test different coupling algorithms for accurately solving the multi-physics problem. Besides, to efficiently simulate field cases, a pseudo-3D simulator has been developed to model multiple fracture propagation considering stress shadow effects, hydraulic fractures interacting with natural fractures, stress heterogeneities, and limit entry. Following that, leading-edge work will be introduced to quantitatively interpret measured data of LF DAS. A novel Green’s function-based inversion model will be discussed to calculate fracture width and height from measured strain data. The presentation will be concluded with a field case study in an unconventional oil reservoir to illustrate the workflow of quantitative data interpretation.

Bio: Dr. Kan Wu is an associate Professor and Class of ’75 DVG Career Development Professor in Harold Vance department of petroleum engineering at Texas A&M University. Her research interests include modeling and optimization of hydraulic fracturing, multi-scale and multi-physics modeling, and data interpretation and geomechanics modeling of distributed fiber optic strain measurements. Wu has authored or co-authored more than 90 technical papers, which have been cited about 3490 times and more than 3160 times over the past five years (Source: Google Scholar). Kan holds a Ph.D. degree in petroleum engineering from The University of Texas at Austin. She is an associate editor for the SPE Reservoir Evaluation & Engineering Journal and the Journal of Petroleum Science and Engineering.