Graduate Seminar - Integration of Shale Gas Production Data and Microseismic for Fracture and Reservoir Properties using a Fast Marching Method

Friday, March 29, 2013 from 03:30 PM to 04:30 PM

Please join us at 3:30 p.m. Friday, March 29, 2013 in Keplinger Hall, room #M2, for the McDougall School of Petroleum Engineering's Graduate Seminar.  Prof. Michael J. King, John & Debbie Bethancourt Endowed Professor, Texas A & M Energy Institute, Texas A & M University, will lecture on "Integration of Shale Gas Production Data and Mircoseismic for Fracture and Reservoir Properties using a Fast Marching Method." (Co-authors: Jiang Xie, Changdong Yang, Neha Gupta, and Akhil Datta-Gupta, Texas A&M University)

ABSTRACT

We present a novel approach to calculate drainage volume and well performance in shale gas reservoirs using a Fast Marching Method (FMM) combined with a geometric pressure approximation. Our approach can fully account for complex fracture network geometries associated with multistage hydraulic fractures and their impact on the well pressure and rates.

The major advantages of our proposed approach are its simplicity, intuitive appeal and computational efficiency. For example, we can compute and visualize the time evolution of the well drainage volume for multimillion cell geologic models in seconds without resorting to reservoir simulation. A geometric approximation of the drainage volume is then used to compute the well rates and the reservoir pressure.

The speed and versatility of our proposed approach makes it ideally suited for parameter estimation via inverse modeling of shale gas performance data. We utilize experimental design to perform the sensitivity analysis to identify the most significant parameters and a genetic algorithm to calibrate the relevant fracture and matrix parameters in shale gas reservoirs by history matching of production data. In addition to the production data, microseismic information is utilized to help us constrain the fracture extent and orientation and to estimate the stimulated reservoir volume (SRV). The proposed approach is applied to a fractured shale gas well. The results clearly show reduced uncertainty in the estimated fracture parameters and SRV, leading to improved forecasting and reserve estimation.

This presentation is an expanded version of SPE161357.

Refreshments will be served at 3:15PM.

Contact:
Dr. Rami Younis
rami-younis@utulsa.edu
(918) 631-2426