Engineering topics

Zhang, Zhishuai

Publications and source records attributed to Zhang, Zhishuai.

5 records

DOE OSTI2022

Interpretation of HFTS 2 Microseismic Data Using Bedding-Plane-Slip Mechanism

Summary The objective of this study is to understand how microseismic events are generated during hydraulic fracturing, as well as the role of geomechanical conditions (i.e., stress and mechanical stratigraphy) in this process. In the industry, microseismic event clouds have been generally used as an “outer boundary” of the “stimulated reservoir volume” (SRV). However, by comparing with other surveillance data (low-frequency distributed acoustic sensing, or LF-DAS, strain) in the Hydraulic Fracturing Test Site (HFTS) 2 experiment, we show that this assumption is fundamentally flawed. The HFTS 2 data have three unique observations that have not been commonly observed in other datasets: (1) Due to the influence of offset pad depletion, microseismic data show that hydraulic fractures from the child well can propagate over 3,000 ft into the depleted low-stress zone. (2) By comparing microseismic and horizontal fiber LF-DAS strain data, we observe that the microseismic event cloud does not necessarily reflect the created hydraulic fracture volume. Particularly, the extent of microseismic event clouds near heel stages is much shorter than what is shown with LF-DAS strain data. (3) Microseismic event magnitudes are larger in the depleted regions. Through geomechanical analysis, we demonstrate that the “bedding-plane-slip” model is likely the mechanism for microseismic generation during hydraulic fracturing. This model successfully explains the above field observations from the HFTS 2 experiment. We also provide a quantitative relationship connecting the microseismic event magnitude with fracture width increment and layer mechanical property contrast.

Engineering↗

DOE OSTI2021

Hydraulic fracture characterization by integrating multidisciplinary data from the Hydraulic Fracturing Test Site 2 (HFTS-2)

Various technologies have traditionally been used to monitor and describe hydraulic fractures from different perspectives. This work demonstrates the value of data integration for hydraulic fracture characterization when multiple data resources are available. The Hydraulic Fracturing Test Site 2 (HFTS 2) is a hydraulic fracturing research project in the Delaware Basin with multiple surveillance techniques including fiber optics sensing, microseismic, pressure/temperature gauges, etc. We integrated the multidisciplinary data from the HFTS-2 to characterize hydraulic fractures. The integrated data revealed interesting fracture propagation features including layering, vertical propagation affected by pore pressure gradient, and different microseismic activities due to difference in-situ conditions. Furthermore, these findings can be insightful for understanding hydraulic fracture propagation. The comparison among multiple surveillance data also helps us to evaluate the roles of various surveillance technologies and provides us experience to make informative decisions depending on different monitoring objectives.

58 GEOSCIENCES↗

DOE OSTI2021

Mechanism of Microseismic Generation During Hydraulic Fracturing - With Evidence From HFTS 2 Observations

The objective of this study is to understand how microseismic events are generated during hydraulic fracturing, as well as the role of geomechanical conditions (i.e., stress and mechanical stratigraphy) in this process. In the industry, microseismic event clouds have been generally used as an “outer-boundary” of the “stimulated reservoir volume” (SRV). However, by comparing with other surveillance data (Low frequency Distributed Acoustic Sensing, or LF-DAS strain) in the Hydraulic Fracturing Test Site (HFTS) 2 experiment, we show that this assumption is fundamentally flawed. The HFTS 2 data has three unique observations that have not been commonly observed in other datasets: 1. Due to influence of offset pad depletion, microseismic data shows that hydraulic fractures from the child well can propagate over 3000 feet into the depleted low stress zone. 2. By comparing microseismic and horizontal fiber LF-DAS strain data, we observe that microseismic event cloud does not necessarily reflect the created hydraulic fracture volume. Particularly, the extent of microseismic event clouds near heel stages are much shorter than what is shown with LF-DAS strain data. 3. Microseismic event magnitudes are larger in the depleted regions. Through geomechanical analysis, we demonstrate that the “bedding-plane-slip” model is likely the mechanism for microseismic generation during hydraulic fracturing. Furthermore, this model successfully explains the above field observations from HFTS 2 experiment. We also provide a quantitative relationship connecting the microseismic event magnitude with fracture width increment and layer mechanical property contrast.

58 GEOSCIENCES↗

DOE OSTI2017

Simultaneous inversion of multiple microseismic data for event locations and velocity model with Bayesian inference

We have applied Bayesian inference for simultaneous inversion of multiple microseismic data to obtain event locations along with the subsurface velocity model. The traditional method of using a predetermined velocity model for event location may be subject to large uncertainties, particularly if the prior velocity model is poor. Our study indicated that microseismic data can help to construct the velocity model, which is usually a major source of uncertainty in microseismic event locations. The simultaneous inversion eliminates the requirement for an accurate predetermined velocity model in microseismic event location estimation.Here, we estimate the posterior probability density of the velocity model and microseismic event locations with the maximum a posteriori estimation, and the posterior covariance approximation under the Gaussian assumption. This provides an efficient and effective way to quantify the uncertainty of the microseismic location estimation and capture the correlation between the velocity model and microseismic event locations. We have developed successful applications on both synthetic examples and real data from the Newberry enhanced geothermal system. Comparisons with location results based on a traditional predetermined velocity model method demonstrated that we can construct a reliable effective velocity model using only microseismic data and determine microseismic event locations without prior knowledge of the velocity model.

58 GEOSCIENCES↗

DOE OSTI2015

Summer Intern Report: Multiple-Event Seismic Location Using Bayesian Inversion: Case Study Using Newberry Enhanced Geothermal System Data

The central idea of the method used in the work is representing all the information we have using probability densities and then using inversion method to infer the A posteriori probability density of the model parameters we would like to know from all these information we have. This information includes forward model (the assumption we made on the problem and the theory we use for arrival time calculation), observations (arrival time), and A prior information on the model parameters (all other information we have on model parameters before the inversion).

58 GEOSCIENCES↗