Hydraulic-fracture geometry characterization using low-frequency DAS signal

2017 ◽  
Vol 36 (12) ◽  
pp. 975-980 ◽  
Author(s):  
Ge Jin ◽  
Baishali Roy
Keyword(s):  
Hydraulic Fracture ◽  
Low Frequency ◽  
Fracture Geometry

Fracture Hits and Hydraulic-Fracture Geometry Characterization Using Low-Frequency Distributed Acoustic Sensing Strain Data

2021 ◽  
Vol 73 (07) ◽  
pp. 39-42
Author(s):  
Kan Wu ◽  
Yongzan Liu ◽  
Ge Jin ◽  
George Moridis
Keyword(s):  
Strain Rate ◽  
Green’S Function ◽  
Green's Function ◽  
Hydraulic Fracture ◽  
Laser Energy ◽  
Low Frequency ◽  
Forward Model ◽  
Hydraulic Fractures ◽  
Fracture Geometry

The propagation process and geometry of hydraulic fractures depend on complex interactions among the induced fractures and the pre-existing rock fabric, the heterogeneous rock properties, and the stress state. Accurate characterization of the resulting complex hydraulic-fracture geometry remains challenging. Fiber-optic-based distributed acoustic sensing (DAS) measurements have been used for monitoring hydraulic fracturing in adjacent treatment wells. DAS requires an optical fiber attached to the wellbore to transmit the laser energy into the reservoir. Each section of the fiber scatters a small portion of the laser energy back to a surface sensing unit, which uses interferometry techniques to determine strain changes along with the fiber. DAS data in offset wells fall in the low-frequency bands, which has been proven to be a powerful attribute for the characterization of the geometry of hydraulic fractures. Numerous recently published field examples demonstrate the potential of low-frequency DAS (LF-DAS) data for the detailed characterization of the hydraulic fracture geometry. Understanding the fracture-induced rock deformation associated with LF-DAS signals would be beneficial for the better interpretation of real-time data. However, interpretation of LF-DAS measurement is challenging due to the complexity of the subsurface conditions, in addition to potential unanticipated completion issues such as perforation failure, stage isolation failure, etc. All current research efforts focus on the qualitative interpretation of field data.In this study, we quantified the hydraulic fracture propagation process and described the fracture geometry by developing a geomechanical forward model and a Green’s function-based inversion model for the LF-DAS data interpretation, substantially enhancing the value of the LF-DAS data in the process. The work has a significant transformative potential, involving a tool package with developed forward and inversion models that can provide crucial insights for the optimization of hydraulic-fracturing treatments and reservoir development. Methodology The tool package can be used directly in the field to interpret LF-DAS data and monitor hydraulic fracture propagation. Raw data from the field measurement can be automatically processed. The geomechanics forward model we developed can quantify and analyze the strain-rate response from the LF-DAS measurements based on the 3D displacement discontinuity method. Fracture hits are detected by calculating three 1D features along the channel (location) axis, i.e., the maximum strain rate, the summation of strain rates, and the summation of strain-rate amplitudes. Channels with fracture hits usually exhibit significant peak values of these three features. We proposed general guide-lines for fracture-hit detection based on the quantitative analysis of strain/strain-rate responses during the multistage fracturing treatment. The details of the forward model can be found in Liu et al. (SPE 202482, 204457, AMRA-2020-1426). Additionally, we developed a novel Green’s function-based inversion model to qualify fracture width and height based on the determined fracture hits. The strain field that is estimated from the integration of the strain rates measured by the LF-DAS data along the offset monitoring well is related to the fracture widths through a geomechanics Green’s function. The resulting linear system of equations is solved by the least-square method. Details can be found in Liu et al. (SPE 204158, 205379, 204225).

Quantitative Hydraulic-Fracture Geometry Characterization with LF-DAS Strain Data: Numerical Analysis and Field Applications

2021 ◽  
Author(s):  
Yongzan Liu ◽  
Ge Jin ◽  
Kan Wu ◽  
George Moridis
Keyword(s):  
Hydraulic Fracture ◽  
True Strain ◽  
Low Frequency ◽  
Forward Model ◽  
Inversion Algorithm ◽  
Fracture Geometry ◽  
Fracture Width ◽  
True Value ◽  
Strain Data ◽  
Fracture Height

Abstract Low-frequency distributed acoustic sensing (LF-DAS) has been used for hydraulic fracture monitoring and characterization. Large amounts of DAS data have been acquired across different formations. The low-frequency components of DAS data are highly sensitive to mechanical strain changes. Forward geomechanical modeling has been the focus of current research efforts to better understand the LF-DAS signals. Moreover, LF-DAS provides the opportunity to quantify fracture geometry. Recently, Liu et al. (2020a;2020b) proposed an inversion algorithm to estimate hydraulic fracture width using LF-DAS data measured during multifracture propagation. The LF-DAS strain data is linked to the fracture widths through a forward model developed based on the Displacement Discontinuity Method (DDM). In this study, we firstly investigated the impacts of fracture height on the inversion results through a numerical case with a four-cluster completion design. Then we discussed how to estimate the fracture height based on the inversion results. Finally, we applied the inversion algorithm to two field examples. The inverted widths are not sensitive to the fracture height. In the synthetic case, the maximum relative error is less than 10% even when the fracture height is two times of the true value. After obtaining the fracture width, the fracture height can be estimated by matching the true strain data under various heights with a strong smooth weight. The error between the calculated strain and true strain decreases as the height is getting close to the true value. In the two field examples, the temporal evolutions of both width summation of all fractures and the width of each fracture show consistent behaviors with the field LF-DAS measurements. The calculated strain data from the forward model matches well with the field LF-DAS strain data. The results demonstrate the robustness and accuracy of the proposed inversion algorithm.

Data Analytics to Help Improve Hydraulic Fracture Geometry Prediction Using Support Vector Regression: Case Study

2020 ◽  
Author(s):  
Avinash Wesley ◽  
Bharat Mantha ◽  
Ajay Rajeev ◽  
Aimee Taylor ◽  
Mohit Dholi ◽  
...  
Keyword(s):  
Support Vector Regression ◽  
Hydraulic Fracture ◽  
Data Analytics ◽  
Support Vector ◽  
Fracture Geometry

4D Geomechnical Model Creation for Estimation of Field Development Effect on Hydraulic Fracture Geometry (Russian)

2016 ◽  
Author(s):  
Valeriy Pavlov ◽  
Evgeny Korelskiy ◽  
Kreso Kurt Butula ◽  
Artem Kluybin ◽  
Danil Maximov ◽  
...  
Keyword(s):  
Hydraulic Fracture ◽  
Fracture Geometry ◽  
Field Development

Low Frequency DAS Data Study with Integrated Data Analysis for Monitoring Hydraulic Fracture

2020 ◽  
Author(s):  
M. Ichikawa ◽  
M. Kato ◽  
S. Uchida ◽  
K. Tamura ◽  
A. Kato ◽  
...  
Keyword(s):  
Data Analysis ◽  
Hydraulic Fracture ◽  
Low Frequency ◽  
Integrated Data Analysis

An Integrated View of Hydraulic Induced Fracture Geometry in Hydraulic Fracture Test Site 2

2021 ◽  
Author(s):  
Gustavo A. Ugueto ◽  
Magdalena Wojtaszek ◽  
Paul T. Huckabee ◽  
Alexei A. Savitski ◽  
Artur Guzik ◽  
...  
Keyword(s):  
Hydraulic Fracture ◽  
Test Site ◽  
Fracture Test ◽  
Fracture Geometry
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