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In recent years, the development of distributed acoustic sensing (DAS) technology has enabled direct monitoring of subsurface strain during hydraulic fracturing (HF) operations. Most low-frequency (<1 Hz) DAS (LFDAS) signals exhibit strain or strain-rate patterns that are characteristic of propagating tensile hydraulic fractures. However, mixed-mode fault reactivation, consisting of shear slip (mode II) with a tensile opening (mode I), can occur in cases where propagating hydraulic fractures intersect preexisting natural fractures or faults. We have developed anomalous LFDAS signals that were observed during an HF operation, with characteristics that differ from typical tensile hydraulic fractures. Anomalous characteristics include the onset of an asymmetric compression-extension doublet after pumping was terminated. The location of the signals, coupled with the image log and seismic data, suggests that mixed-mode reactivation occurred on a preexisting fault. We use a simplified numerical model based on the displacement discontinuity method (DDM) to simulate the anomalous LFDAS signal. Results find that the first-order characteristics of the anomalous signal can be approximated as an initial tensile fault opening (mode I) followed by a shear slip (mode II) on a fault. We determine the approach of using DDM to investigate mixed-mode fault reactivation during HF operations.
In recent years, the development of distributed acoustic sensing (DAS) technology has enabled direct monitoring of subsurface strain during hydraulic fracturing (HF) operations. Most low-frequency (<1 Hz) DAS (LFDAS) signals exhibit strain or strain-rate patterns that are characteristic of propagating tensile hydraulic fractures. However, mixed-mode fault reactivation, consisting of shear slip (mode II) with a tensile opening (mode I), can occur in cases where propagating hydraulic fractures intersect preexisting natural fractures or faults. We have developed anomalous LFDAS signals that were observed during an HF operation, with characteristics that differ from typical tensile hydraulic fractures. Anomalous characteristics include the onset of an asymmetric compression-extension doublet after pumping was terminated. The location of the signals, coupled with the image log and seismic data, suggests that mixed-mode reactivation occurred on a preexisting fault. We use a simplified numerical model based on the displacement discontinuity method (DDM) to simulate the anomalous LFDAS signal. Results find that the first-order characteristics of the anomalous signal can be approximated as an initial tensile fault opening (mode I) followed by a shear slip (mode II) on a fault. We determine the approach of using DDM to investigate mixed-mode fault reactivation during HF operations.
Low-frequency distributed acoustic sensing (LF-DAS) has recently received much attention for its ability to monitor fracture propagation at offset wells. Hydraulic Fracture Test Site-2 (HFTS-2), a DOE- sponsored field-based research experiment, has acquired LF-DAS datasets during the stimulation of many horizontal wells in the Wolfcamp Formation of the Permian Basin. Over 100 stimulated stages with different completion designs in four horizontal wells were monitored by two horizontal offset wells and one vertical pilot hole with permanent fibers. The parent well depletion affected all four horizontal wells in almost half of their lateral section. Several studies have been done on the acquired comprehensive dataset. In this study, we apply our novel Green's function-based inversion algorithm to calculate the fracture width of each stage and investigate the impact of parent well depletion on fracture geometry. The Green's function-based inversion algorithm (Liu et al., 2021) has been successfully applied to a few stages of field case studies. The Green function matrix was built based on linear elasticity theory (Three- Dimensional Displacement Discontinuity Method) to relate the fracture openings with strain responses measured along the length of the fiber. This novel algorithm only relies on measured strain to solve fracture geometry at the monitoring well. Therefore, it is independent of the physics of the fracture propagation process and can be used to validate hydraulic fracture modeling results. Using our inversion algorithm, we can efficiently and quantitatively interpret LF-DAS data to provide information on fracture geometry and completion efficiency. We analyze more than 100 stages of the LF-DAS measurements obtained at the fiber wells B3H and B4H during B1H, B2H, and B4H stimulations. We apply our inversion algorithm for four datasets covering the stimulation of the above mentioned three wells. The fracture growth at stages in the parent well depleted zone is biased more towards upper formations and in the east direction. The fracture width at the stages in the parent well depletion zone is reduced compared to fracture widths at stages in the non- depleted zone irrespective of the monitoring well location relative to the treatment wells. This difference in fracture widths will affect the proppant distribution to a great extent, thereby, affecting the effectiveness of the stimulation. We also illustrate the application of the inversion algorithm for stages that have both conventional fracture hits with "heart-shaped" signals as well as fracture reopening signals. Our inversion algorithm gives a reasonable estimate of the fracture width that aligns with the qualitative analysis of microseismic datasets and statistic summary of fracture hit numbers of HFTS-2. We believe that this study provides us with insights into the fracture geometry due to parent well depletion effects quantitatively.
Summary Low-frequency distributed acoustic sensing (LF-DAS) has recently received much attention for its ability to monitor fracture propagation at offset wells. Hydraulic Fracture Test Site-2 (HFTS-2), a Department of Energy–sponsored field-based research experiment, has acquired LF-DAS data sets during the stimulation of many horizontal wells in the Wolfcamp Formation of the Permian Basin. Over 100 stimulated stages with different completion designs in four horizontal wells were monitored by two horizontal offset wells and one vertical pilot hole with permanent fibers. The parent well depletion affected all four horizontal wells in almost half of their lateral section. Several studies have been performed on the acquired comprehensive data set. In this study, we apply our novel Green’s function-based inversion algorithm to calculate the fracture width of each stage and investigate the impact of parent well depletion on fracture geometry. The Green’s function-based inversion algorithm has been successfully applied to a few stages of field case studies. The Green’s function matrix was built based on linear elasticity theory (3D displacement discontinuity method) to relate the fracture openings with strain responses measured along the length of the fiber. This novel algorithm only relies on measured strain to solve fracture geometry at the monitoring well. Therefore, it is independent of the physics of the fracture propagation process and can be used to validate hydraulic fracture modeling results. Using our inversion algorithm, we can efficiently and quantitatively interpret LF-DAS data to provide information on fracture geometry and completion efficiency. We analyze more than 100 stages of the LF-DAS measurements obtained at the fiber wells B3H and B4H during B1H, B2H, and B4H stimulations. We apply our inversion algorithm for four data sets covering the stimulation of the abovementioned three wells. The fracture growth at stages in the parent well’s depleted zone is biased more toward upper formation and in the easterly direction. The fracture width at the stages in the parent well’s depletion zone is reduced compared to fracture widths at stages in the nondepleted zone irrespective of the monitoring well location relative to the treatment wells. This difference in fracture widths will affect the proppant distribution to a great extent, thereby affecting the effectiveness of the stimulation. We also illustrate the application of the inversion algorithm for stages that have both conventional fracture hits with “heart-shaped” signals as well as fracture reopening signals. Our inversion algorithm gives a reasonable estimate of the fracture width that aligns with the qualitative analysis of microseismic data sets and statistic summary of fracture hit numbers of HFTS-2. We believe that this quantitative study provides us with insights into the fracture geometry due to parent well depletion effects.
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