Correlation and Interpretation of Microseismic Responses using Pressure Measurements in Offset Observation Wells

2015 ◽  
Author(s):  
Robert Downie ◽  
Joel Le Calvez ◽  
Barry Dean ◽  
Jeff Rutledge
Keyword(s):  
Surface Pressure ◽  
Source Parameters ◽  
Reservoir Pressure ◽  
Pressure Measurements ◽  
Observation Well ◽  
Fracture Pressure ◽  
Microseismic Data ◽  
Pressure Depletion ◽  
Microseismic Events ◽  
Observation Wells

Abstract Interpretation of the microseismic data acquired during hydraulic fracture treatments is based on a variety of techniques that make use of the locations, times, and source parameters of the detected events, in conjunction with the stimulation treatment data. It is sometimes possible to observe trends or changes in the microseismic data that correspond to the surface pressure measurements; however this aspect of interpretation becomes problematic due the variability of fluid friction, slurry density, perforation restrictions, and other near-wellbore pressures when computing bottom hole fracturing pressure. An interpretation technique is proposed that uses pressure measurements in observation wells that are offset to the treatment well during microseismic interpretations. The observation well can be any well with open perforations in close proximity to the treatment well. The observation well pressures are not affected by the many complicating factors that are encountered when estimating pressure in the fracture from the surface pressure measured in the treatment well. Example data from field observations are used to demonstrate that the detection of microseismic events near an observation well and corresponding detection of fluid pressure from the fracture in the observation well validates the calculated event locations. The relationship between fracture pressure, the state of stress, and microseismic responses is discussed using Mohr-Coulomb failure criteria. Observation-well pressures and microseismic events are also used to identify instances where reservoir pressure depletion near the observation well affects surface operations at the treatment well. The results of the study show that reliable measurements of fracture pressure for use in microseismic interpretations can be obtained from offset observation wells, and where reservoir pressure depletion causes deviations from expected fracture behavior. The results also show that microseismic responses are directly related to fracture pressure, and not simply the presence of fracturing fluid itself, leading to an improved understanding of the conditions under which microseismic events occur.

Surface pressure measurements in shock wave/boundary-layer interactions

1997 ◽  
Vol 11 (2) ◽  
pp. 164-172
Author(s):  
Yeol Lee ◽  
Sanjay Garg ◽  
Gary S. Settles
Keyword(s):  
Boundary Layer ◽  
Shock Wave ◽  
Surface Pressure ◽  
Pressure Measurements ◽  
Wave Boundary Layer ◽  
Wave Boundary

Aerodynamic Noise Characterization of a Full-Scale Wind Turbine through High-Frequency Surface Pressure Measurements

2015 ◽  
Vol 14 (5-6) ◽  
pp. 729-766 ◽  
Author(s):  
Franck Bertagnolio ◽  
Helge Aa. Madsen ◽  
Christian Bak ◽  
Niels Troldborg ◽  
Andreas Fischer
Keyword(s):  
Wind Turbine ◽  
Surface Pressure ◽  
High Frequency ◽  
Full Scale ◽  
Aerodynamic Noise ◽  
Pressure Measurements ◽  
Noise Characterization

scholarly journals Surface Pressure Measurements on Supercritical Airfoil Employing Pressure -Sensitive Paint

2012 ◽  
Vol 31 ◽  
pp. 1160-1167
Author(s):  
Qiang Zhou ◽  
Jian Xiong ◽  
Liusheng Chen ◽  
Husheng Ma ◽  
Yang Tao
Keyword(s):  
Surface Pressure ◽  
Pressure Measurements ◽  
Pressure Sensitive Paint ◽  
Supercritical Airfoil ◽  
Pressure Sensitive

Low-Cost Measurement of Hydraulic Fracture Dimensions Using Pressure Data in Treatment and Observation Wells – A Workflow for Every Well

2021 ◽  
Author(s):  
A. Kirby Nicholson ◽  
Robert C. Bachman ◽  
R. Yvonne Scherz ◽  
Robert V. Hawkes
Keyword(s):  
Hydraulic Fracturing ◽  
Real Time ◽  
Hydraulic Fracture ◽  
Full Scale ◽  
Pressure Data ◽  
Observation Well ◽  
High Quality ◽  
Fracture Length ◽  
Geometry Model ◽  
Observation Wells

Abstract Pressure and stage volume are the least expensive and most readily available data for diagnostic analysis of hydraulic fracturing operations. Case history data from the Midland Basin is used to demonstrate how high-quality, time-synchronized pressure measurements at a treatment and an offsetting shut-in producing well can provide the necessary input to calculate fracture geometries at both wells and estimate perforation cluster efficiency at the treatment well. No special wellbore monitoring equipment is required. In summary, the methods outlined in this paper quantifies fracture geometries as compared to the more general observations of Daneshy (2020) and Haustveit et al. (2020). Pressures collected in Diagnostic Fracture Injection Tests (DFITs), select toe-stage full-scale fracture treatments, and offset observation wells are used to demonstrate a simple workflow. The pressure data combined with Volume to First Response (Vfr) at the observation well is used to create a geometry model of fracture length, width, and height estimates at the treatment well as illustrated in Figure 1. The producing fracture length of the observation well is also determined. Pressure Transient Analysis (PTA) techniques, a Perkins-Kern-Nordgren (PKN) fracture propagation model and offset well Fracture Driven Interaction (FDI) pressures are used to quantify hydraulic fracture dimensions. The PTA-derived Farfield Fracture Extension Pressure, FFEP, concept was introduced in Nicholson et al. (2019) and is summarized in Appendix B of this paper. FFEP replaces Instantaneous Shut-In Pressure, ISIP, for use in net pressure calculations. FFEP is determined and utilized in both DFITs and full-scale fracture inter-stage fall-off data. The use of the Primary Pressure Derivative (PPD) to accurately identify FFEP simplifies and speeds up the analysis, allowing for real time treatment decisions. This new technique is called Rapid-PTA. Additionally, the plotted shape and gradient of the observation-well pressure response can identify whether FDI's are hydraulic or poroelastic before a fracture stage is completed and may be used to change stage volume on the fly. Figure 1Fracture Geometry Model with FDI Pressure Matching Case studies are presented showing the full workflow required to generate the fracture geometry model. The component inputs for the model are presented including a toe-stage DFIT, inter-stage pressure fall-off, and the FDI pressure build-up. We discuss how to optimize these hydraulic fractures in hindsight (look-back) and what might have been done in real time during the completion operations given this workflow and field-ready advanced data-handling capability. Hydraulic fracturing operations can be optimized in real time using new Rapid-PTA techniques for high quality pressure data collected on treating and observation wells. This process opens the door for more advanced geometry modeling and for rapid design changes to save costs and improve well productivity and ultimate recovery.

Time-Averaged Heat Transfer and Pressure Measurements and Comparison With Prediction for a Two-Stage Turbine

1994 ◽  
Vol 116 (1) ◽  
pp. 14-22 ◽  
Author(s):  
M. G. Dunn ◽  
J. Kim ◽  
K. C. Civinskas ◽  
R. J. Boyle
Keyword(s):  
Surface Pressure ◽  
Space Shuttle ◽  
Three Dimensional ◽  
Stanton Number ◽  
Navier Stokes ◽  
Pressure Measurements ◽  
Two Stage ◽  
Pressure Transducers ◽  
Pressure Distributions ◽  
Main Engine

Time-averaged Stanton number and surface-pressure distributions are reported for the first-stage vane row and the first-stage blade row of the Rocketdyne Space Shuttle Main Engine two-stage fuel-side turbine. These measurements were made at 10, 50, and 90 percent span on both the pressure and suction surfaces of the component. Stanton-number distributions are also reported for the second-stage vane at 50 percent span. A shock tube is used as a short-duration source of heated and pressurized air to which the turbine is subjected. Platinum thin-film gages are used to obtain the heat-flux measurements and miniature silicone-diaphragm pressure transducers are used to obtain the surface pressure measurements. The first-stage vane Stanton number distributions are compared with predictions obtained using a quasi-three dimensional Navier–Stokes solution and a version of STAN5. This same N–S technique was also used to obtain predictions for the first blade and the second vane.

Arrival-time picking of microseismic events based on MSNet

Geophysics ◽  
2021 ◽  
pp. 1-66
Author(s):  
Guanqun Sheng ◽  
Shuangyu Yang ◽  
Xiaolong Guo ◽  
Xingong Tang
Keyword(s):  
Arrival Time ◽  
Spatial Information ◽  
Synthetic Data ◽  
Microseismic Monitoring ◽  
Monitoring Data ◽  
Convolutional Network ◽  
Low Snr ◽  
Microseismic Data ◽  
Microseismic Events ◽  
Arrival Time Picking

Arrival-time picking of microseismic events is a critical procedure in microseismic data processing. However, as field monitoring data contain many microseismic events with low signal-to-noise ratios (SNRs), traditional arrival-time picking methods based on the instantaneous characteristics of seismic signals cannot meet the picking accuracy and efficiency requirements of microseismic monitoring owing to the large volume of monitoring data. Conversely, methods based on deep neural networks can significantly improve arrival-time picking accuracy and efficiency in low-SNR environments. Therefore, we propose a deep convolutional network that combines the U-net and DenseNet approaches to pick arrival times automatically. This novel network, called MSNet not only retains the spatial information of any input signal or profile based on the U-net, but also extracts and integrates more essential features of events and non-events through dense blocks, thereby further improving the picking accuracy and efficiency. An effective workflow is developed to verify the superiority of the proposed method. First, we describe the structure of MSNet and the workflow of the proposed picking method. Then, datasets are constructed using variable microseismic traces from field microseismic monitoring records and from the finite-difference forward modeling of microseismic data to train the network. Subsequently, hyperparameter tuning is conducted to optimize the MSNet. Finally, we test the MSNet using modeled signals with different SNRs and field microseismic data from different monitoring areas. By comparing the picking results of the proposed method with the results of U-net and short-term average and long-term average (STA/LTA) methods, the effectiveness of the proposed method is verified. The arrival picking results of synthetic data and microseismic field data show that the proposed network has increased adaptability and can achieve high accuracy for picking the arrival-time of microseismic events.

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