Integration of microseismic data, completion data, and production data to characterize fracture geometry in the Permian Basin

2018 ◽  
Vol 56 ◽  
pp. 62-71 ◽  
Author(s):  
Ross Patterson ◽  
Wei Yu ◽  
Kan Wu
Keyword(s):  
Production Data ◽  
Permian Basin ◽  
Fracture Geometry ◽  
Microseismic Data ◽  
Data Completion

An Efficient Hydraulic Fracture Geometry Calibration Workflow Using Microseismic Data, Geomechanics, DFN Models, and History Matching

2021 ◽  
Author(s):  
Joseph Alexander Leines-Artieda ◽  
Chuxi Liu ◽  
Hongzhi Yang ◽  
Jianfa Wu ◽  
Cheng Chang ◽  
...  
Keyword(s):  
Hydraulic Fracture ◽  
History Matching ◽  
Fracture Propagation ◽  
Natural Fracture ◽  
Unconventional Reservoirs ◽  
Production Data ◽  
Fracture Geometry ◽  
Microseismic Data ◽  
Fracture Models ◽  
Microseismic Events

Abstract Reliable estimates of hydraulic fracture geometry help reduce the uncertainty associated with estimated ultimate recovery (EUR) forecasts and optimize field developing planning in unconventional reservoirs. For these reasons, operators gather information from different sources with the objective to calibrate their hydraulic fracture models. Microseismic data is commonly acquired by operators to estimate hydraulic fracture geometry and to optimize well completion designs. However, relying solely on estimates derived from microseismic information may lead to inaccurate estimates of hydraulic fracture geometry. The objective of this study is to efficiently calibrate hydraulic fracture geometry by using microseismic data, physics-based fracture propagation models, and the embedded discrete fracture model (EDFM). We first obtain preliminary estimates of fracture geometry based on microseismic events’ spatial location and density with respect to the perforation cluster location. We then tune key completion parameters using an in-house fracture propagation model to provide hydraulic fracture geometries that are constrained by the microseismic cloud. In the history matching process, we included the effect of natural fractures, using the microseismic events location as natural fracture initiation points. Finally, we used cutoff coefficients to further reduce hydraulic fracture geometries to match production data. The results of this work showed a fast and flexible method to estimate fracture half-lengths and fracture heights, resulting in a direct indicator of the completion design. Additionally, hydraulic-natural fracture interactions were assessed. We concluded that the inclusion of cutoff coefficients as history matching parameters allows to derive realistic hydraulic and natural fracture models calibrated with microseismic and production data in unconventional reservoirs.

Pressure Depletion Mapping Using Multi-Well DFITs and its Applications in Hydraulic Fracture Characterization and Permeability Estimation: Examples from Montney Formation

2021 ◽  
Author(s):  
Behjat Haghshenas ◽  
Farhad Qanbari
Keyword(s):  
Data Analysis ◽  
Surface Area ◽  
Reservoir Simulation ◽  
Production Data ◽  
Hydraulic Fractures ◽  
Fracture Geometry ◽  
Fracture Conductivity ◽  
Field Case ◽  
Pressure Depletion ◽  
Montney Formation

Abstract Recovery factor for multi-fractured horizontal wells (MFHWs) at development spacing in tight reservoirs is closely related to the effective horizontal and vertical extents of the hydraulic fractures. Direct measurement of pressure depletion away from the existing producers can be used to estimate the extent of the hydraulic fractures. Monitoring wells equipped with downhole gauges, DFITs from multiple new wells close to an existing (parent) well, and calculation of formation pressure from drilling data are among the methods used for pressure depletion mapping. This study focuses on acquisition of pressure depletion data using multi-well diagnostic fracture injection tests (DFITs), analysis of the results using reservoir simulation, and integration of the results with production data analysis of the parent well using rate-transient analysis (RTA) and reservoir simulation. In this method, DFITs are run on all the new wells close to an existing (parent) well and the data is analyzed to estimate reservoir pressure at each DFIT location. A combination of the DFIT results provides a map of pressure depletion around the existing well, while production data analysis of the parent well provides fracture conductivity and surface area and formation permeability. Furthermore, reservoir simulation is tuned such that it can also match the pressure depletion map by adjusting the system permeability and fracture geometry of the parent well. The workflow of this study was applied to two field case from Montney formation in Western Canadian Sedimentary Basin. In Field Case 1, DFIT results from nine new wells were used to map the pressure depletion away from the toe fracture of a parent well (four wells toeing toward the parent well and five wells in the same direction as the parent). RTA and reservoir simulation are used to analyze the production data of the parent well qualitatively and quantitatively. The reservoir model is then used to match the pressure depletion map and the production data of the parent well and the outputs of the model includes hydraulic fracture half-lengths on both sides of the parent well, formation permeability, fracture surface area and fracture conductivity. In Field Case 2, the production data from an existing well and DFIT result from a new well toeing toward the existing wells were incorporated into a reservoir simulation model. The model outputs include system permeability and fracture surface area. It is recommended to try the method for more cases in a specific reservoir area to get a statistical understanding of the system permeability and fracture geometry for different completion designs. This study provides a practical and cost-effective approach for pressure depletion mapping using multi-well DFITs and the analysis of the resulting data using reservoir simulation and RTA. The study also encourages the practitioners to take every opportunity to run DFITs and gather pressure data from as many well as possible with focus on child wells.

Data-Mining Approach Evaluates Production Performance in the Permian Basin

2021 ◽  
Vol 73 (01) ◽  
pp. 46-47
Author(s):  
Chris Carpenter
Keyword(s):  
Data Mining ◽  
Horizontal Wells ◽  
Production Performance ◽  
Asia Pacific ◽  
Production Data ◽  
Permian Basin ◽  
Midland Basin ◽  
Data Mining Approach ◽  
Cumulative Production ◽  
Short And Long Term

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper URTeC 198192, “Production Performance Evaluation From Stimulation and Completion Parameters in the Permian Basin: Data-Mining Approach,” by Mustafa A. Al-Alwani, SPE, and Shari Dunn-Norman, SPE, Missouri University of Science and Technology, and Larry K. Britt, SPE, NSI Fracturing, et al., prepared for the 2019 SPE/AAPG/SEG Asia Pacific Unconventional Resources Technology Conference, 18–19 November, Brisbane, Australia. The paper has not been peer reviewed. The complete paper uses 3,782 unconventional horizontal wells to analyze the effect of proppant volume and the length of the perforated lateral on short- and long-term well productivity across the Permian Basin. Tying cumulative production to completion and stimulation practices showed that increasing pumped proppant per well from 5 million to less than 10 million lbm yielded a 34% increase in 5-year cumulative average barrels of oil equivalent (BOE). Raising the pumped proppant per well to 10 million-15 million lbm and 15 million-20 million lbm increased 5-year cumulative BOE from the previous proppant range group to 27% and 18.5%, respectively. Introduction For this study, stimulation chemical data from Permian (Midland) Basin wells were downloaded from FracFocus for all horizontal wells completed and stimulated between 2012 and 2018. The data were then subjected to rigorous cleaning and processing, a process detailed in the complete paper, and then combined with DrillingInfo completion and production parameters. Combining these data provided ample parameters for stimulation, completion, and production data. The objective of the study was to investigate the production performance of Permian Basin wells as a result of different ranges of stimulation and completion parameters. Fig. 1 shows a database representation of the major counties in the Permian Basin with the number of wells in each county. Results and Discussion To substitute for any quantities of produced gas, all production data have been converted to BOE by using the conversion factor of 1 BOE=6 Mcf. The amount of proppant being pumped and the length of the perforated lateral length have been selected to represent the stimulation size and the completion magnitude, respectively.

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