Search citation statements
Paper Sections
Citation Types
Year Published
Publication Types
Relationship
Authors
Journals
Hydraulic fracturing is now considered to be a standard completions process used to improve oil and gas recovery in unconventional reservoirs. Injection/fall-off pressure from a micro-fracturing test contains important geomechanical information, including the inference of the minimum horizontal stress, natural fracture permeability, and in-situ pore pressure. The determination of in-situ stress is crucial for designing, modeling, and evaluating hydraulic fractures. This paper presents a field example of a micro-fracturing job to determine minimum horizontal stress and characterize natural fractures in terms of permeability. The analysis of micro-fracturing data consists of two parts: pre-closure analysis and after-closure analysis. The pre-closure analysis involved the analysis of early pressure fall-off data to determine the fracture closure stress of a particular formation at a specific depth. The tests were performed by injecting a small volume of fluid into a small, confined, and isolated zone at low rates to create a small fracture. The closure stress was determined from the analysis of the pressure decline after shut-in. To estimate natural fracture permeability, a series of numerical fully coupled hydro-mechanical simulations of hydraulic fracture propagation was conducted in a naturally fractured reservoir by varying the natural fracture initial permeabilities. The pressure decline after shut-in of the formation tester pump was analyzed using G-function and square-root-time methods. The point at which the G-function derivative began to deviate downward from the linear trend was identified as the point at which the fracture closes. The cycle of injection and fall-off was repeated four times. After the first cycle, in each subsequent cycle, the fracture pressure was reduced by approximately 20 psi. Based on these four cycles and petrophysical data, a customized model was developed, and poro-mechanical simulations were performed to characterize natural fractures in the formation. The simulation results explain the variation of micro-fracturing pressure history, during the four injection cycles. A comparison of the pressure history from the micro-fracture tests with the injection pressure obtained from the numerical simulation suggested that the formation included relatively impermeable natural fractures. The characterization of natural fractures during micro-fracturing provides additional information not captured by a traditional G-function or square-root-time analysis. Multiple cycles of injection and pressure fall-off provide a qualitative assessment of in-situ pore pressure and a consistent minimum in-situ stress. Understanding the fracture pressure and natural fractures in the formation is a key component of successful reservoir completion and development. However, challenges exist in the measurement of these reservoir properties with conventional methods of diagnostic fracture injection testing (DFIT™). This new analysis method represents a step forward in terms of overcoming such challenges.
Hydraulic fracturing is now considered to be a standard completions process used to improve oil and gas recovery in unconventional reservoirs. Injection/fall-off pressure from a micro-fracturing test contains important geomechanical information, including the inference of the minimum horizontal stress, natural fracture permeability, and in-situ pore pressure. The determination of in-situ stress is crucial for designing, modeling, and evaluating hydraulic fractures. This paper presents a field example of a micro-fracturing job to determine minimum horizontal stress and characterize natural fractures in terms of permeability. The analysis of micro-fracturing data consists of two parts: pre-closure analysis and after-closure analysis. The pre-closure analysis involved the analysis of early pressure fall-off data to determine the fracture closure stress of a particular formation at a specific depth. The tests were performed by injecting a small volume of fluid into a small, confined, and isolated zone at low rates to create a small fracture. The closure stress was determined from the analysis of the pressure decline after shut-in. To estimate natural fracture permeability, a series of numerical fully coupled hydro-mechanical simulations of hydraulic fracture propagation was conducted in a naturally fractured reservoir by varying the natural fracture initial permeabilities. The pressure decline after shut-in of the formation tester pump was analyzed using G-function and square-root-time methods. The point at which the G-function derivative began to deviate downward from the linear trend was identified as the point at which the fracture closes. The cycle of injection and fall-off was repeated four times. After the first cycle, in each subsequent cycle, the fracture pressure was reduced by approximately 20 psi. Based on these four cycles and petrophysical data, a customized model was developed, and poro-mechanical simulations were performed to characterize natural fractures in the formation. The simulation results explain the variation of micro-fracturing pressure history, during the four injection cycles. A comparison of the pressure history from the micro-fracture tests with the injection pressure obtained from the numerical simulation suggested that the formation included relatively impermeable natural fractures. The characterization of natural fractures during micro-fracturing provides additional information not captured by a traditional G-function or square-root-time analysis. Multiple cycles of injection and pressure fall-off provide a qualitative assessment of in-situ pore pressure and a consistent minimum in-situ stress. Understanding the fracture pressure and natural fractures in the formation is a key component of successful reservoir completion and development. However, challenges exist in the measurement of these reservoir properties with conventional methods of diagnostic fracture injection testing (DFIT™). This new analysis method represents a step forward in terms of overcoming such challenges.
The objective of this work is to highlight wireline straddle-packer microfrac testing is an underutilized technology today by the oil and gas industry despite these tests have evolved significantly in the last 10 years. This work also summaries the technological improvements and latest advances of microfrac service deployment in addition to share the future of in-situ reservoir stress monitoring from fiber-optic Distributive Strain Sensing (DSS). Over 500 microfrac tests and more than 30 decades of stress testing data are compiled and analyzed from science and data-collection pilot wells drilled around the world. The number of pressure tests collected by the industry is estimated by Baker Hughes’ database and competitor’s market share to compare the substantial difference between the number of reservoir pressure points and microfrac stress test collected every year for the last decade. Machine learning algorithms predict tectonic strain values to match microfrac formation breakdown and fracture closure using basic rock elastic properties to calculate the static stiffness of the formations where the stress tests are obtained. The microfrac success rate has increased from 20% to 85% in the last decade thanks to upgraded straddle packer tool capabilities and improved operational practices. The formation breakdown pressure data consistently indicates higher level of uncertainty than reservoir pore pressure. However, the industry collects several orders of magnitude more pore pressure points than microfrac stress tests every year. Possibly, this is the consequence of using basic effective in-situ stress ratio models by geomechanics practitioners that requires few calibration points from leak-off tests or borehole breakout modelling. This practice could treat microfracs as a nice-to-have calibration data rather than an essential subsurface tectonic stress information. A significant increase in microfrac testing is observed during the US shale gas revolution in order to calibrate stress profile models where basic effective stress ratio models failed to predict a lithology-dependent stress contrast between pay and non-pay intervals. The data shows the importance of using microfrac tests to calibrate subsurface tectonic strain values and predict accurate hydraulic fracture containment. The predicted tectonic strain data from microfrac testing shows values between 0.05 to 1.2 mStrain which can also be detected with current fiber optic technology using two centimeter grading and capable of detecting two micrometers of deformation. This new distributed strain sensing technology can be implemented to detect changes of stress and strain as the reservoir is developed by producer and injector wells. This technology may be the future of stress monitoring at the reservoir scale.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.