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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.
In initial fracturing of tight oil and gas reservoirs, due to the influence of geological and technological factors, the fracture conductivity has decreased, and the single-well productivity has been reduced. It is urgent to repeat transformation to restore or increase productivity. Well selection and layer selection is one of the key factors that affect the design of re-fracturing and the effect of stimulation. Based on a big database of well-sites, establishing machine intelligence theory determines the elasto-plasticity, permeability, porosity, completion parameters, production decline parameters and skin coefficient that affect the effect of re-fracturing stimulation by dimensionless parameter method of well and layer selection and its stimulation evaluation model. Combined with artificial neural network and BP algorithm, the index weights of strata with different reservoir physical properties are calculated to analyze the final evaluation value of fracturing effect. On the basis of remaining oil distribution research, scale extended fracture repeated fracturing is increased, injection-production well pattern is improved, scale repeated fracturing effect is increased, well pattern is improved, target layer is repeatedly fractured, and oil increase effect is obvious after fracturing.
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