The goal of this two-year research project is to utilize a pressure/salinity responsive electrically active proppant (EAP) to characterize hydrogeological response of fracture network in simulated production conditions. The project seeks to develop an approach to remotely monitor changes in pressure and/or salinity within the fractured network in near real time. The methods developed and demonstrated during this study will lead to a better understanding of the extent of proppant-filled fracture networks, formation stress states, fluid leakoff and invasion, and characterizations of engineered fracture systems.
Bureau of Economic Geology (BEG) at the University of Texas at Austin - Austin, TX 78759
Duke University – Durham, NC 27705
University of North Carolina – Raleigh, NC 27699
Because induced fracture networks’ propped zones are generally very thin (commonly less than 5 mm), they are difficult to detect and delineate at depth. Hydraulic Fracturing (HF) has evolved to a sophisticated multistep process with varying flow rates, carrier fluids (e.g., gel or slick water), proppant loadings, and proppant grain sizes. Current tools such as microseismic and tiltmeter monitoring can provide information on fracture extent but provide little or no information on the movement and final distribution of proppant or production fluids. Recovery from a HF reservoir is often a small fraction of the original oil in place ranging often to less than 10–20% ultimate recovery from tight unconventional reservoirs. As stated in the FOA1990: “Part of this problem is due to the inability of current well completion processes to effectively stimulate the entire reservoir area contacted by the wellbore. Innovative technologies are needed that can help improve the effectiveness of reservoir completion methods, maximize stimulated reservoir volume, and optimize recovery over a well’s entire producing life”.
Previous work by BEG has resulted in a set of validated electromagnetic (EM) codes to interrogate HF extent remotely by EM geophysics. Based on these results, an updated multiscale, multimode forward and inversion approach will be developed. Lab studies will be carried out to characterize the impact of salinity/pressure and flow on properties of electromagnetically active proppant (EAP). This information along with host rock properties will be used as input for solvers to discern feasibility of detection and will inform design of optimal EM and seismic survey configurations for successful demonstration of the concept. Once sensitivity of detection has been demonstrated in Year 1, field survey work will be conducted at the BEG’s Devine Field Pilot Site (DFPS) in Year 2.
This project has several significant impacts on energy production from hydraulic fracture networks and can be applied to various subsurface applications. By enabling the optimization of refracturing processes through monitoring fracture dynamics (e.g., flow, leakoff, pressure evolution, and salinity changes), this project results in more efficient production from hydraulically fractured reservoirs. The unique and comprehensive datasets collected in this study will be disseminated to the public and will lay the foundation for the advancement of additional geophysical mapping and modeling techniques. The highly instrumented and characterized EAP-filled fracture anomaly at the DFPS can be utilized as a unique asset to conduct and validate future studies related to this project.
Initial data collected from primary deployment is promising. The EM contrast response is a signal decrease during fracture opening or flow of fresh water in propped fractures (i.e., increase of contact resistance, which result from water getting between the grains of EAP, or by separation of EAP grains during fracture dilation), and a signal increase as a result of fluid leakoff into the formation during the shut-in periods (i.e., a decrease of contact resistance in EAP as a result of water leakoff, and EAP compaction). The concept is that the placed doped proppant pack is more electrically conductive when the fracture is closed than when the propped fracture is dilated further. In addition, the proppant pack is less electrically conductive when it is partially saturated by water than when it is fully saturated.
Data collected in the primary results will be utilized to develop a laboratory fracture model that simulates the in-situ impedance response of the Devine fracture during flow of high-pressure water or saline solution in the fracture. Scanning electron microscope (SEM) analysis of representative core samples will be performed to understand a reservoir to pore-scale.
This project has started generating the training data for Neural Network (NN) and will perform NN-based inversion with the synthetic data first. Another machine learning based 29 inversion solver with Extreme Learning Machine (ELM) and Convolution Neural Network (CNN) is also under investigation, which will be able to do pixel-wise inversion. The model will be updated with the history-matched results based on data collected during the preliminary field-test operations and will be used for calculations of required volumes for BP2 field deployment which we are currently planning.
This project plans to use total-field/scattered-field (TF/SF) technique to do forward modelling for the EM sensitivity analysis in order to efficiently simulate the case where the surface transmitters are far away from the injection well.