During the past several years, there has been a growing recognition of the threats posed by the use of shallow tunnels against both international border security and the integrity of critical facilities. This has led to the development and testing of a variety of geophysical and surveillance techniques for the detection of these clandestine tunnels. The challenges of detection of these tunnels arising from the complexity of the near surface environment, the subtlety of the tunnel signatures themselves, and the frequent siting of these tunnels in urban environments with a high level of cultural noise, have time and again shown that any single technique is not robust enough to solve the tunnel detection problem in all cases. The question then arises as to how to best combine the multiple techniques currently available to create an integrated system that results in the best chance of detecting these tunnels in a variety of clutter environments and geologies. This study utilizes Taguchi analysis with simulated sensor detection performance to address this question. The analysis results show that ambient noise has the most effect on detection performance over the effects of tunnel characteristics and geological factors.
The project objective was to detail better ways to assess and exploit intelligent oil and gas field information through improved modeling, sensor technology, and process control to increase ultimate recovery of domestic hydrocarbons. To meet this objective we investigated the use of permanent downhole sensors systems (Smart Wells) whose data is fed real-time into computational reservoir models that are integrated with optimized production control systems. The project utilized a three-pronged approach (1) a value of information analysis to address the economic advantages, (2) reservoir simulation modeling and control optimization to prove the capability, and (3) evaluation of new generation sensor packaging to survive the borehole environment for long periods of time. The Value of Information (VOI) decision tree method was developed and used to assess the economic advantage of using the proposed technology; the VOI demonstrated the increased subsurface resolution through additional sensor data. Our findings show that the VOI studies are a practical means of ascertaining the value associated with a technology, in this case application of sensors to production. The procedure acknowledges the uncertainty in predictions but nevertheless assigns monetary value to the predictions. The best aspect of the procedure is that it builds consensus within interdisciplinary teams The reservoir simulation and modeling aspect of the project was developed to show the capability of exploiting sensor information both for reservoir characterization and to optimize control of the production system. Our findings indicate history matching is improved as more information is added to the objective function, clearly indicating that sensor information can help in reducing the uncertainty associated with reservoir characterization. Additional findings and approaches used are described in detail within the report. The next generation sensors aspect of the project evaluated sensors and packaging survivability issues. Our findings indicate that packaging represents the most significant technical challenge associated with application of sensors in the downhole environment for long periods (5+ years) of time. These issues are described in detail within the report. The impact of successful reservoir monitoring programs and coincident improved reservoir management is measured by the production of additional oil and gas volumes from existing reservoirs, revitalization of nearly depleted reservoirs, possible re-establishment of already abandoned reservoirs, and improved economics for all cases. Smart Well monitoring provides the means to understand how a reservoir process is developing and to provide active reservoir management. At the same time it also provides data for developing high-fidelity simulation models. This work has been a joint effort with Sandia National Laboratories and UT-Austin's Bureau of Economic Geology, Department of Petroleum and Geosystems Engineering, and the Institute of Computational and Engineering Mathematics.
An important application of seismic and acoustic unattended ground sensors (UGS) is the estimation of the three dimensional position of an emitting target. Seismic and acoustic data derived from UGS systems provide the taw information to determine these locations, but can be processed and analyzed in a number of ways using varying amounts of auxiliary information. Processing methods to improve arrival time picking for continuous wave sources and methods for determining and defining the seismic velocity model are the primary variables affecting the localization accuracy. Results using field data collected from an underground facility have shown that using an iterative time picking technique significantly improves the accuracy of the resulting derived target location. Other processing techniques show little advantage over simple crosscorrelation along in terms of accuracy, but may improve the ease with which time picks can be made. An average velocity model found through passive listening or a velocity model determined from a calibration source near the target source both result in similar location accuracies, although the use of station correction severely increases the location error.