Improved Upscaling & Well Placement Strategies for Tight Gas Reservoir Simulation and Management

Tight gas reservoirs provide almost one quarter of the current U.S. domestic gas production, with significant projected increases in the next several decades in both the U.S. and abroad. These reservoirs constitute an important play type, with opportunities for improved reservoir simulation & management, such as simulation model design, well placement. Our work develops robust and efficient strategies for improved tight gas reservoir simulation and management. Reservoir simulation models are usually acquired by upscaling the detailed 3D geologic models. Earlier studies of flow simulation have developed layer-based coarse reservoir simulation models, from the more detailed 3D geologic models. However, the layer-based approach cannot capture the essential sand and flow. We introduce and utilize the diffusive time of flight to understand the pressure continuity within the fluvial sands, and develop novel adaptive reservoir simulation grids to preserve the continuity of the reservoir sands. Combined with the high resolution transmissibility based upscaling of flow properties, and well index based upscaling of the well connections, we can build accurate simulation models with at least one order magnitude simulation speed up, but the predicted recoveries are almost indistinguishable from those of the geologic models. General practice of well placement usually requires reservoir simulation to predict the dynamic reservoir response. Numerous well placement scenarios require many reservoir simulation runs, which may have significant CPU demands. We propose a novel simulation-free screening approach to generate a quality map, based on a combination of static and dynamic reservoir properties. The geologic uncertainty is taken into consideration through an uncertainty map form the spatial connectivity analysis and variograms. Combining the quality map and uncertainty map, good infill well locations and drilling sequence can be determined for improved reservoir management. We apply this workflow to design the infill well drilling sequence and explore the impact of subsurface also, for a large-scale tight gas reservoir. Also, we evaluated an improved pressure approximation method, through the comparison with the leading order high frequency term of the asymptotic solution. The proposed pressure solution can better predict the heterogeneous reservoir depletion behavior, thus provide good opportunities for tight gas reservoir management

RACIAL DISPARITIES IN MORTALITY RISKS IN A SAMPLE OF THE U.S. MEDICARE POPULATION

Racial disparities in mortality risks adjusted by socioeconomic status (SES) are not well understood. To add to the understanding of racial disparities, we construct and analyze a data set that links, at individual and zip code levels, three government databases: Medicare, Medicare Current Beneficiary Survey and U.S. Census. Our study population includes more than 4 million Medicare enrollees residing in 2095 zip codes in the Northeast region of U.S. We develop hierarchical models to estimate Black-White disparity in risk of death, adjusted by both individual-level and zip codelevel income. We define population-level attributable risk (AR), relative attributable risk (RAR) and odds ratio (OR) of death comparing Blacks versus Whites, and we estimate these parameters using a Bayesian approach via Markov chain Monte Carlo. By applying the multiple imputation method to fill in missing data, our estimates account for the uncertainty from the missing individual-level income data. Results show that for the Medicare population being studied, there is a statistically and substantively significantly higher risk of death for Blacks compared with Whites, in all three measures of AR, RAR, and OR, both adjusted and not adjusted for income. In addition, after adjusting for income we find statistically significant reduction of AR but not of RAR and OR

Joint Transmit Resource Management and Waveform Selection Strategy for Target Tracking in Distributed Phased Array Radar Network

In this paper, a joint transmit resource management and waveform selection (JTRMWS) strategy is put forward for target tracking in distributed phased array radar network. We establish the problem of joint transmit resource and waveform optimization as a dual-objective optimization model. The key idea of the proposed JTRMWS scheme is to utilize the optimization technique to collaboratively coordinate the transmit power, dwell time, waveform bandwidth, and pulse length of each radar node in order to improve the target tracking accuracy and low probability of intercept (LPI) performance of distributed phased array radar network, subject to the illumination resource budgets and waveform library limitation. The analytical expressions for the predicted Bayesian Cram\'{e}r-Rao lower bound (BCRLB) and the probability of intercept are calculated and subsequently adopted as the metric functions to evaluate the target tracking accuracy and LPI performance, respectively. It is shown that the JTRMWS problem is a non-linear and non-convex optimization problem, where the above four adaptable parameters are all coupled in the objective functions and constraints. Combined with the particle swarm optimization (PSO) algorithm, an efficient and fast three-stage-based solution technique is developed to deal with the resulting problem. Simulation results are provided to verify the effectiveness and superiority of the proposed JTRMWS algorithm compared with other state-of-the-art benchmarks

Energy and Time-Aware Inference Offloading for DNN-based Applications in LEO Satellites

In recent years, Low Earth Orbit (LEO) satellites have witnessed rapid development, with inference based on Deep Neural Network (DNN) models emerging as the prevailing technology for remote sensing satellite image recognition. However, the substantial computation capability and energy demands of DNN models, coupled with the instability of the satellite-ground link, pose significant challenges, burdening satellites with limited power intake and hindering the timely completion of tasks. Existing approaches, such as transmitting all images to the ground for processing or executing DNN models on the satellite, is unable to effectively address this issue. By exploiting the internal hierarchical structure of DNNs and treating each layer as an independent subtask, we propose a satellite-ground collaborative computation partial offloading approach to address this challenge. We formulate the problem of minimizing the inference task execution time and onboard energy consumption through offloading as an integer linear programming (ILP) model. The complexity in solving the problem arises from the combinatorial explosion in the discrete solution space. To address this, we have designed an improved optimization algorithm based on branch and bound. Simulation results illustrate that, compared to the existing approaches, our algorithm improve the performance by 10%-18%Comment: Accepted by ICNP 2023 Worksho

Joint Route Optimization and Multidimensional Resource Management Scheme for Airborne Radar Network in Target Tracking Application

In this article, we investigate the problem of joint route optimization and multidimensional resource management (JRO-MDRM) for an airborne radar network in target tracking application. The mechanism of the proposed JRO-MDRM scheme is to adopt the optimization technique to collaboratively design the flight route, transmit power, dwell time, waveform bandwidth, and pulselength of each airborne radar node subject to the system kinematic limitations and several resource budgets, with the aim of simultaneously enhancing the target tracking accuracy and low probability of intercept (LPI) performance of the overall system. The predicted Bayesian Cramér–Rao lower bound and the probability of intercept are calculated and employed as the metrics to gauge the target tracking performance and LPI performance, respectively. It is shown that the resulting optimization problem is nonlinear and nonconvex, and the corresponding working parameters are coupled in both objective functions, which is generally intractable. By incorporating the particle swarm optimization and cyclic minimization approaches, an efficient four-step solution algorithm is proposed to deal with the above problem. Extensive numerical results are provided to demonstrate the correctness and advantages of our developed scheme compared with other existing benchmarks