Multi-sensor optimal deployment based efficient and synchronous data acquisition in large three-dimensional physical similarity simulation

Purpose This paper aims to propose a deployment optimization and efficient synchronous acquisition method for compressive stress sensors used by stress distribution law research based on the genetic algorithm and numerical simulations. The authors established a new method of collecting the mining compressive stress-strain distribution data to address the problem of the number of sensors and to optimize the sensor locations in physical similarity simulations to improve the efficiency and accuracy of data collection. Design/methodology/approach First, numerical simulations were used to obtain the compressive stress distribution curve under specific mining conditions. Second, by comparing the mean square error between a fitted curve and simulation data for different numbers of sensors, a genetic algorithm was used to optimize the three-dimensional (3D) spatial deployment of sensors. Third, the authors designed an efficient synchronous acquisition module to allow distributed sensors to achieve synchronous and efficient acquisition of hundreds of data points through a built-in on-board database and a synchronous sampling communication structure. Findings The sensor deployment scheme was established through the genetic algorithm, A synchronous and selective data acquisition method was established for reduced the amount of sensor data required under synchronous acquisition and improved the system acquisition efficiency. The authors obtained a 3D compressive stress distribution when the advancement was 200 m on a large-scale 3D physical similarity simulation platform. Originality/value The proposed method provides a new optimization method for sensor deployment in physical similarity simulations, which improves the efficiency and accuracy of system data acquisition, providing accurate acquisition data for experimental data analysis.

Overcoming Production Logging Challenges in Evaluating Extended Reach Horizontal Wells with Advanced Completions

Objective/ Scope Production logging analysis is essential to understand and evaluate reservoir performance throughout the lifetime of an oil well. Data acquisition and analysis is known to be challenging in modern extended reach horizontal wells due to multiple factors such as conveyance difficulties, fluid segregation, debris, or open hole washouts. Advanced compact multiple array production logging tool (APLT) is proposed to minimize the uncertainties related to these challenges. Method, Procedure, and Process The proposed sensor deployment method provides a comprehensive borehole coverage, thus maximizing the amount of subsurface information collected to evaluate the production performance of a horizontal well. Essential measurements are combined on six individual arms. Each arm is independently deployed which guarantees the best borehole coverage in a variety of borehole condition. Robust mechanical arm design minimizes damage, allows tolerance to decentralization, and provides greater confidence in determining the sensor locations. Each arm utilizes two fluid holdup sensors (Resistance, Optical) and one velocity sensor (Micro-Spinner). Co-location of the sensors minimizes the uncertainty related to sensor spacing when compared with previous generation of APLT. Results, Observations, Conclusions The new sensor deployment method and analysis results are discussed showing the added value in barefoot completion as well as advanced ICD completion. The holdup sensors response from previous generation APLT is compared to the advanced tool and how it relates to better borehole coverage. The results also illustrate use of high frequency optical probes for phase holdup determination. In addition, the optical probes are used to confirm bubble point pressure at in situ conditions by confidently detecting the first gas indication in the tubular. The results clearly show how a compact APLT maximizes the borehole coverage in highly deviated and horizontal wells. This is critical in collecting representative data of all segregated fluids which enables more accurate interpretation of the flow profile in the well and better understanding of reservoir performance. Novel / Additive Information The novelty of the new instrument is the ability to maximize the amount of subsurface production logging information collected with low uncertainty and minimum operational risk.

Sensor Deployment Strategy and Traffic Demand Estimation with Multisource Data

Since traffic origin-destination (OD) demand is a fundamental input parameter of urban road network planning and traffic management, multisource data are adopted to study methods of integrated sensor deployment and traffic demand estimation. A sensor deployment model is built to determine the optimal quantity and locations of sensors based on the principle of maximum link and route flow coverage information. Minimum variance weighted average technology is used to fuse the observed multisource data from the deployed sensors. Then, the bilevel maximum likelihood traffic demand estimation model is presented, where the upper-level model uses the method of maximum likelihood to estimate the traffic demand, and the lower-level model adopts the stochastic user equilibrium (SUE) to derive the route choice proportion. The sequential identification of sensors and iterative algorithms are designed to solve the sensor deployment and maximum likelihood traffic demand estimation models, respectively. Numerical examples demonstrate that the proposed sensor deployment model can be used to determine the optimal scheme of refitting sensors. The values estimated by the multisource data fusion-based traffic demand estimation model are close to the real traffic demands, and the iterative algorithm can achieve an accuracy of 10−3 in 20 s. This research has significantly promoted the effects of applying multisource data to traffic demand estimation problems.

Optimization-Based Approaches for Minimizing Deployment Costs for Wireless Sensor Networks with Bounded Estimation Errors

As wireless sensor networks have become more prevalent, data from sensors in daily life are constantly being recorded. Due to cost or energy consumption considerations, optimization-based approaches are proposed to reduce deployed sensors and yield results within the error tolerance. The correlation-aware method is also designed in a mathematical model that combines theoretical and practical perspectives. The sensor deployment strategies, including XGBoost, Pearson correlation, and Lagrangian Relaxation (LR), are determined to minimize deployment costs while maintaining estimation errors below a given threshold. Moreover, the results significantly ensure the accuracy of the gathered information while minimizing the cost of deployment and maximizing the lifetime of the WSN. Furthermore, the proposed solution can be readily applied to sensor distribution problems in various fields.

On-line Optimal Ranging Sensor Deployment for Robotic Exploration

<div>Navigation in an unknown environment without any preexisting positioning infrastructure has always been hard for mobile robots. This paper presents a self-deployable ultra wideband UWB infrastructure by mobile agents, that permits a dynamic placement and runtime extension of UWB anchors infrastructure while the robot explores the new environment. We provide a detailed analysis of the uncertainty of the positioning system while the UWB infrastructure grows. Moreover, we developed a genetic algorithm that minimizes the deployment of new anchors, saving energy and resources on the mobile robot and maximizing the time of the mission. Although the presented approach is general for any class of mobile system, we run simulations and experiments with indoor drones. Results demonstrate that maximum positioning uncertainty is always controlled under the user's threshold, using the Geometric Dilution of Precision (GDoP).</div>