Uncertain Low Penetration Rate -- A Practical Issue in Mobile Intelligent Transportation Systems

The penetration rate is one of the most important factors that affects the effectiveness of the mobile phone-based traffic state estimation. This article thoroughly investigates the influence of the penetration rate on the traffic state estimation using mobile phones as traffic probes and proposes reasonable solutions to minimize such influence. In this research, the so-called “acceptable” penetration rate, at which the estimation accuracy is kept as an “acceptable” level, is identified. This recognition is important to bring the mobile phone-based traffic state estimation systems into realization. In addition, two novel “velocity-density inference” models, namely the “adaptive” and the “adaptive feedback” velocity-density inference circuits, are proposed to improve the effectiveness of the traffic state estimation. Furthermore, an artificial neural network-based prediction approach is introduced to a the effectiveness of the velocity and the density estimation when the penetration rate degrades to 0%. These improvements are practically meaningful since they help to guarantee a high accurate traffic state estimation, even in cases of very low penetration rate. The experimental evaluations reveal the effectiveness as well as the robustness of the proposed solutions.

Traffic data collection and traffic state estimation are essential for traffic congestion management within smart cities framework. To overcome the inherent shortcomings of single-source data collection techniques, this paper investigates a data fusion method, employing fix and floating (or probe) data types, to estimate traffic states on both freeway and signalized arterial links. The proposed method explores the relationships among the number of probe data, sampling interval of probe vehicles, probe vehicles penetration rate, estimation interval, and traffic density. This paper introduces the definition of generalized penetration rate based on the quotient of travel time of probe vehicles and total travel time. Furthermore, traffic generalized density is estimated with probe data and observed long-term average of actual penetration rate of probe vehicles. Furthermore, real-time penetration rate of probe vehicles is estimated with probe data and density measurements collected by fixed loop detectors. The validity of the proposed method is investigated by extensive tests on NGSIM field data and microsimulation data that reveal accurate performance for real-time traffic state estimation. The proposed methods demonstrate promising extensions for network-wide traffic state estimation.

Real time traffic states estimation on arterials based on trajectory data

There has been extensive research in traffic state estimation that accounts for the stochastic nature of traffic flow models. However, these studies often exhibit limitations such as an exclusive focus on motorway traffic and a reliance on a single data source. This paper departs from these methods by introducing a stochastic estimation framework that is specifically designed for urban arterial traffic. The framework has the capability to incorporate multiple data sources, which serves to improve its accuracy and robustness. The framework is composed of three components: (i) a stochastic traffic flow model, (ii) a filtering algorithm, and (iii) an algorithm for incorporating multi-source measurements. In terms of the traffic model, we introduce a new stochastic Switching Mode Model that can be applied to arterial roads that have both signalized and unsignalized intersections. This model does not consider uncertainty in the current mode of operation, which substantially reduces the computational complexity because there is only one mode at each time step. Furthermore, we propose three different filtering algorithms for multi-source traffic estimation, including the incremental stochastic Kalman Filter (SKF), the incremental stochastic Unscented Kalman Filter (SUKF), and the hybrid approach. Since the SKF can only deal with linear functions, non-linear measurement equations need to be linearized using first-order Taylor expansions. The SUKF is based on the Unscented Transform (UT), which enables it to work with a wider range of functions regardless of linearity, non-linearity, or non-differentiability. The hybrid algorithm is a combination of the SKF and the SUKF, in which linear equations are treated similarly to the SKF, and non-linear equations are handled with the UT in the same way as in the SUKF. The performances of these algorithms were similar when applied to the synthetic data of an urban arterial in Christchurch CBD, New Zealand. The hybrid algorithm, however, worked slightly better and was more stable than the other two.

A new synthetic-based Penetration Rate Enhancer designed as an environmentally acceptable alternative to terpenes is shown to be an effective treatment for increasing penetration rates by preventing or minimizing bit balling while drilling active shales prone to forming "gumbo". Both laboratory drilling simulator results and field case-histories are presented documenting the effectiveness of this chemistry and efficient techniques for application. The results show that this chemistry is effective with tri-cone bits but is more efficient with polycrystalline diamond cutter (PDC) bits and is particularly efficient when used with advanced design polished cutter bits with super sharp PDC cutters. Introduction Stagnant oil & gas prices coupled with escalating rig rates have placed an ever increasing premium on reducing drilling costs. The most significant component of the total drilling cost is typically the rig operating cost which makes drilling rate one of the key variable in determining a project's economic viability. One of the most significant developments toward maximizing penetration rates over the last 2 decades, particularly drilling shales, has been the introduction of PDC bits. These bits have set new penetration rate benchmarks when drilling shales, especially when used with oil and synthetic-based emulsion drilling fluids. PDC bits used with water-based drilling fluids have not performed nearly as well, especially in drilling deeper shales. The problem has proven to be so persistent that the Drilling Engineering Association formed cooperative research project DEA #90 in 1995 to investigate the causes. The initial findings of DEA 90, as released in Test Module #1, duplicated slow ROP scenarios seen in actual drilling operations. Low penetration rates with PDC bits in plastic shales, drilled at high bottom hole pressures with water-based drilling fluids were ascertained, along with mechanisms causing the slow penetration rates. PDC Bits and Shale PDC bits have traditionally been suited for drilling in shales. These bits become less effective, and possibly uneconomical, as certain shale types become stressed from increase in depth, mud weight, and cutter induced loading. Normally, PDC cutters easily shear shale as they drill. Shearing generates formation chips, which can be removed from under the bit face using conventional hydraulics. As the pressure stresses increase at depth, the formation becomes more plastic and requires different bit cutting mechanics to cut efficiently. This is accelerated as fluid density increases. Shale, and other ductile formations, tend to flow more easily at stress and thus conform to, and adhere more strongly, to surfaces they contact. In effect, the shear stress necessary to displace the cuttings from the face of a cutting element increases significantly under these conditions. It is believed that the shear stress required to displace a cutting from the face of cutter may be higher than the stresses necessary to shear the formation in front of the cutter. This phenomenon has been documented in the literature as a "built up edge". As the built up edge remains relatively stationary on the face of the cutting element, it becomes a tough, solidified agglomeration of formation, through shear enhanced compassion and hydration. The built up edge forms a very dull geometry, which hinders the performance of the cutting structure and results in unacceptable penetration rates. The concept of generating lower volume chips (thinner) and lower frictional forces on the cutter face, has proven to be a combination of technologies which significantly improves the drilling rate when drilling shales that typically result in low penetration rates. By polishing the surface of the diamond cutting element, the shear stress to displace the formation chip is drastically lowered. In addition, the rock strengthening effect that often occurs in front of a cutting element is avoided, allowing the formation to be failed at lower bit loads. It is also advantageous to concentrate the highest cutting stresses where the formation stress is initiated. P. 493^

Harder formations in geothermal and deeper oil and gas wells typically see lower rates of penetration (ROPs) and higher bit wear. Recent drilling experience in the Utah FORGE observed higher ROPs and longer runs using lighter drilling muds and new cutter technologies. Questions have arisen regarding reasons for the improved ROP and if the higher ROP is due to increased spurt loss or reduced hydrostatic pressure from lighter drilling fluids. A study was performed on an inhouse rig using two polycrystalline diamond compact (PDC) bits (4-bladed vs 5-bladed) to analyze the effect of confinement on ROP in Sierra White Granite (SWG). Tests were performed at confinements of 0, 1000, and 2000 psi and at constant rotational speeds of 80 and 150 RPM. Results show that when confinement is increased, ROPs are similar in the ineffective cutting Phase I. However, results in Phase II indicate that ROP is largely different between the unconfined and higher confinement testing. No significant effect on ROP is observed between 1000 and 2000 psi. At zero confinement, the 4-bladed bit outperformed the 5-bladed bit while at 1000 and 2000 psi confinement, the performance was similar. This paper presents the laboratory results with potential applications to hard rock geothermal drilling. INTRODUCTION Confining pressure in drilling terms is the hydrostatic pressure at the bottom of the well while drilling in impermeable formations. Many studies have outlined the effects of confining pressure on rock deformation but the effect of confinement on drilling efficiency and ROP is yet to be thoroughly investigated. In recent years, hard rock drilling especially in geothermal reservoirs has faced great challenges such as low penetration rates and high bit wear leading to high drilling costs. Advanced bit technology is required to maneuver the unique physical and mechanical properties of hard and abrasive rocks at high confining pressures. Although roller cone bits were predominantly used to drill hard and abrasive formations, PDC bits with enhanced cutter properties and ‘special’ cutter geometries are at the forefront of drilling research and technology. During the past years, new PDC cutter technology has improved cutter wear resistance and its capability to carry higher loads. Recent drilling experience in the Utah FORGE observed higher ROPs and longer bit runs using lighter drilling muds and new cutter technologies including shaped cutters (Dupriest and Noynaert, 2022). Glowka, (1985) discussed the potential for developing a PDC drill bit for hard rock applications such as geothermal drilling. Different cutter geometrical shapes and designs are currently being designed to address heavy impact damage in hard formations, prevent thermal damage and extend drilling runs while maximizing ROP. Zhu et al. (2022) analyzed the loading performance and rock cutting mechanism of different PDC cutters by using single cutter analysis to study geometry, aggressiveness, and stress distribution in granite. Xiong et al. (2022) studied the performance of a stinger PDC cutter in granite to evaluate cutting force and mechanical specific energy (MSE). Barnett et al. (2022) studied the effects of drillstring torsional vibration on ROP with PDC bits in hard rock. Wang et al. (2020) designed a PDC hybrid drill bit to navigate hard and abrasive formations. Akhtarmanesh et al. (2021) designed an ROP model for drilling hard and abrasive formations for PDC bits. It was seen that distinct phases of drilling exist where Phase I is the inefficient drilling phase due to insufficient WOB and Phase II is the efficient drilling phase due to higher WOB which results in a higher ROP. Figure 1 shows the different phases of drilling as described by Akhtarmanesh et al. (2021). Due to the complexity of drilling in hard formations, more testing is crucial to completely understand the effects of temperature, rock behavior, and confining pressure on ROP and bit cutting mechanism. An understanding of bit cutting action in hard rock is crucial for successful ROP optimization and improving drilling efficiency.

A data fusion approach for real-time traffic state estimation in urban signalized links

Traffic state estimation (TSE) is a critical component of the efficient intelligent transportation systems (ITS) operations. In the literature, TSE methods are divided into model-driven methods and data-driven methods. Each approach has its limitations. The physics information-based neural network (PINN) framework emerges to mitigate the limitations of the traditional TSE methods, while the state-of-art of such a framework has focused on single road segments but can hardly deal with traffic networks. This paper introduces a PINN framework that can effectively make use of a small amount of observational speed data to obtain high-quality TSEs for a traffic network. Both model-driven and data-driven components are incorporated into PINNs to combine the advantages of both approaches and to overcome their disadvantages. Simulation data of simple traffic networks are used for studying the highway network TSE. This paper demonstrates how to solve the popular LWR physical traffic flow model with a PINN for a traffic network. Experimental results confirm that the proposed approach is promising for estimating network traffic accurately.

Traffic state estimation (TSE) refers to the inference of traffic state information from partially observed traffic data and some prior knowledge of the traffic dynamics. TSE plays a key role in traffic management as traffic control relies on an accurate estimation of the traffic states. Macroscopic traffic models describe traffic dynamics with aggregated values such as traffic density, velocity, and flow and are often employed for TSE of a freeway road segment. This paper integrates Partial Differential Equation (PDE) observer design and deep learning paradigm to estimate spatial-temporal traffic states from boundary sensing. With the PDE observer providing an rigorous guarantee for state estimates, we propose Observer-Informed Deep Learning (OIDL) paradigm which is a data-driven solution to TSE that leverages the PDE observer design. An Observer-Informed Neural Network (OINN) is constructed by training NN to generate state estimates and use the boundary observer for regularization. The OINN forms a novel class of data-efficient function approximators that encode PDE observer as theoretical guarantee and improves the accuracy and convergence speed. Experiments using NGSIM data-set demonstrate that the proposed OIDL reduces the estimation error compared to either the model-based observer or either the data-driven neural network. We also compare OIDL with the existing Physics-informed Deep Learning (PIDL) approach for TSE.

In this paper, we studied the relationship between Vehicle Total Time (VTT) and the traffic conditions for signalized intersection. VTT is the duration that a vehicle remains associated with road side unit RSU through group leader. As the vehicle travels along an intersection and encounters different degrees of delay (i.e. different traffic conditions), the value of VTT varies accordingly. Intuitively, the higher value of VTT indicates the worse degree of traffic condition. This study, investigates the ways to estimate the traffic condition of an intersection based on the measurements of VTT taken from Vehicle to Everything V2X protocol of the COLOMBO project. Specifically, we estimate the traffic conditions of an intersection based on average speed and its derivative (acceleration) using fuzzy logic. The proposed method is evaluated using a well known network simulator benchmark (ns3). The simulation results reveal that VTT can be used with data fusion techniques to estimate the traffic conditions of an intersection with good accuracy even under low penetration rates (PR). This is comparable to a similar technique used in COLOMBO to abstract the traffic conditions of an intersection based on average speed. As a result, this study suggests a potential use of VTT as traffic indicator for signal control design in the future work.

In the past, wells drilled in Crater field required an average of 310 days to reach total depth (TD), mainly because of operational problems, including drillstring twist-off, stuck pipe, unplanned sidetracks, excessive directional complexity when drilling, and under-reaming. Low rates of penetration (ROP) while drilling the Eocene and Paleocene intervals also impacted drilling time. Design optimization provided by a major service company allowed the operator to finish the recent Crater-52 well 95 days ahead of plan and 155 days ahead of the average duration for previous wells. This achievement significantly reduced operating costs and helped facilitate an anticipated production of 285,000 barrels of oil. Key achievements are summarized below: Drilled the 17.5-in. section 500 m deeper than the original design using 5.5-in. drillpipe instead of the traditional 5-in. Set 9.875-in. casing at the Medium Eocene Level, which made it possible to drill the 8.5-in. section with a much lower differential pressure, increasing ROP from 1 to 5 m/hr. Saved $3.4 MMUSD in operational costs and recovered 285,000 barrels of oil ahead of schedule. Reduced non-productive time (NPT) from 30% to 5.5%. Lithological characterization, calibration, and updating of the geo-mechanical model helped the operator drill 500 m deeper in the 17.5-in. section, which eliminated the need for hole enlarging in two problematic sections. Using 5.5-in. drill pipe improved hole cleaning, with faster ROPs as a direct consequence. Drilling the 12.25-in. section to the Medium Eocene made it possible to drill the 8.5-in. section with lighter mud weights, decrease differential pressure, and elevate ROP. Lost circulation risks were also minimized. The NPT was 83% lower than the field average. Based on these results, future Crater field well designs will also be optimized, and the drilling team is evaluating the feasibility of applying the optimized design in other fields. The improved hydraulics parameters helped increase hole-cleaning effectiveness and ROP while preventing operational incidents in the 17.5-in. and 12.25-in. intervals.

Traffic state estimation is one of the very basic and most important tasks in intelligent transportation systems. Data collected from multiple types of traffic sensors are valuable sources to estimate traffic state. However, those data are always incomplete for the cases such as sensors broken, data missing, etc. The incomplete data can also be used to accurately estimate traffic state by data prediction and data fusion. Firstly, the missing data are predicted based on the data correlations extracted on large amount of historical taxi GPS data. Then, data collected from multiple sensors are fused to get more accurate results. Finally, computing of data fusion is parallelized. The main research focus is placed on learning for extracting the inherent spatio-temporal correlations of traffic data and fusing multimedia traffic data. Accuracy and efficiency experiments are conducted and the experimental results demonstrate that our method can process incomplete data and achieve accurate and real-time traffic state estimation.

This paper addresses the problem of traffic state estimation (TSE) in the presence of heterogeneous sensors which include both fixed and moving sensors. Traditional fixed sensors are expensive and cannot be installed throughout the highway. Moving sensors such as Connected Vehicles (CVs) offer a relatively cheap alternative to measure traffic states across the network. Moving forward it is thus important to develop such models that effectively use the data from CVs. One such model is the nonlinear second-order Aw-Rascle-Zhang (ARZ) model which is a realistic traffic model, reliable for TSE and control. A state-space formulation is presented for the ARZ model considering junctions in the formulation which is important to model real highways with ramps. A Moving Horizon Estimation (MHE) implementation is presented for TSE using a linearized ARZ model. Various state-estimation methods used for TSE in the literature along with the presented approach are compared with regard to accuracy and computational tractability with the help of a numerical study using the VISSIM traffic simulation software. The impact of various strategies for querying CV data on the estimation performance is also considered. Several research questions are posed and addressed with a thorough analysis of the results.

Compression is an essential aspect of real-time operations as the bandwidth of transmitted information is very limited during logging while drilling. Processing of nuclear magnetic resonance (NMR) data involves the inversion of echo trains (ET), which are compressed downhole and transmitted via telemetry. The performance of individual NMR ET compression techniques also needs to be evaluated on the input signal level because the comparison of inversion results is highly dependent on regularization. Improved compression is key to properly encoding and decoding rate of penetration (ROP)-dependent motion effects with the goal of improving the accuracy of NMR T2 distribution at increased ROP in real time. The value of information of an ET differs over its range. First, early echoes have a large impact on the total porosity. They require a large contribution to the computation of, i.e., root-mean-squared error, used for comparing compression techniques. Secondly, non-exponential motion artifacts have a non-negligible impact on the inversion for cutoff-dependent volumetrics with increasing ROP. We propose a logarithmic weighting of the error contribution when comparing different compression implementations. Principal component analysis (PCA) is a linear transformation and is, therefore, very robust. It can reduce the dimensionality of data even given highly clustered inputs. In a previous implementation of PCA, ROP motion effects were neglected in developing the basis set of vectors at the low ROP used in earlier drilling operations. However, neglecting ROP motion effects may have noticeable effects on inversion results for porosity and volumetrics at increased ROP. Therefore, new PCA basis vectors are developed to account for these motion effects. Parallel with the development of the enhanced PCA basis vectors, an alternative denoising and compression concept utilizing a neural network autoencoder was developed and evaluated. The autoencoder first encodes the input data to a lower-dimensional representation. The original input is reconstructed from this compact latent space encoding using a decoding block. The autoencoder is trained on a synthetic data set comprising different subsurface properties and acquisition parameters learning the salient features. With the aim of implementing the encoding block on a low latency/memory device downhole, optimization of the network topology involved both the size of the network and the accuracy of the decompressed signals for real-time NMR inversion. Validation of the PCA and autoencoder compression is performed using inverted volumetrics of the encoded and decoded ETs. A new PCA basis for NMR data compression was developed to properly encode and decode ROP-related motion effects. Its performance was compared to a conventional compression technique. In addition, results of a denoising and compression algorithm for NMR ET based on a fully connected neural network autoencoder are presented. Tests show an improvement in the axial motion effects introduced by increasing ROP. By using a weighted error metric to compare individual compression implementations, it is possible to focus on the value of information on the early echoes. Accounting for ROP motion effects in the development of a new PCA basis, as well as using the autoencoder in training, improves the inversion of movable and irreducible fluid components. In addition, a reduction in the amount of data to be transmitted allows for a further decrease in the telemetry footprint of the NMR measurement. Both approaches increase the amount of transmitted information while improving the evaluation of the subsurface environment.

Traffic state estimation (TSE) fundamentally involves solving high-dimensional spatiotemporal partial differential equations (PDEs) governing traffic flow dynamics from limited, noisy measurements. While Physics-Informed Neural Networks (PINNs) enforce PDE constraints point-wise, this paper adopts a physics-informed deep operator network (PI-DeepONet) framework that reformulates TSE as an operator learning problem. Our approach trains a parameterized neural operator that maps sparse input data to the full spatiotemporal traffic state field, governed by the traffic flow conservation law. Crucially, unlike PINNs that enforce PDE constraints point-wise, PI-DeepONet integrates traffic flow conservation model and the fundamental diagram directly into the operator learning process, ensuring physical consistency while capturing congestion propagation, spatial correlations, and temporal evolution. Experiments on the Next Generation Simulation (NGSIM) dataset demonstrate superior performance compared to state-of-the-art baselines. Further analysis reveals insights into optimal function generation strategies and branch network complexity. Additionally, the impact of input function generation methods and the number of functions on model performance is explored, highlighting the robustness and efficacy of proposed framework.