An Integrated Dilemma Zone Protection System Using Connected Vehicle Technology

Basic signal operation strategies allocate green time to different traffic movements to control the flow at an intersection. Signal control applications consider different objectives, such as coordination with multiple intersections, multimodal priority, and safety. Real-time signal control applications rely mainly on infrastructure-based detection data. With the emergence of connected vehicle technology, high-resolution data from connected vehicles will become available for signal control. This paper presents a framework that uses connected vehicle data for adaptive signal control and considers dilemma zone protection, multimodal signal priority, and coordination. Initially, the market penetration rate of connected vehicles will be low, so infrastructure-based detector actuation logic is integrated into the framework to improve performance. Simulation analysis demonstrated good results when the penetration rate was medium to high and that the actuation logic was necessary when the penetration rate was low.

Endogenous market penetration dynamics of automated and connected vehicles: Transport-oriented model and its paradox

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 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^

We estimate the long-run economic value of variable renewable generation with increasing penetration using a unique investment and dispatch model that captures long-run investment decisions while also incorporating detailed operational constraints and hourly time resolution over a full year. High time resolution and the incorporation of operational constraints are important for estimating the economic value of variable generation, as is the use of a modeling framework that accommodates new investment decisions. The model is herein applied with a case study that is loosely based on California in 2030. Increasing amounts of wind, photovoltaics (PV), and concentrating solar power (CSP) with and without thermal energy storage (TES) are added one at a time. The marginal economic value of these renewable energy sources is estimated and then decomposed into capacity value, energy value, day-ahead forecast error cost, and ancillary services. The marginal economic value, as defined here, is primarily based on the combination of avoided capital investment cost and avoided variable fuel and operations and maintenance costs from other power plants in the power system. Though the model only captures a subset of the benefits and costs of renewable energy, it nonetheless provides unique insights into how the value of that subset changes with technology and penetration level. Specifically, in this case study implementation of the model, the marginal economic value of all three solar options is found to exceed the value of a flat-block of power (as well as wind energy) by \$20--30/MWh at low penetration levels, largely due to the high capacity value of solar at low penetration. Because the value of CSP per unit of energy is found to be high with or without thermal energy storage at low penetration, we find little apparent incremental value to thermal storage at low solar penetration in the present case study analysis. The marginal economic value of PV and CSP without thermal storage is found to drop considerably (by more than \$70/MWh) as the penetration of solar increases toward 30\percent on an energy basis. This is due primarily to a steep drop in capacity value followed by a decrease in energy value. In contrast, the value of CSP with thermal storage drops much less dramatically as penetration increases. As a result, at solar penetration levels above 10\percent, CSP with thermal storage is found to be considerably more valuable relative to PV and CSP without thermal storage. The marginal economic value of wind is found to be largely driven by energy value, and is lower than solar at low penetration. The marginal economic value of wind drops at a relatively slower rate with penetration, however. As a result, at high penetration, the value of wind can exceed the value of PV and CSP without thermal storage. Though some of these findings may be somewhat unique to the specific case study presented here, the results: (1) highlight the importance of an analysis framework that addresses long-term investment decisions as well as short-term dispatch and operational constraints, (2) can help inform long-term decisions about renewable energy procurement and supporting infrastructure, and (3) point to areas where further research is warranted.

Evaluating the safety impact of connected and autonomous vehicles on motorways

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.

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.

Low penetration rate is one of the essential issues in the mobile phone based traffic state estimation model. This paper proposes an appropriate genetic algorithm (GA) mechanism to optimize the traffic state estimation model even in cases of low penetration rate. This mechanism also reduces the critical penetration rate, thus improves the error-tolerance as well as the scalability of the traffic state estimation system. The paper also investigates the ANN-based prediction model to overcome the weakness of the GA-based traffic state estimation approach when the penetration rate becomes unacceptably low. In addition, the effect of different level related road segments on the prediction effectiveness is thoroughly discussed. Consequently, this study provides practically useful instructions in verifying the data missing rate at different level related road segments to ensure the prediction accuracy. The experimental evaluations reveal the effectiveness and the robustness of the proposed solutions.

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.

Operators have expanded their search for new oil and gas reserves to more complex and challenging environments such as the ultradeepwater, presalt carbonate plays offshore Brazil. The major challenge these wells have faced historically is the well cost overrun from low rate of penetration (ROP) and downhole tool nonproductive time (NPT) related to drilling dysfunctions. To avoid costly trial and error, advanced software was combined with service provider and operator experience to get it right the first time: improving ROP while minimizing damage to the bottomhole assemblies (BHAs), drilling each section on time and on budget. An advanced 4D (spatial 3D + time) finite element transient drillstring dynamics modeling package is used to design the cutting structures, predict the drilling dynamics, evaluate different BHAs and drilling configurations, and optimize drilling parameters. BHAs for riserless, post-salt, salt, and silicified carbonate sections of the well are analyzed and optimized. Simulations are run to determine proposed BHA configurations and the potential for axial, lateral/torsional, and stick-slip vibration for different rock strengths. The BHAs are then optimized by adjusting cutting structure, stabilizer placement, hole enlargement ratio, and drillstring configuration. Once an optimum BHA is selected for each section, a sensitivity analysis is conducted on RPM, weight on bit, and torque to maximize ROP for each BHA. Three criteria are used to evaluate BHA performance: stability, robustness/reliability, and measurement quality. The performance of each optimized BHA is compared to the performance of the base case BHA from offset wells. The accuracy of the model's simulations and the optimum recommended parameters is also examined against field results. Overall, the optimized BHAs delivered up to 50% higher ROP compared to the base case from offset wells drilling similar formations. In most cases, the optimum parameters recommended by the model were also the optimum parameters in the field test. The increased confidence in the model from the field tests enabled its use to optimize the BHA further for future wells. This work presents a digital bit and drillstring design workflow, used to virtually run and analyze different BHA designs prior to running in the field. The work, tested in the field, resulted in multiple learnings shared in this paper, specifically in the realm of drilling mechanics.

Social networking services are a modern phenomenon in the field of human relations. As differentiated from western science, the issue of SNS spatial distribution is not a matter of high concern in Ukrainian researchers because of a number of objective and subjective reasons. The main task of the research is to analyze the spatial distribution of the amount of users and the penetration level of social networking services such as Facebook and Instagram in administrative districts and cities of region subordination belonging to Rivnenska oblast. Facebook is the most popular social networking service in Rivnenska oblast. There is a trend towards concentration of users' accounts in big cities: Rivne City, Dubno City, Varash City. More than 70% of users of the oblast live in the cities of oblast subordination and 58.4% of accounts are concentrated in Rivne City which is also the regional center. T. Hägerstrand's Spatial Innovation Diffusion Theory has been proved – the largest cities of the oblast are regional innovation centers. Densely populated Sarnenskyi, Bereznivskyi, and Kostopilskyi districts are distinguished among the districts. Facebook is the least used in Dubenskyi, Rivnenskуі, Ostrozkyi, Zarichnenskyi, Demydivskyi, Hoshchanskyi districts. The penetration rate of the social networking service Facebook in the oblast is 32.7% (in Ukraine – 31.0%). The highest level is in cities of oblast subordination. Rivne City and Dubno City are particularly distinguished. High levels of penetration are achieved due to the high development of technical infrastructure, through the companies' and shops' accounts, as well as accounts of residents from surrounding villages. The relatively low penetration rate in Varash City, the second largest city of Rivne oblast due to population, can be explained by the weaker connections with the surrounding area. In administrative districts, the penetration rate is much lower. A relatively high percentage of users are in Radyvylivskyi, Bereznivskyi, Kostopilskyi districts. These districts are characterized by a high level of socio-economic development and convenient socio-geographical location. The lowest level of penetration of the social networking service is in Demydivskyi, Zarichnenskyi, Hoshchanskyi districts. These are little developed and depressed territories within the oblast. The second most popular social networking service in Rivnenska oblast is Instagram (26.3% penetration rate). In terms of development, it is inferior to Facebook, and its main users are mostly young people. In the spatial distribution of Instagram users, we can trace the same trends as the Facebook. In general, the distribution of social networking services such as Facebook and Instagram in Rivnenska oblast corresponds to a hierarchical model of spatial diffusion. There is a trend towards concentration of users in big cities.

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.

Summary Positive displacement motor (PDM) bottomhole assemblies (BHAs) are widely used in horizontal drilling due to their low cost and high rate of penetration (ROP) under rotary conditions. However, their limited-trajectory control ability often requires switching to sliding drilling for sharp corrections, which significantly reduces efficiency. Moreover, the quantitative relationship between build up rate (BUR) and controllable parameters such as revolutions per minute (RPM) and mud flow rate (MFR) remains unclear under rotary conditions. To address these issues, we propose a machine learning (ML)–based model predictive control (MPC) framework that regulates trajectory solely through real-time optimization of drilling parameters. A hybrid prediction model, integrating mechanical principles and ML, is developed to map weight on bit (WOB), RPM, and MFR to BUR, with a Lipschitz continuity constraint enhancing stability and robustness. The MPC objective function is improved by incorporating adaptive weighting, which accelerates convergence, reduces overshoot, and improves control consistency. The proposed method is evaluated across five simulation scenarios, which are analysis of control behavior, objective function enhancement, piecewise setpoint tracking, comparison with classical controllers, and robustness testing. The approach is validated using field data from a horizontal well in the Junggar Basin, northwest China. The results show that the ML-MPC strategy achieves accurate setpoint tracking under various conditions, maintains strong robustness to input noise, initialization variations, and measurement disturbances (with steady-state error < 0.2°/30 m), and meets real-time computation requirements (<20 seconds per control step). Analysis of the optimized control parameters provides data-driven insights into how WOB, RPM, and MFR influence BUR. Notably, a control inertia effect is observed: When the control objective aligns with the existing trend of variation, the system responds more efficiently; when it opposes the trend, the response becomes slower and more prone to overshoot. Overall, this work presents a practical framework for real-time trajectory control and contributes to the automation and intelligent drilling technologies in the oil and gas industry.

In combination with the current developments in the area of automatically driving vehicles, the introduction of inter-vehicle communication plays a crucial role for realising the long-term objective of what is known as cooperative driving. A cornerstone for the expansion of automated vehicles is their thorough understanding of the current driving environment. For this purpose, each vehicle generates an environment model containing information about other perceived traffic participants and objects. Local perception sensors are important data providers for this model, as they contribute implicit knowledge about the environment. In combination with a direct communication link between traffic participants, explicit knowledge can be added to the environment model as well. The key concept developed within this thesis is called Collective Perception: it focuses on sharing data gathered by local perception sensors of one vehicle with other traffic participants by means of inter-vehicle communication. As a result of this concept, future applications relying on a comprehensive understanding of the current driving environment are made feasible. The analyses presented in this thesis employ a vehicular ad-hoc network (VANET) based on the standardised framework of the European IEEE 802.11p-based ITS G5 protocol stack for inter-vehicle communication. The effectiveness of the technology relies on an existing communication link between a sufficient number of communication partners - the critical mass. The expansion of inter-vehicle communication, however, can be supported by capacitating indirect effects. Collective Perception is one representative of these effects, as the information density within the network between the vehicles is increased, even at low market penetration rates. At the core of Collective Perception stands the introduction of a message format which serves as a vehicle for the exchange of sensor data within a VANET. The development of the message is influenced by two perspectives: First, the vehicle perspective affects the relevant contents of the message required by data-fusion processes and application algorithms. Second, from the network perspective, constraints resulting from the network stack and effects caused by congestion control mechanisms have to be considered. This thesis addresses both perspectives to develop a holistic concept for exchanging sensor data within a VANET.