Maximum Penetration Rate Research Articles

User-needs-oriented shared DC charging resources optimal configuration and operation for improving EV penetration in old residential communities

The charging limitation of electric vehicles (EVs) in old residential communities (ORCs) derived from insufficient electricity capacity and parking spaces significantly impacts residents' quality of life and impedes urban sustainability. Considering economic and social factors, ORCs are likely to persist worldwide for the foreseeable future. To tackle these challenges, this paper proposes the scheme of shared DC fast charging stations to address the EV charging issues in ORCs. Specific methods involve minimizing the charging station spaces and charging infrastructure investment, while optimizing the photovoltaic (PV) and energy storage capacity. Additionally, a coordinated charging strategy based on dynamic price is designed to guide users in choosing the initial charging time, enhancing EV charging infrastructure utilization and EV penetration. This work is predominantly driven by human needs, integrating surveys on human behavior related to charging station locations and charging times. This case study is based on a real-life ORC in Shenzhen, China. By utilizing the proposed dynamic-pricing coordinated charging strategy tailored to the station capacity, the maximum EV penetration rate is to be achieved with satisfying EV charging demand. Furthermore, the implementation of dynamic pricing effectively guides users to adjust charging behavior, achieving the peak-shaving and valley-filling performance of load curves.

Corrosion at Top-of-the-Line in High Pressure and Dense CO2 Environments

This study presents unique data on top-of-the-line corrosion (TLC) occurring in high-pressure environments where CO2 was in the gaseous, liquid, or supercritical state. While CO2 is traditionally in a gaseous phase, this form of degradation is referred to as TLC. In this study, similar phenomena with different mechanisms were observed in liquid CO2 and supercritical states all of which are referred to as TLC due to the location of specimens and ease of comprehension. Experiments were conducted to investigate the effect of CO2 partial pressure (ranging from 20 bar to 100 bar) with temperatures (30°C to 50°C) relating to different water condensation rates (0.001 mL/m2/s to 0.1 mL/m2/s). Uniform and localized TLC rates increased with a higher water condensation rate and surface temperature. As long as CO2 remained gaseous, its partial pressure (pCO2) showed a negligible influence on both uniform and localized TLC rates. At the highest gaseous CO2 content tested, the formation of a protective iron carbonate (FeCO3) layer decreased the TLC rate, with this effect being more pronounced at lower water condensation rates. The risk of localized corrosion for specimens exposed to this environment at high and medium water condensation rates remained an issue. In the dense phase CO2 environment, the difference in temperature between the bulk environment and the specimen’s surface caused a similar phenomenon to water condensation, termed water drop-out, which resulted in corrosion. The rate of water drop-out could not be measured experimentally or estimated theoretically but is a complex function of temperature, pCO2, and CO2 physical state. The interplay between high pCO2 and low pH of the dropped-out water led to elevated uniform and localized corrosion rates. The depth of localized corrosion, at the high and medium water drop-out conditions, reached its maximum at the surface temperature of ca. 45°C. At a lower surface temperature of ca. 25°C and a higher surface temperature of ca. 65°C, the maximum penetration rate was decreased due to slower kinetics of reactions and the formation of a more protective FeCO3 layer, respectively. The results presented in this study highlight the significant difference between corrosion rates, especially in the form of localized damage, in gaseous and dense-phase CO2 environments.

Real-time rate of penetration prediction for motorized bottom hole assembly using machine learning methods

Drilling rate of penetration (ROP) is one of the most important factors that have their significant effect on the drilling operation economically and efficiently. Motorized bottom hole assembly (BHA) has different applications that are not limited to achieve the required directional work but also it could be used for drilling optimization to enhance the ROP and mitigate the downhole vibration. Previous work has been done to predict ROP for rotary BHA and for rotary steerable system BHA; however, limited studies considered to predict the ROP for motorized BHA. In the present study, two artificial intelligence techniques were applied including artificial neural network and adaptive neurofuzzy inference system for ROP prediction for motorized assembly in the rotary mode based on surface drilling parameters, motor downhole output parameters besides mud parameters. This new robust model was trained and tested to accurately predict the ROP with more than 5800 data set with a 70/30 data ratio for training and testing respectively. The accuracy of developed models was evaluated in terms of average absolute percentage error, root mean square error, and correlation coefficient (R). The obtained results confirmed that both models were capable of predicting the motorized BHA ROP on Real-time. Based on the proposed model, the drilling parameters could be optimized to achieve maximum motorized BHA ROP. Achieving maximum ROP will help to reduce the overall drilling cost and as well minimize the open hole exposure time. The proposed model could be considered as a robust tool for evaluating the motorized BHA performance against the different BHA driving mechanisms which have their well-established models.

Determination of the optimal connected and automated vehicles platoon size based on the merging success rate

ABSTRACT The platoon size is a critical parameter in connected and automated vehicle (CAV) platoon configuration. However, the optimal platoon size configuration for the mixed human-driven vehicles (HVs) and CAVs traffic has not been well-studied, especially in merging areas. This paper aims to determine the optimal platoon size in the merge area through numerical analysis. Specifically, the existing merge probability calculation model is improved considering the impact of mainline random platoon size on on-ramp vehicles. In the improved model, the CAV platoon is treated as a vehicle with different lengths, and the headway between the platoon and single vehicle and the headway between the platoons are related to the maximum platoon size and CAV penetration rate. Three key parameters, namely the CAV penetration rate, acceleration lane length, and mainline traffic volume, were combined at various values. Based on this input, the relationship between the success rate and the platoon size was analyzed. The numerical analysis results indicate that: (1) when the CAV penetration rate is 10% ~30%, the merge success rate increases and stabilizes as the platoon size increases, and the optimal platoon size is 4 ~ 6. (2) When the CAV penetration rate is 40% ~70%, the trend of merge success rate varies with increasing platoon size under different mainline traffic volumes and acceleration lane lengths. Under the situation with lower traffic and longer acceleration length, the merge success rate tends to decline easier with larger platoon sizes, with 3 ~ 8 being the optimal platoon size. (3) When the CAV penetration rate is 80% ~90%, as the platoon size increases, the merging success rate tends to increase to the highest point and decrease. The optimal platoon size is 3 ~ 5. Finally, the validity of the theoretical model is confirmed through simulation experiments, and its limitations are discussed.

An explainable ensemble machine learning model to elucidate the influential drilling parameters based on rate of penetration prediction

Many researchers have examined the benefits of machine learning (ML) algorithms in geothermal drilling, especially for predicting the rate of penetration (ROP) of drilling. However, little attention has been given to enhancing the explainability of ML algorithms. Motivated by this, this study examines the feasibility of an explainable ML approach for elucidating the behavior of the drilling parameters to generate explainable insights regarding the surface-controllable drilling parameters that could maximize the ROP for drilling effectiveness (DE). First, this study presents a performance analysis using hyper-parameter technique of the ensemble supervised machine learning (ESML) for ROP predictions. Secondly, this study uses an explainable ML method to explain the drilling parameters' behavior based on the ESML prediction. The results show that the ensemble CatBoost model shows the best performance for ROP prediction. Then, the explainable ML successfully revealed several influential drilling parameters that could maximize the ROP for DE. In earlier drilling (0–4,000 m in depth), temperature and pressure contribute to a lower ROP, and the drilling fluid flow rate and rotary speed contribute to a higher ROP. On the other hand, as the drills go deeper into the earth (4,000–7,536 m in depth), maximum ROP can be obtained by maximizing the drilling pressure, weight on bit (WOB), surface torque, and rotary speed (RPM) of the drill bit, while drilling fluid flow rate does not significantly contribute to ROP. The explainable ML also highlighted the drilling parameters' inflection points when a switch from low to high rates of ROP occurs. Overall, the findings show the proposed model's strength in generating explainable insights that can improve the selection of appropriate ROP settings and support future decision-making processes in geothermal drilling.

Development of annular pump to enhance drilling distance and cuttings return in horizontal directional drilling

In many shallow horizontal directional drilling (HDD) projects, the annulus fluid pressure soon reaches the blowout or frac-out pressure of the soil formation. The pressure at single-source pump therefore must be limited. This limits the maximum drilling distance (borehole length), maximum attainable rate of penetration (ROP), and maximum distance over which cuttings can be returned. A novel concept involving a series of annular pumps distributed along the borehole length to aid in generation of distributed annulus pressure boost and cuttings transport in HDD was conceived. The success of the above concept, designated as distributed pumping system (DPS), relies on the ability to design and power an annular pump that can fit in the constricted annulus geometry of HDD boreholes while satisfying the performance requirements. We present the laboratory-scale development of three annular pumps based on the turbomachinery design principle of axial blade propellers. The pump prototypes were 3D printed and tested in the laboratory test rig flow loop built to simulate the annular flow configuration of HDD boreholes. The annular pump was powered using the axial propeller turbine housed inside the hub of each annular pump. The turbine converts hydraulic energy from the inner flow (analogous to drilling fluid flow within drill rod) to power the integrated setup of the axial inner turbine and annular pump, in short, designated as integrated turbine-pump (ITP). Results from the tests on three preliminary prototypes of the annular pump using water as the medium in the test rig yielded a maximum pressure boost of 44 kPa–enough to space the annular pumps 80 m apart when designed for a typical annular pressure drop rate of 0.54 kPa/m. The performance index (i) of the ITP, defined as the ratio of pressure boost provided by the annular pump in the outer (annular) flow to the pressure drop created by an inner turbine in the inner flow, reached a maximum value of 0.6. The results from the study promise a technology that can enable long-distance HDD cuttings return without the risk of blowout or the need for an external power source.

Design and performance of the Hotrod melt-tip ice-drilling system

Abstract. We introduce the design and performance of an electrothermal ice-drilling system designed to insert a temperature sensor cable into ice. The melt tip is relatively simple and low-cost, designed for a one-way trip to the ice–bed interface. The drilling system consists of a melt tip, umbilical cable, winch, interface, power supply, and support items. The melt tip and the winch are the most novel elements of the drilling system, and we make the hardware and electrical designs of these components available open-access. Tests conducted in a laboratory indicate that the melt tip has an electrical energy to forward melting heat transfer efficiency of ∼35 % with a theoretical maximum penetration rate of ∼12 m h−1at maximum 6.0 kW power. In contrast, ice-sheet testing suggests the melt tip has an analogous heat transfer efficiency of ∼15 % with a theoretical maximum penetration rate of ∼6 m h−1. We expect the efficiency gap between laboratory and field performance to decrease with increasing operator experience. Umbilical freeze-in due to borehole refreezing is the primary depth-limiting factor of the drilling system. Enthalpy-based borehole refreezing assessments predict refreezing below critical umbilical diameter in ∼4 h at −20 ∘C ice temperatures and ∼20 h at −2 ∘C. This corresponds to a theoretical depth limit of up to ∼200 m, depending on firn thickness, ice temperature, and operator experience.

Drilling operation optimization using machine learning framework

Optimizing the surface parameters to maximize the rate of penetration (ROP) could be achieved through a machine learning (ML) framework. Hence, predicting ROP is a crucial factor in any optimization problem. A defined range of surface parameters will provide the maximum ROP for a given bottom hole assembly (BHA) while drilling a particular formation lithology. This maximization of ROP value is valid for a specific range of certain combinations of surface parameters and subsurface equipment. However, tuning these surface parameters is an art rather than a science. Despite the abundance of offset wells data, local experience and trial-and-error methods are the current keys for this tuning. This work used a large drilling dataset (460,076 records) to develop two artificial neural network (ANN) models. The first ANN model is used to predict standpipe pressure (SPP) and the apparent surface torque for the drilling system. In addition to other inputs, these two parameters are used in the second ANN model to predict the ROP. The first ANN achieved 92.39% and 93.71% accuracy in predicting the apparent torque and standpipe pressure values respectively; while the second ANN achieved 87.63% accuracy when tested against 39,294 records that were not used in training. To apply the system in real applications, the ROP is predicted, and a genetic algorithm (GA) is used to optimize both the surface parameters and the downhole equipment while drilling a section of the hole. This system or framework (ANN models coupled with the GA algorithm) was used in a field trial in one of the Egyptian Western Desert routine wells. The framework was used in two sections within the 8 ½” hole and enhanced these sections' ROP by 15.05% and 14.41% compared to similar sections in different offset wells. It resulted in an overall best-in-class (BIC) well performance compared to similar wells in the field.

Investigation of carbon steel corrosion rate in different acidic environments

This study focused on evaluating the rate of carbon steel corrosion in one molarity of HNO3 H2SO4 and HCl in various acidic conditions. The twenty-day exposure of carbon steel specimens to acidic conditions was investigated and followed by the evaluation of corrosion penetration rate and mils per year. The results showed that the 1 M HNO3 solution was the most damaging to carbon steel due to its oxidizing character. Among the tested acidic media HNO3 was the most harmful followed by H2SO4 and HCl. Carbon steel metal's corrosion behaviour was affected by acid medium strength and exposure duration. Dissolving of carbon steel increased with increasing corrosive media severity and exposure time. Concept of passive behaviour in metals has been proposed as the primary reason for this effect. The corrosion penetration rate values for carbon steel used in the present investigation followed the order of HNO3 > H2SO4 > HCl when exposed to the three acidic environments. The maximum corrosion penetration rate values for carbon steel metal subjected to HCl H2SO4 and HNO3 were determined to be 0.008952 (2nd day) 0.03095 (1st day) and 0.039373 (1st day) respectively. In addition corrosion penetration rate values steadily dropped throughout the final days of immersion eventually becoming essentially constant in all acidic media. As a result of the greater impact of all the acidic media used in this investigation corrosion pitting occur on carbon steel consistently. By intensifying the acid medium these effects were exacerbated.

Role of Temperature Gradient and Soil Thermal Properties on Frost Heave

In cold regions, the soil temperature gradient and depth of frost penetration can significantly affect roadway performance because of frost heave and thaw settlement of the subgrade soils. The severity of the damage depends on the soil index properties, temperature, and availability of water. While nominal expansion occurs with the phase change from pore water to ice, heaving is derived primarily from a continuous flow of water from the vadose zone to growing ice lenses. The temperature gradient within the soil influences water migration toward the freezing front, where ice nucleates, coalesces into lenses, and grows. This study evaluates the frost heave potential of frost-susceptible soils from Iowa (IA-PC) and North Carolina (NC-BO) under different temperature gradients. One-dimensional frost heave tests were conducted with a free water supply under three different temperature gradients of 0.26°C/cm, 0.52°C/cm, and 0.78°C/cm. Time-dependent measurements of frost penetration, water intake, and frost heave were carried out. Results of the study suggested that frost heave and water intake are functions of the temperature gradient within the soil. A lower temperature gradient of 0.26°C/cm leads to the maximum total heave of 18.28 mm (IA-PC) and 38.27 mm (NC-BO) for extended periods of freezing. The maximum frost penetration rate of 16.47 mm/hour was observed for a higher temperature gradient of 0.78°C/cm and soil with higher thermal diffusivity of 0.684 mm2/s. The results of this study can be used to validate numerical models and develop engineered solutions that prevent frost damage.

Machine Learning Application in Enhancing Drilling Performance

Drilling Oil and Gas wells is an expensive operation, where the cost for drilling a single shallow offshore HP-UHT well may exceed 30 million USD easily. Due to the huge cost involved, companies are eager to accomplish the drilling operations in a minimal time frame, by increasing the drilling rate or rate of penetration (ROP) and reducing the downhole tool failures. The Mechanical Specific Energy (MSE) concept addresses these two drilling performance measures in totality as it provides maximum ROP which can be achieved with the existing drilling system (i.e., Drill Bits, Drilling Tubular, Well Profile, and Mud weight) without causing downhole tool failures. ROP is a function of Weight on Bit, Rotation per Minute, and Flow rate for an existing drilling system, optimization of these parameters results in increased ROP, whereas unharmonious values lead to shock and vibration in drilling assembly. Machine Learning algorithms facilitate this optimization of drilling parameters for enhancing drilling performance, by the development of an accurate ROP prediction and optimization model. The ROP prediction model was developed using Artificial Neural Network and Random Forest, and the optimization model was developed using evolutionary optimization algorithms like particle swarm optimization and genetic algorithms. The result of the optimization model depends upon the accuracy of the ROP prediction model and the upper and lower limits of drilling parameters defined in the optimization model. Machine learning applications in improving the Drilling Performance can be easily quantified in terms of saving days, where one day of operational cost for the company is approximately 0.2 million USD per Offshore – HP-UHT well.

Irradiance penetration distribution and flashing light frequency simultaneously affected with microalgal cell absorption and CO2 bubble scattering in a raceway pond

In order to investigate light penetration and flashing light frequency for microalgal cell-CO2 bubble culture system in a raceway pond, user-defined function for CO2 mass transfer and bubble scattering models coupled with discrete ordinates radiation model were adopted to clarify simultaneous effects of microalgal cell absorption and CO2 bubble scattering. Light intensity along the microalgal suspension depth attenuated more rapidly with increased biomass concentration, decreased bubble generation diameter, increased CO2 gas content and incident light intensity. Ratio of light zone decreased from 81.13 % to 20.00 % when biomass concentration increased from 0 to 0.4 g/L because of light absorption and shading effects of microalgae. When bubble generation diameter increased from 0.1 to 1.6 mm, ratio of light zone increased from 37.95 % to 42.64 %, while microalgal flashing light cycle first decreased to a valley of 1.81 s at 0.8 mm and then increased. Local light intensity in the upper layers was more enhanced due to lots of CO2 bubbles gathering and reflecting more light with decreased bubble diameter and increased gas content. Light attenuated more rapidly in microalgal suspension with decreased bubble generation diameter and increased CO2 gas content because of increased bubble diffraction coefficient and contact area. When initial CO2 volume fraction increased from 0.02 to 0.2, flashing light frequency of microalgal cells decreased from 0.55 to 0.29 Hz and light zone time ratio φ decreased from 36.90 % to 18.40 %. At a biomass concentration of 0.1 g/L and a bubble flow rate of 0.1 m/s, the maximum light penetration and microalgal growth rate was achieved when bubble diameter, incident light intensity and gas content were optimally at 0.8 mm, 200 W/m2 and 0.02, respectively. This work provides data support and theoretical guidance for photobioreactor design and optimization of light energy utilization.

Predicting Rate of Penetration and optimization Weight on bit using Artificial Neural Networks

Obtaining the maximum Rate of Penetration (ROP) by optimization drilling parameters is the aim of every drilling engineer. This is because it could save time, reduce cost and minimize drilling problems. However, ROP depends on a lot of parameters which lead to difficulties in its prediction. Therefore, it is necessary and important to investigate a solution predicting ROP with high accuracy to determine the suitable drilling parameters. In this study, a new approach using Artificial Neural Network (ANN) has been proposed to predict ROP from real – time drilling data of several wells in Nam Rong - Doi Moi field with more than 900 datasets included important parameters such as the weight on bit (WOB), weight of mud (MW), rotary speed (RPM), standpipe pressure (SPP), flow rate (FR), torque (TQ). In the process of training the network, algorithms and the number of neurons in the hidden layer were varied to find the optimal model. The ANN model shows high accuracy when compared to actual ROP, therefore it can be recommended as an effective and suitable method to predict the ROP of other wells in the research area. Besides, base on the proposed ANN model, authors carried out experiments and determind the optimal weight on bit value for the drilling interval from 1800 to 2300 m of wells in Nam Rong Doi Moi field

Potential of Utilizing Thermal Energy Storage Integrated Ground Source Heat Pump System to Reshape Electricity Demand in the United States

Abstract Each year, more than 20% of electricity generated in the United States is consumed for meeting the thermal demands (e.g., space cooling, space heating, and water heating) in residential and commercial buildings. Integrating thermal energy storage (TES) with building’s HVAC systems has the potential to reshape the electric load profile of the building and mitigate the mismatch between the renewable generation and the demand of buildings. A novel ground source heat pump (GSHP) system integrated with underground thermal energy storage (UTES) has been proposed to level the electric demand of buildings while still satisfying their thermal demands. This study assessed the potential impacts of the proposed system with a bottom-up approach. The impacts on the electricity demand in various electricity markets were quantified. The results show that, within the capacity of the existing electric grids, the maximum penetration rate of the proposed system in different wholesale markets could range from 51% to 100%. Overall, about 46 million single-family detached houses can be retrofitted into the proposed system without increasing the annual peak demand of the corresponding markets. By implementing the proposed system at its maximum penetration rate, the grid-level summer peak demand can be reduced by 9.1% to 18.2%. Meanwhile, at the grid level, the annual electricity consumption would change by −12% to 2%. The nationwide total electricity consumption would be reduced by 9%.

Use of Artificial Neural Networks in predicting rate of penetration and optimization weight on bit for several wells in Nam Rong - Doi Moi field, Vietnam

Obtaining the maximum Rate of Penetration (ROP) by optimization of drilling parameters is the aim of every drilling engineer. This helps to save time, reduces cost and minimizes drilling problems. Since ROP depends on a lot of parameters, it is very difficult to predict it correctly. Therefore, it is necessary and important to investigate a solution for predicting ROP with high accuracy in order to determine the suitable drilling parameters. In this study, a new approach using Artificial Neural Network (ANN) has been proposed to predict ROP from real - time drilling data of several wells in Nam Rong - Doi Moi field with more than 900 datasets included important parameters such as weight on bit (WOB), weight of mud (MW), rotary speed (RPM), stand pipe pressure (SPP), flow rate (FR), torque (TQ). In the process of training the network, algorithms and the number of neurons in the hidden layer were varied to find the optimal model. The ANN model shows high accuracy when comparing to actual ROP, therefore it can be recommended as an effective and suitable method to predict ROP of other wells in research area. Besides, base on the proposed ANN model, authors carried out experiments and determine the optimal weight on bit value for the drilling interval from 1800 to 2300 m of wells in in Nam Rong Doi Moi field.

Three phase corrosion of pipeline steel: Size effects of deposited solids under water droplets and an oil diffusion barrier

Existing studies have examined either under deposit corrosion in bulk solutions or underoil droplet corrosion without deposits. Yet, neither of these scenarios represents the conditions existing within a diluted bitumen pipeline where underoil droplet corrosion occurs in tandem with under deposit corrosion. Herein, this industrially important 3-phase system – solid deposit, aqueous droplet, oil cover – is studied for the first time. In particular, the relationship between varying deposit particle size and underoil droplet corrosion is established. Scanning electron microscopy coupled with profilometry was used to monitor droplet corrosion over time for API-X100 pipeline steel covered with different sized silica particles underneath simulated diluted bitumen. A zero resistance ammeter was used to validate the observed corrosion from the immersion experiments. The data indicate that localized corrosion coupled with uniform corrosion occurred beneath the large (80% of the cumulative particle size distribution less than 540 µm) and small (80% of the cumulative particle size distribution less than 43 µm) silica covered underoil droplets on API-X100 pipeline steels after 24 h of exposure. The underoil droplet corrosion of silica covered steel has maximum localized penetration predominantly around the silica pile perimeter. With continued exposure, up to 240 h, the corrosion beneath the two sizes of silica continues to develop with a difference in rate. The maximum penetration rate (13.58±0.76 mm/year) of localized corrosion under the large silica particles was higher than that (10.02±0.87 mm/year) of the localized corrosion beneath the small silica particles.

Improving drilling performance through optimizing controllable drilling parameters

The prediction of the drilling rate of penetration (ROP) is one of the key aspects of drilling optimization due to its significant role in reducing expensive drilling costs. Many variables could affect ROP, which can be classified into two general categories; controllable operational variables and uncontrollable or environmental variables. Minimizing the drilling cost can be achieved through optimizing the controllable drilling parameters. As a direct result, the drilling speed will be increased while maintaining safe practices. The primary purpose of this study is to address the simultaneous impact of controllable parameters such as weight on bit (WOB), revolutions per minute, and flow rate (FR) on the rate of penetration (ROP). Response surface methodology was applied to develop a mathematical relation between operational controllable drilling parameters and ROP. To accomplish this, actual field datasets from several wells drilled in Southern Iraq in different fields were used. The second purpose of this study was to identify all prospective optimal ranges of these controllable parameters to obtain superior drilling performance with an optimum ROP. The obtained results showed that the developed model offers a cost-effective tool for determining the maximum ROP as a function of controllable parameters with reasonable accuracy. In addition, the proposed model was used to estimate optimal combinations of controllable drilling parameters for various depths. The results have shown that FR has the most significant effect on ROP variation with a sum of squares values of 23.47. Applying high WOB does not permanently improve ROP but could result in reducing ROP for some cases. The developed mechanical specific energy model for polycrystalline diamond compact (PDC) bit with vertical and deviated wells can estimate combinations of controllable drilling parameters. The developed model can be successfully applied to predict and optimize the drilling rate when using PDC bits, hence reducing the drilling time and the associated drilling cost for future wells.

Research on Weight-on-bit Self-adjusting Dual-diameter PDC Bit Design and Effect Evaluation Utilizing Stress-Releasing Effect of Rock at Bottomhole

Rock-breaking efficiency is not so satisfied using the conventional PDC bit, especially in the hard, soft–hard interbedded, and strong abrasive formations in ultra-deep wells. A method is proposed to make full use of stress-releasing effect at bottomhole, thereby improving the rock-breaking efficiency, and a supporting weight-on-bit (WOB) self-adjusting dual-diameter PDC bit (WSDB) has been invented. The WSDB can dynamically adjust the depth-of-cut control characteristics, which could improve the rate of penetration (ROP) while mitigating the stick–slip vibration caused by the rapid change of depth of cut. The stress field of the rock at the bottomhole is calculated with a numerical method, and the working mechanism of the bit is analyzed. The application effect of the WSDB is evaluated through laboratory tests preliminarily. The results show that the stress concentration area on the shoulder of the reaming bit is decreased obviously and a larger stress unloading area emerges in the intersection between reaming and pilot bit. Thus, mechanical specific energy (MSE) can be greatly reduced when the reaming bit drills into this section. Indoor tests show that the maximum ROP of WSDB can be reached up to 70% higher compared to the conventional PDC bit, and higher ROP could be obtained with a relatively lower bit torque. With drilling parameters greater than the critical revolution and WOB, the reduction in MSE can be reached up to 36% during the rock-breaking process. Making further efforts into the optimization of bit structure to reduce the vibration intensity is expected to achieve more improved bit performance.

Expansion Planning Method of Offshore Multiplatform Power System With Wind Power Considering Cable Size Selection

With the expansion of the scale of subsea oil and gas resource exploitation, the offshore multiplatform interconnected power systems have become the main form of offshore platform group energy supply. The offshore multiplatform interconnected power system with wind power can effectively reduce the system fuel consumption and carbon emission, which has become an important direction for the development of future offshore multiplatform interconnected power systems. In this paper, the scenario method is adopted to portray the wind power output. System evaluation indicators considering the economy, reliability and environmental benefits of the system are proposed. Considering the nonnegligible impact of the cable size selection problem on the system economic cost, this paper introduces cable size selection variables in the expansion planning model of the offshore multiplatform interconnected power system with wind power to achieve accurate optimization of the system planning scheme but also greatly increases the difficulty of solving the model. A model linearization method is applied to linearize the nonlinear part of the model, so that the optimized model is a mixed-integer linear programming model under the determined system topology, and thus can be solved using commercial planning software. In addition, an improved genetic algorithm is proposed to optimize the system topology, which improves the efficiency of the model solution. The cases of different scale verify the validity of the model and the solution algorithm and show the impact of the maximum penetration rate of wind power on the total system cost.

Experimental investigations and statistical modelling of dilution rate and area of penetration in submerged arc welding of SS316-L

Abstract Submerged arc welding (SAW) is a common welding process which is used in industries such as structural and vessel construction. The advantage of SAW process is, pure weld bead as the weld arc is completely submerged in flux, high deposition rate, and low defect rates and great penetration. It has been reported in prior literatures that it is difficult to control SAW parameters for accurate welding bead. Therefore, in the present work, the attempt has been made to study the effect of various SAW parameters such as welding current, arc voltage and travel speed on area of penetration and dilution rate of weld bead. The experiments were performed on SS 316-L steel alloy. The statistical models were developed to predict the dilution rate and area of penetration in SAW process. The central composite design (CCD) of Response surface methodology (RSM) was used to design the set of experiments. Analysis of variance (ANOVA) was carried out at a confidence interval of 95% to determine the parameters having the significant influence (p less than 0.05) on the responses. The developed models were validated using three confirmatory experiments, and Multi objective genetic algorithm (GA) was used to find the optimum parameters for dilution rate and area of penetration. It was observed that the dilution rate and area of penetration increased with increasing welding current and arc voltage. However, both decreased with increasing the travel speed. The maximum area of penetration was obtained at maximum voltage and minimum travel speed. Optimization revealed that the maximum dilution rate and area of penetration were observed at welding current with 334.068 A, arc voltage 26.70 V and travel speed 13.29 m/hr.