A Novel Application of Ensemble Methods with Data Resampling Techniques for Drill Bit Selection in the Oil and Gas Industry

Selection of the most suitable drill bit type is an important task for drillers when planning for new oil and gas wells. With the advancement of intelligent predictive models, the automated selection of drill bit type is possible using earlier drilled offset wells’ data. However, real-field well data samples naturally involve an unequal distribution of data points that results in the formation of a complex imbalance multi-class classification problem during drill bit selection. In this analysis, Ensemble methods, namely Adaboost and Random Forest, have been combined with the data re-sampling techniques to provide a new approach for handling the complex drill bit selection process. Additionally, four popular machine learning techniques namely, K-nearest neighbors, naïve Bayes, multilayer perceptron, and support vector machine, are also evaluated to understand the performance degrading effects of imbalanced drilling data obtained from Norwegian wells. The comparison of results shows that the random forest with bootstrap class weighting technique has given the most impressive performance for bit type selection with testing accuracy ranges from 92% to 99%, and G-mean (0.84–0.97) in critical to normal experimental scenarios. This study provides an approach to automate the drill bit selection process over any field, which will minimize human error, time, and drilling cost

Intelligent Drilling of Oil and Gas Wells Using Response Surface Methodology and Artificial Bee Colony

The oil and gas industry plays a vital role in meeting the ever-growing energy demand of the human race needed for its sustainable existence. Newer unconventional wells are drilled for the extraction of hydrocarbons that requires advanced innovations to encounter the challenges associated with the drilling operations. The type of drill bits utilized in any drilling operation has an economical influence on the overall drilling operation. The selection of suitable drill bits is a challenging task for driller while planning for new wells. Usually, when it comes to deciding the drill bit type, generally, the data of previously drilled wells present in similar geological formation are analyzed manually, making it subjective, erroneous, and time consuming. Therefore, the main objective of this study was to propose an automatic data-driven bit type selection method for drilling the target formation based on the Optimum Penetration Rate (ROP). Response Surface Methodology (RSM) and Artificial Bee Colony (ABC) have been utilized to develop a new data-driven modeling approach for the selection of optimum bit type. Data from three nearby Norwegian wells have been utilized for the testing of the proposed approach. RSM has been implemented to generate the objective function for ROP due to its strong data-fitting characteristic, while ABC has been utilized to locate the global optimal value of ROP. The proposed model has been generated with a 95% confidence level and compared with the existing model of Artificial Neural Network and Genetic Algorithm. The proposed approach can also be applied over any other geological field to automate the drill bit selection, which can minimize human error and drilling cost. The United Nations Development Programme also promotes innovations that are economical for industrial sectors and human sustainability

Investigation of SiO2 Nanoparticle Retention in Flow Channels, Its Remediation Using Surfactants and Relevance of Artificial Intelligence in the Future

In this study, the effect of these variables on commercial silica NP retention was presented in a fabricated flow model considering only the physical adsorption aspects of silica NP retention. From our observations, it was established that while silica NP concentration, flow rate and salt are key variables in influencing silica NP agglomeration and retention, the effect of temperature was highly subdued. The effect of salt-induced agglomeration was particularly severe at moderate salinity (≈4 wt% NaCl). To mitigate the effect of salt-induced agglomeration, a commonly used anionic surfactant, sodium dodecyl sulfate (SDS) was added to the solution and the silica NP retention was tabulated. An amount of 0.3 wt% SDS was found to negate salt-induced agglomeration significantly, paving the way for use of silica NP solutions, even in the presence of saline conditions. A section on the prospective use of artificial intelligence for this purpose has been included. This study is useful for understanding NP retention behaviour, especially in the presence of salinity and its mitigation using surfactants, in flow applications

Determination of Point-to-Point 3D Routing Algorithm Using LiDAR Data for Noise Prediction

Urban planning, noise propagation modelling, viewshed analysis, etc., require determination of routes or supply lines for propagation. A point-to-point routing algorithm is required to determine the best routes for the propagation of noise levels from source to destination. Various optimization algorithms are present in the literature to determine the shortest route, e.g., Dijkstra, Ant-Colony algorithms, etc. However, these algorithms primarily work over 2D maps and multiple routes. The shortest route determination in 3D from unlabeled data (e.g., precise LiDAR terrain point cloud) is very challenging. The prediction of noise data for a place necessitates extraction of all possible principal routes between every source of noise and its destination, e.g., direct route, the route over the top of the building (or obstruction), routes around the sides of the building, and the reflected routes. It is thus required to develop an algorithm that will determine all the possible routes for propagation, using LiDAR data. The algorithm uses the novel cutting plane technique customized to work with LiDAR data to extract all the principal routes between every pair of noise source and destination. Terrain parameters are determined from routes for modeling. The terrain parameters, and noise data when integrated with a sophisticated noise model give an accurate prediction of noise for a place. The novel point-to-point routing algorithm is developed using LiDAR data of the RGIPT campus. All the shortest routes were tested for their spatial accuracy and efficacy to predict the noise levels accurately. Various routes are found to be accurate within ±9 cm, while predicted noise levels are found to be accurate within ±6 dBA at an instantaneous scale. The novel accurate 3D routing algorithm can improve the other urban applications too

GIS Mapping of Short-Term Noisy Event of Diwali Night in Lucknow City

Noise is a universal problem that is particularly prominent in developing nations like India. Short-term noise-sensitive events like New Year’s Eve, derby matches, DJ night, Diwali night (celebration with firecracker) in India, etc. create lots of noise in a short period. There is a need to come up with a system that can predict the noise level for an area for a short period indicating its detailed variations. GIS (Geographic Information System)-based google maps for terrain data and crowd-sourced or indirect collection of noise data can overcome this challenge to a great extent. Authors have tried to map the highly noisy Diwali night for Lucknow, a northern city of India. The mapping was done by collecting the data from 100 points using the noise capture app (30% were close to the source and 70% were away from the source (receiver). Noise data were predicted for 750 data points using the modeling interpolation technique. A noise map is generated for this Diwali night using the crowd-sourcing technique for Diwali night. The results were also varied with 50 test points and are found to be within ±4.4 dB. Further, a noise map is also developed for the same site using indirect data of noise produced from the air pollution open-sourced data. The produced noise map is also verified with 50 test points and found to be ±6.2 dB. The results are also corroborated with the health assessment survey report of the residents of nearby areas

GIS Mapping of Short-Term Noisy Event of Diwali Night in Lucknow City

Noise is a universal problem that is particularly prominent in developing nations like India. Short-term noise-sensitive events like New Year’s Eve, derby matches, DJ night, Diwali night (celebration with firecracker) in India, etc. create lots of noise in a short period. There is a need to come up with a system that can predict the noise level for an area for a short period indicating its detailed variations. GIS (Geographic Information System)-based google maps for terrain data and crowd-sourced or indirect collection of noise data can overcome this challenge to a great extent. Authors have tried to map the highly noisy Diwali night for Lucknow, a northern city of India. The mapping was done by collecting the data from 100 points using the noise capture app (30% were close to the source and 70% were away from the source (receiver). Noise data were predicted for 750 data points using the modeling interpolation technique. A noise map is generated for this Diwali night using the crowd-sourcing technique for Diwali night. The results were also varied with 50 test points and are found to be within ±4.4 dB. Further, a noise map is also developed for the same site using indirect data of noise produced from the air pollution open-sourced data. The produced noise map is also verified with 50 test points and found to be ±6.2 dB. The results are also corroborated with the health assessment survey report of the residents of nearby areas

The Review of Carbon Capture-Storage Technologies and Developing Fuel Cells for Enhancing Utilization

The amount of CO2 released in the atmosphere has been at a continuous surge in the last decade, and in order to protect the environment from global warming, it is necessary to employ techniques like carbon capture. Developing technologies like Carbon Capture Utilization and Storage aims at mitigating the CO2 content from the air we breathe and has garnered immense research attention. In this review, the authors have aimed to discuss the various technologies that are being used to capture the CO2 from the atmosphere, store it and further utilize it. For utilization, researchers have developed alternatives to make profits from CO2 by converting it into an asset. The development of newer fuel cells that consume CO2 in exchange for electrical power to drive the industries and produce valuable hydrocarbons in the form of fuel has paved the path for more research in the field of carbon utilization. The primary focus on the article is to inspect the environmental and economic feasibility of novel technologies such as fuel cells, different electrochemical processes, and the integration of artificial intelligence and data science in them, which are designed for mitigating the percentage of CO2 in the air

Recent Advancements in AI-Enabled Smart Electronics Packaging for Structural Health Monitoring

Real-time health monitoring of civil infrastructures is performed to maintain their structural integrity, sustainability, and serviceability for a longer time. With smart electronics and packaging technology, large amounts of complex monitoring data are generated, requiring sophisticated artificial intelligence (AI) techniques for their processing. With the advancement of technology, more complex AI models have been applied, from simple models to sophisticated deep learning (DL) models, for structural health monitoring (SHM). In this article, a comprehensive review is performed, primarily on the applications of AI models for SHM to maintain the sustainability of diverse civil infrastructures. Three smart data capturing methods of SHM, namely, camera-based, smartphone-based, and unmanned aerial vehicle (UAV)-based methods, are also discussed, having made the utilization of intelligent paradigms easier. UAV is found to be the most promising smart data acquisition technology, whereas convolution neural networks are the most impressive DL model reported for SHM. Furthermore, current challenges and future perspectives of AI-based SHM systems are also described separately. Moreover, the Internet of Things (IoT) and smart city concepts are explained to elaborate on the contributions of intelligent SHM systems. The integration of SHM with IoT and cloud-based computing is leading us towards the evolution of future smart cities

Review of Structural Health Monitoring Techniques in Pipeline and Wind Turbine Industries

There has been enormous growth in the energy sector in the new millennium, and it has enhanced energy demand, creating an exponential rise in the capital investment in the energy industry in the last few years. Regular monitoring of the health of industrial equipment is necessary, and thus, the concept of structural health monitoring (SHM) comes into play. In this paper, the purpose is to highlight the importance of SHM systems and various techniques primarily used in pipelining industries. There have been several advancements in SHM systems over the years such as Point OFS (optical fiber sensor) for Corrosion, Distributed OFS for physical and chemical sensing, etc. However, these advanced SHM technologies are at their nascent stages of development, and thus, there are several challenges that exist in the industries. The techniques based on acoustic, UAVs (Unmanned Aerial Vehicles), etc. bring in various challenges, as it becomes daunting to monitor the deformations from both sides by employing only one technique. In order to determine the damages well in advance, it is necessary that the sensor is positioned inside the pipes and gives the operators enough time to carry out the troubleshooting. However, the mentioned technologies have been unable to indicate the errors, and thus, there is the requirement for a newer technology to be developed. The purpose of this review manuscript is to enlighten the readers about the importance of structural health monitoring in pipeline and wind turbine industries