Machine learning algorithm for prediction of stuck pipe incidents using statistical data: case study in middle east oil fields

One of the most troublesome issues in the drilling industry is stuck drill pipes. Drilling activities will be costly and time-consuming due to stuck pipe issues. As a result, predicting a stuck pipe can be more useful. This study aims to use an artificial intelligence technology called hybrid particle swarm optimization neural network (PSO-based ANN) to predict the probability of a stuck pipe in a Middle East oil field. In this field, a total of 85 wells were investigated. Therefore, to predict this problem, we must examine and determine the role of drilling parameters by creating an appropriate model. In this case, an artificial neural network is used to solve and model the problem. In this way, by processing the parameters of wells with and without being stuck in this field, the stuck or non-stuck of drilling pipes in future wells is predicted. To create a PSO-based ANN model database, mud characteristics, geometry, hydraulic, and drilling parameters were gathered from well daily drilling reports. In addition, two databases for directional and vertical wells were established. There are two types of datasets used for each database: stuck and non-stuck. It was discovered that the PSO-based ANN model could predict the incidence of a stuck pipe with an accuracy of over 80% for both directional and vertical wells. This study divided data from several cases into four sections: 17 ½″, 12 ¼″, 8 ½″, and 6 1/8″. The key reasons for sticking and the mechanics have been thoroughly investigated for each section. The methodology presented in this paper enables the Middle East drilling industry to estimate the risk of stuck pipe occurrence during the well planning procedure.

3D Model Classification Based on Bayesian Classifier with AdaBoost

With the exponential increasement of 3D models, 3D model classification is crucial to the effective management and retrieval of model database. Feature descriptor has important influence on 3D model classification. Voxel descriptor expresses surface and internal information of 3D model. However, it does not contain topological structure information. Shape distribution descriptor expresses geometry relationship of random points on model surface and has rotation invariance. They can all be used to classify 3D models, but accuracy is low due to insufficient description of 3D model. This paper proposes a 3D model classification algorithm that fuses voxel descriptor and shape distribution descriptor. 3D convolutional neural network (CNN) is used to extract voxel features, and 1D CNN is adopted to extract shape distribution features. AdaBoost algorithm is applied to combine several Bayesian classifiers to get a strong classifier for classifying 3D models. Experiments are conducted on ModelNet10, and results show that accuracy of the proposed method is improved.

Application of BIM for structural engineering: a case study using Revit and customary structural analysis and design software

Building information modelling (BIM) represents a workflow whose application on a construction project will enable all involved players to compile as well as work with information on every aspect of a building in a common model/database. Through BIM, the entire building can be virtually designed and built on a computer. BIM touches every part of a building’s life cycle starting from the design phase well into the construction phase and beyond that into asset management. This research examines the experiences of early adopters of BIM and use that insight to introduce BIM, specially focusing on the structural analysis and design stage of a building. The study demonstrates how all structural design activities can be integrated with each other and how cross-discipline collaboration with the architect can be achieved through the adoption of BIM without leaving ones customary structural design platform. As a demonstration, a sample building is modelled using Revit along with conventional structural software packages ETABS and SAFE. Plugins and applications were developed for these software packages to facilitate interoperability amongst them so that they all act together as a single platform. Modelling, analysis, design and clash detections were facilitated by applying BIM. Major benefits of employing BIM in a structural design project are illustrated through this research.

Research and Implementation of the Sports Analysis System Based on 3D Image Technology

On the basis of existing research, this paper analyzes the algorithms and technologies of 3D image-based sports models in depth and proposes a fusion depth map in view of some of the shortcomings of the current hot spot sports model methods based on 3D images. We use the 3D space to collect the depth image, remove the background from the depth map, recover the 3D motion model from it, and then build the 3D model database. In this paper, based on the characteristics of continuity in space and smoothness in time of a rigid body moving target, a reasonable rigid body target motion hypothesis is proposed, and a three-dimensional motion model of a rigid body target based on the center of rotation of the moving target and corresponding motion is designed to solve the equation with parameters. In the case of unknown motion law, shape, structure, and size of the moving target, this algorithm can achieve accurate measurement of the three-dimensional rigid body motion target’s self-rotation center and related motion parameters. In the process of motion parameter calculation, the least square algorithm is used to process the feature point data, thereby reducing the influence of noise interference on the motion detection result and correctly completing the motion detection task. The paper gives the measurement uncertainty of the stereo vision motion measurement system through simulated and real experiments. We extract the human body motion trajectory according to the depth map and establish a motion trajectory database. For using the recognition algorithm of the sports model based on the 3D image, we input a set of depth map action sequences. After the above process, the 3D motion model is obtained and matched with the model in the 3D motion model database, and the sequence with the smallest distance is calculated. The corresponding motion trajectory is taken as the result of motion capture, and the efficiency of this system is verified through experiments.

Farmer Cards: Model, Database, Accuracy, and Improvement in Goverment Quality Service

This study aims to test the model adoption of farmer card innovation by farmers. Data were collected through the use of the survey technique. The respondents were obtained through cluster sampling from six districts that included the greatest numbers of farmer card users in central Java. Data analysis was done through the SEM technique. As a result, from the five variables included, reconstruction and merging of the independent variables were then carried out so that three important antecedent variables appeared regarding influencing the adoption of farmer cards, namely leadership, facility conditions, and the role of government mediated by perceptions of card technology. The application of farmer cards produced such a farmer database, accuracy improvement, and government service to farmers. Future research needs to be directed toward carrying out development research related to increasing the capacity of information technology regarding farmer cards; hence it will bring better welfare to farmers. No previous research has explained how small farmers adopt information technology provided by the government. Many events have transpired, but these dynamics have not been revealed in previous research. This research resulted in an adoption model that enriches the previous Rogers’ innovation adoption theory, especially how leadership factors play an important role in the adoption of information technology innovations

A Multi-omic Huntington’s Disease Transgenic Sheep-Model Database for Investigating Disease Pathogenesis

Background: The pathological mechanism of cellular dysfunction and death in Huntington’s disease (HD) is not well defined. Our transgenic HD sheep model (OVT73) was generated to investigate these mechanisms and for therapeutic testing. One particular cohort of animals has undergone focused investigation resulting in a large interrelated multi-omic dataset, with statistically significant changes observed comparing OVT73 and control ‘omic’ profiles and reported in literature. Objective: Here we make this dataset publicly available for the advancement of HD pathogenic mechanism discovery. Methods: To enable investigation in a user-friendly format, we integrated seven multi-omic datasets from a cohort of 5-year-old OVT73 (n = 6) and control (n = 6) sheep into a single database utilising the programming language R. It includes high-throughput transcriptomic, metabolomic and proteomic data from blood, brain, and other tissues. Results: We present the ‘multi-omic’ HD sheep database as a queriable web-based platform that can be used by the wider HD research community (https://hdsheep.cer.auckland.ac.nz/). The database is supported with a suite of simple automated statistical analysis functions for rapid exploratory analyses. We present examples of its use that validates the integrity relative to results previously reported. The data may also be downloaded for user determined analysis. Conclusion: We propose the use of this online database as a hypothesis generator and method to confirm/refute findings made from patient samples and alternate model systems, to expand our understanding of HD pathogenesis. Importantly, additional tissue samples are available for further investigation of this cohort.

Pediatric In-Hospital Cardiac Arrest International Registry (PACHIN): protocol for a prospective international multicenter register of cardiac arrest in children

Background and aims Cardiac arrest (CA) in children is a major public health problem. Thanks to advances in cardiopulmonary resuscitation (CPR) guidelines and teaching skills, results in children have improved. However, pediatric CA has a very high mortality. In the treatment of in-hospital CA there are still multiple controversies. The objective of this study is to develop a multicenter and international registry of in-hospital pediatric cardiac arrest including the diversity of management in different clinical and social contexts. Participation in this register will enable the evaluation of the diagnosis of CA, CPR and post-resuscitation care and its influence in survival and neurological prognosis. Methods An intrahospital CA data recording protocol has been designed following the Utstein model. Database is hosted according to European legislation regarding patient data protection. It is drafted in English and Spanish. Invitation to participate has been sent to Spanish, European and Latinamerican hospitals. Variables included, asses hospital characteristics, the resuscitation team, patient’s demographics and background, CPR, post-resuscitation care, mortality, survival and long-term evolution. Survival at hospital discharge will be evaluated as a primary outcome and survival with good neurological status as a secondary outcome, analyzing the different factors involved in them. The study design is prospective, observational registry of a cohort of pediatric CA. Conclusions This study represents the development of a registry of in-hospital CA in childhood. Its development will provide access to CPR data in different hospital settings and will allow the analysis of current controversies in the treatment of pediatric CA and post-resuscitation care. The results may contribute to the development of further international recommendations. Trial register: ClinicalTrials.gov Identifier: NCT04675918. Registered 19 December 2020 – Retrospectively registered, https://clinicaltrials.gov/ct2/show/record/NCT04675918?cond=pediatric+cardiac+arrest&draw=2&rank=10

AN INVERSION APPROACH FOR BIOCHEMICAL PARAMETERS OF VEGETATION BASED ON THE PROSPECT-5 MODEL

Accurate inversion of vegetation biochemicals using the PROSPECT model mostly depends on a proper inversion approach, including a suitable optimizing algorithm, appropriate dependent variables, and different properties from spectra of reflectance (R) and transmittance (T). In this paper, we propose a special inversion method using PROSPECT-5 and then explore its effectiveness in inverting chlorophyll, carotenoids, equivalent water thickness, and dry matter per area data from the ANGERS database. The inversion strategy includes (i) an optimal algorithm with constrained bounds (fminsearchbnd) to replace the common function fminsearch, (ii) and four parameters are considered together and separately as dependent variables of models, (iii) Using properties from the spectra of R, T and combined R&T to invert the above four biochemical parameters. The results show that fminsearchbnd can improve the model's R2 based on a field-measured database. Moreover, using the entire set of parameters together as the model inputs is more effective than using single parameters separately. T spectra are favoured for all parameter inversions in the model database while being inapplicable in the ANGERS database. These findings provide an appropriate inversion strategy for the PROSPECT-5 model in vegetation biochemical parameters analysis and suggest further research to develop an accurate inversion process for vegetation based on various physical models.