Point process simulation of generalised inverse Gaussian processes and estimation of the Jaeger integral

In this paper novel simulation methods are provided for the generalised inverse Gaussian (GIG) Lévy process. Such processes are intractable for simulation except in certain special edge cases, since the Lévy density associated with the GIG process is expressed as an integral involving certain Bessel functions, known as the Jaeger integral in diffusive transport applications. We here show for the first time how to solve the problem indirectly, using generalised shot-noise methods to simulate the underlying point processes and constructing an auxiliary variables approach that avoids any direct calculation of the integrals involved. The resulting augmented bivariate process is still intractable and so we propose a novel thinning method based on upper bounds on the intractable integrand. Moreover, our approach leads to lower and upper bounds on the Jaeger integral itself, which may be compared with other approximation methods. The shot noise method involves a truncated infinite series of decreasing random variables, and as such is approximate, although the series are found to be rapidly convergent in most cases. We note that the GIG process is the required Brownian motion subordinator for the generalised hyperbolic (GH) Lévy process and so our simulation approach will straightforwardly extend also to the simulation of these intractable processes. Our new methods will find application in forward simulation of processes of GIG and GH type, in financial and engineering data, for example, as well as inference for states and parameters of stochastic processes driven by GIG and GH Lévy processes.

STEP Change in Preventing Stuck Pipe and Tight Hole Events Using Machine Learning

Wellbore stability problems, such as stuck pipe and tight spots, are one of the most critical risks that impact drilling operations. Over several years, Oil and Gas Operator in Middle East has been facing problems associated with stuck pipe and tight spot events, which have a major impact on drilling efficiency, well cost, and the carbon footprint of drilling operations. On average, the operator loses 200 days a year (Non-Productive Time) on stuck pipe and associated fishing operations. Wellbore stability problems are hard to predict due to the varying conditions of drilling operations: different lithology, drilling parameters, pressures, equipment, shifting crews, and multiple well designs. All these factors make the occurrence of a stuck pipe quite hard to mitigate only through human intervention. For this reason, The operator decided to develop an artificial intelligence tool that leverages the whole breadth and depth of operator data (reports, sensor data, well engineering data, lithology data, etc.) in order to predict and prevent wellbore stability problems. The tool informs well engineers and rig crews about possible risks both during the well planning and well execution phase, suggesting possible mitigation actions to avoid getting stuck. Since the alarms are given ahead of the bit, several hours before the possible occurrence of the event, the well engineers and rig crews have ample time to react to the alarms and prevent its occurrence. So far, the tool has been deployed in a pilot phase on 38 wells giving 44 true alarms with a recall of 94%. Since mid-2021 operator has been rolling out the tool scaling to the whole drilling operations (over 40 rigs).

Demolition of Offshore Wellhead Topsides - Weight Engineering Challenges

Some of the platforms installed in offshore fields in India have exceeded their design lifespan but continues to operate. For these platforms to continue operating safely and successfully, major revamp is required. As the wellheads are in operation beyond their intended lifespan and requires revamping due to their heavily corroded state, decommissioning, removal and replacement of existing offshore structures presents technical and economic challenges to Operating Company's and Contractors, alike. Due to the age of these platforms, availability of technical/engineering data is minimal and often needs to be developed from scratch. The focus of this paper is on Weight Engineering and challenges in developing such data for a platform without as built information. The paper also touches on the different stages of executing the project including demolition engineering strategies applied, use of different installation aids to facilitate demolition etc during successful execution of Brownfield works in Mumbai High field by National Petroleum Construction Company (NPCC).

Modelling to Engineering Data Using a New Class of Continuous Models

In this paper, a new flexible generator of continuous lifespan models referred to as the Topp-Leone Weibull G (TLWG) family is developed and studied. Several mathematical characteristics have been investigated. The new hazard rate of the new model can be “monotonically increasing,” “monotonically decreasing,” “bathtub,” and “J shape.” The Farlie Gumbel Morgenstern (FGM) and the modified FGM (MFGM) families and Clayton Copula (CCO) are used to describe and display simple type Copula. We discuss the estimation of the model parameters by the maximum likelihood (MLL) estimations. Simulations are carried out to show the consistency and efficiency of parameter estimates, and finally, real data sets are used to demonstrate the flexibility and potential usefulness of the proposed family of algorithms by using the TLW exponential model as example of the new suggested family.

Specifics of turbo-alternator design with a high rotational speed of 6000 rpm

Object and purpose of research. The object under study is a 36 МW turbo-alternator (TA) with electromagnetic excitation and a high rotational speed of 6000 rpm, which can be used as an option for ac electric power source of 100 Hz in ship electric power systems with a turbo-alternator plant. The purpose is to perform electromagnetic calculations to determine TA main data and technical characteristics, including the stator and rotor pack, their design, mass of active materials, etc. for comparison with a TA of the same power but 3000 rpm. Materials and methods. The studies are based on research and engineering data about investigations and design of double-pole industrial TA of 50 Hz as well as TA with a high current frequency (100 Hz and higher). For this purpose, the known formulas were used to estimate the size of TA active elements, excitation forces of stator and rotor windings, as well as methods for calculation of main TA parameters and technical characteristics. Main results. Design specifics of TA with a high rotational speed of 6000 rpm is identified, and results of electromagnetic estimations are obtained for a specific 36 MW turbo-alternator of 100 Hz with a forced close cycle cooling and better mass and size characteristics. Conclusions. The obtained results are of practical value, showing feasibility of developing a version of 36.0 МW TA with a rotational speed of 6000 rpm and significantly reduced specific mass and size characteristics – tentatively by 35–40 % as compared to the existing TA of the same power but with a speed of 3000 rpm.

A New Two-Parametric Maxwell-Rayleigh Distribution: Application to the Analysis of Engineering Data

In this study a new generalisation of Rayleigh Distribution has been studied and referred it is as “A New Two-Parametric Maxwell-Rayleigh Distribution”. This distribution is obtained by adopting T-X family procedure. Several distributional properties of the formulated distribution including moments, moment generating function, Characteristics function and incomplete moments have been discussed. The expressions for ageing properties have been derived and discussed explicitly. The behaviour of the pdf and Hazard rate function has been illustrated through different graphs. The parameters are estimated through the technique of MLE. Eventually the versatility and the efficacy of the formulated distribution have been examined through real life data sets related to engineering science.

Applying Reverse Engineering Principles on Analysis of Failures Occurred in Ship Production

In the current work, a new error evaluation methodology is introduced based on error analysis in ship production with reverse engineering data. The aim is to determine the errors and prevent or reduce the occurrence in other projects. First step is to compose a database of the errors; then, group the similar errors and calculate the Error Priority Number (EPN) by the evaluation of the predetermined criteria. The radar diagrams, which are suitable for representing a number of parameters having the same variables, were used to present the error groups in a simple way. The error groups were created on the diagram with the scores taken from the specific criteria. With the aid of the radar diagram, valuable information is given by presenting similarities and dissimilarities of these errors with other error groups. After examining the radar diagrams and evaluating the results, the cause and effect diagrams were prepared for these error groups from the field experts. Thus, the methodology should be customized for the shipyard to ensure maximum efficiency.

Data-Efficient Machine Learning on 3D Engineering Data

Decisions in engineering design are closely tied to the 3D shape of the product. Limited availability of 3D shape data and expensive annotation present key challenges for using Artificial Intelligence in product design and development. In this work we explore transfer learning strategies to improve the data-efficiency of geometric reasoning models based on deep neural networks as used for tasks such as shape retrieval and design synthesis. We address the utilization of problem- related and un-annotated 3D data to compensate for small data volumes. Our experiments show promising results for knowledge transfer on mechanical component benchmarks.