MED and WPT based technique for bearings fault detection

A new technique is proposed in this work for fault detection in rolling element bearings, which is based on minimum entropy deconvolution (MED), wavelet packet decomposition (WPT) and envelop analysis. Firstly, the collected vibration signal is preprocessed to highlight defect-related impulses, and a new indicator named envelope spectra sparsity (ESS) is proposed to automatically select the filter length of MED. Then the preprocessed signal is decomposed into WPT nodes, and the most sensitive node containing fault-related information are selected from all the nodes to improve the accuracy of the fault detection. Sparsity of wavelet packet nodes signal (SWPN) is proposed in this step as a measure indicator. Lastly the power spectrum is used to highlight the bearing fault characteristic frequencies. The effectiveness of the proposed AMED-WPT technique in feature extraction and analysis is verified by a series of experimental tests corresponding to different bearing conditions

Image Restoration Algorithm Based on Artificial Fish Swarm Micro Decomposition of Unknown Priori Pixel

In this paper, we put forward a new method to holographic reconstruct image that prior information, module matching and edge structure information is unknown. The proposed image holographic restoration algorithm combines artificial fish swarm micro decomposition and brightness compensation. The traditional method uses subspace feature information of multidimensional search method, it is failed to achieve the fine structure information of image texture template matching and the effect is not well. Therefore, it is difficult to holographic reconstruct the unknown pixels. This weakness obstructs the application of image restoration to many fields. Therefore, we builds a structure texture conduction model for the priority determination of the block that to be repaired, then we use subspace feature information multidimensional search method to the confidence updates of unknown pixel. In order to maintain the continuity of damaged region in image, the artificial fish swarm algorithm decomposition model is combined with the image brightness compensation strategy of edge feature. The simulation result shows that it has a good visual effect in image restoration of a priori unknown pixel, recovery time and computation costs are less, the stability and convergence performance is improved

Downhole Temperature Modeling for Non-Newtonian Fluids in ERD Wells

Having precise information of fluids' temperatures is a critical process during planning of drilling operations, especially for extended reach drilling (ERD). The objective of this paper is to develop an accurate temperature model that can precisely calculate wellbore temperature distributions. An established semi-transient temperature model for vertical wellbores is extended and improved to include deviated wellbores and more realistic scenarios using non-Newtonian fluids. The temperature model is derived based on an energy balance between the formation and the wellbore. Heat transfer is considered steady-state in the wellbore and transient in the formation through the utilization of a formation cooling effect. In this paper, the energy balance is enhanced by implementing heat generation from the drill bit friction and contact friction force caused by drillpipe rotation. A non-linear geothermal gradient as a function of wellbore inclination, is also introduced to extend the model to deviated wellbores. Additionally, the model is improved by considering temperature dependent drilling fluid transport and thermal properties. Transport properties such as viscosity and density are obtained by lab measurements, which allows for investigation of the effect of non-Newtonian fluid behavior on the heat transfer. Furthermore, applying a non-Newtonian pressure loss model enables an opportunity to evaluate the impact of viscous forces on fluid properties and thus the overall heat transfer. Results from sensitivity analysis of both drilling fluid properties and other relevant parameters will be presented. The main application area of this model is related to optimization of drilling fluid, hydraulics, and wellbore design parameters, ultimately leading to safe and cost efficient operations.publishedVersio

Trajectory Envelope of a Subsea Shuttle Tanker Hovering in Stochastic Ocean Current - Model Development and Tuning

A subsea shuttle tanker (SST) concept for liquid carbon dioxide transportation was recently proposed to support studies evaluating the ultra-efficient underwater cargo submarine concept. One important topic is the position keeping ability of SST during the offloading process. In this process, the SST hovers above the well and connects with the wellhead using a flowline. This process takes around 4 h. Ocean currents can cause tremendous drag forces on the subsea shuttle tanker during this period. The flow velocities over hydroplanes are low throughout this process, and the generated lift forces are generally insufficient to maintain the SST’s depth. The ballast tanks cannot provide such fast actuation to cope with the fluctuation of the current. It is envisioned that tunnel thrusters that can provide higher frequency actuation are required. This paper develops a maneuvering model and designs a linear quadratic regulator that facilitates the SST station-keeping problem in stochastic current. As case studies, the SST footprints at 0.5 m/s, 1.0 m/s, and 1.5 m/s mean current speeds are presented. Numerical results show that the designed hovering control system can ensure the SST’s stationary during offloading. The required thrust from thrusters and the propeller are presented. The presented model can serve as a basis for obtaining a more efficient design of the SST and provide recommendations for the SST operation.acceptedVersio

Approaches to the design of model predictive controllers for linear, piecewise linear and nonlinear systems

Ph.DDOCTOR OF PHILOSOPH

Data-Driven Approaches Tests on A Laboratory Drilling System

In recent years, considerable resources have been invested to exploit vast amounts of data that get collected during exploration, drilling and production of oil and gas. Data-related digital technologies potentially become a game changer for the industry in terms of reduced costs through increasing operational efficiency and avoiding accidents, improved health, safety and environment through strengthening situational awareness and so on. Machine learning, an application of artificial intelligence to offer systems/processes self-learning and self-driving ability, has been around for recent decades. In the last five to ten years, the increased computational powers along with heavily digitized control and monitoring systems have made machine learning algorithms more available, powerful and accurate. Considering the state-of-art technologies that exist today and the significant resources that are being invested into the technologies of tomorrow, the idea of intelligent and automated drilling systems to select best decisions or provide good recommendations based on the information available becomes closer to a reality. This study shows the results of our research activity carried out on the topic of drilling automation and digitalization. The main objective is to test the developed machine learning algorithms of formation classification and drilling operations identification on a laboratory drilling system. In this paper, an algorithm to develop data-driven models based on the laboratory data collocated in many scenarios (for instance, drilling different formation samples with varying drilling operational parameters and running different operations) is presented. Moreover, a testing algorithm based on data-driven models for new formation detection and confirmation is proposed. In the case study, results on multiple experiments conducted to test and validate the developed machine learning methods have been illustrated and discussed.publishedVersio

Review and investigations on geothermal energy extraction from abandoned petroleum wells

Geothermal energy is a sustainable and renewable energy source, which can be used in electricity production, space heating/cooling, and other industrial applications. In the recent years, it has been gathering more and more attention due to its numerous advantages as low impact on the surrounding environment, continuous power outputs, low greenhouse gas emissions, and worldwide availabilities. All make the geothermal energy a significant contributor to the global energy productions in an environmentally friendly way. One big concern of geothermal sources’ exploration is the expensive investment costs of geothermal wells. Utilizing abandoned petroleum wells for the purpose of geothermal extraction is a novel idea. Well temperature profiles help to estimate how much heat can be transferred and produced from the wells. In this paper, a literature review has been done to investigate the existing applications on geothermal energy extraction utilizing abandoned petroleum wells. Then, the case study demonstrates the importance of properties of working fluids, wellbore architecture, and operational parameters (circulation rate, inlet temperature, etc.) in geothermal energy production. The obtained results can be used to achieve an improved data interpretation and generate more optimal solutions. In geothermal projects, extensive knowledge of the heat transfer is of great importance for the economical aspect and the performance of wells. Our work demonstrates that it is a good approach to provide cost-effective solutions to enhance heat extraction from geothermal wells.publishedVersio

Drilling data quality improvement and information extraction with case studies

Data analytics is a process of data acquiring, transforming, interpreting, modelling, displaying and storing data with an aim of extracting useful information, so that decision-making, actions executing, events detecting and incidents managing can be handled in an efficient and certain manner. However, data analytics also meets some challenges, for instance, data corruption due to noises, time delays, missing and external disturbances, etc. This paper focuses on data quality improvement to cleanse, improve and interpret the post-well or real-time data to preserve and enhance data features, like accuracy, consistency, reliability and validity. In this study, laboratory data and field data are used to illustrate data issues and show data quality improvements with using different data processing methods. Case study clearly demonstrates that the proper data quality management process and information extraction methods are essential to carry out an intelligent digitalization in oil and gas industry.publishedVersio

Variations of hydraulic properties of granular sandstones during water inrush : effect of small particle migration

The evaluation of the hydraulic properties evolution of granular sandstones in relation with groundwater inrush within faults is an important issue for mining engineering applications. This paper presents the results of an experimental investigation of small particle migration from granular sandstone samples under different original porosities, particle size compositions and water flow pressures. A new rock testing system has been setup to carry out the tests. Based on the results, it is observed that the overall permeability evolution during the tests can be divided into four different phases, including i) re-arrangement of large rock fragments, ii) water inrush with substantial particle migration, iii) continued moderate particles seepage, and iv) steady state water flow. The crushing of edges and corners of large rock fragments, and the evolution of the fracture network has mainly been observed in the first two phases of the tests. The results indicate that the migration of small particles has an essential effect on permeability and porosity increase during water inrush through fractured sandstone. The samples with higher original porosity, higher percentage of fine particles in their formation and under higher water flow pressures, achieve higher permeability and porosity values when the test is complete. Furthermore, using the measured data, the performances of a number of empirical models, for permeability evolution in fractured porous media, have been studied. The prediction results indicate that not all of the fractures in a sample domain contribute in small particle migration. There are parts of the fracture network that are not effective in particle flow, a sample with less original porosity, more fine particles and under lower water pressure shows less ineffective fractures. Therefore, using the concept of the effective porosity (fracture) is sufficient enough for the flow calculation