Investigation on dynamics of drillstring systems from random viewpoint

Drillstrings are one of the critical components used for exploring and exploiting oil and gas reservoirs in the petroleum industry. As being very long and slender, the drillstring experiences various vibrations during the drilling operation, and these vibrations are random in essence. The first part of the thesis focuses on stochastic stick-slip dynamics of the drill bit by a finite element model and a single degree of freedom drillstring model in Chapters 3 and 4, respectively. In the single degree of freedom model, the path integration (PI) method is firstly used to obtain the probability density evolution of the dynamic response. Then Monte Carlo (MC) simulation is used for validating PI results and conducting the parametric study. The second step of my research is to study the stochastic dynamics of a vertical, multiple degrees of freedom drillstring system. The work of this part is presented in Chapter 5. The novelty of this work relies on the fact that it is the first time that the statistic linearization method is applied to a drillstring system in the bit-rock interaction to find an equivalent linear dynamic system which is then solved with the stochastic Newmark algorithm. After that, the stick-slip and bit-bounce phenomena are analyzed from random viewpoint. The third step of my research move on to directional drilling. A static study of directional drillstring from random viewpoint is presented in Chapter 6. The finite element method (FEM) based on the soft string model is employed and built. Then two strategies are taken to model the random component for hoisting drag calculation. The purpose of this work is to analyze the effects of the random component on hoisting drag calculation by the MC simulation method

Generalize a Small Pre-trained Model to Arbitrarily Large TSP Instances

For the traveling salesman problem (TSP), the existing supervised learning based algorithms suffer seriously from the lack of generalization ability. To overcome this drawback, this paper tries to train (in supervised manner) a small-scale model, which could be repetitively used to build heat maps for TSP instances of arbitrarily large size, based on a series of techniques such as graph sampling, graph converting and heat maps merging. Furthermore, the heat maps are fed into a reinforcement learning approach (Monte Carlo tree search), to guide the search of high-quality solutions. Experimental results based on a large number of instances (with up to 10,000 vertices) show that, this new approach clearly outperforms the existing machine learning based TSP algorithms, and significantly improves the generalization ability of the trained model

Dynamic analysis of a drill-string under deterministic and random excitations

Drill-strings are slender structures used to dig into the rock in search of oil and gas. Failures of drill-strings are time and money consuming and therefore the dynamics of drill-strings must be investigated and carefully controlled. In the thesis, a dynamic model of the drill-string that is suitable for predicting axial, torsional and lateral vibrations is built using Euler-Bernoulli beam theory. The drillstring is driven by a DC motor on the top and is subjected to distributed loads due to its own weight as well as bit/formation interaction. The model is axial-torsional, lateral-torsional coupled. Under deterministic excitations, the model captures stickslip behavior in drilling operation. Analysis on its negative effect on drilling performance is made, and potential mitigation measures are also discussed. In random model, the excitations to the drill-bit are modeled as combination of deterministic and random components. Monte Carlo (MC) simulation is employed to obtain the statistics of the response. Two cases of random excitation with different intensities are investigated. The results from MC simulation are compared against that from deterministic case. Secondly, the thesis focuses on the drill-string torsional vibration and its stick-slip analysis. A finite element model of the drillstring with inclusion of both deterministic and random excitations is also developed. Simulation is carried out under certain parameters and it is shown that in deterministic case the torsional vibration may behave stick-slip. With change of some parameters, bifurcation and chaos of the system are observed. In the random case, Monte Carlo simulation and path integration method are used to capture the probabilistic information of the response. The results of path integration match well to those of deterministic cases. Although there are some limitations, this thesis will help the author better understand drill-string downhole behaviors and lay a foundation for further research work

Stick-Slip Analysis of a Drill String Subjected to Deterministic Excitation and Stochastic Excitation

Using a finite element model, this paper investigates the torsional vibration of a drill string under combined deterministic excitation and random excitation. The random excitation is caused by the random friction coefficients between the drill bit and the bottom of the hole and assumed as white noise. Simulation shows that the responses under random excitation become random too, and the probabilistic distribution of the responses at each discretized time instant is obtained. The two points, entering and leaving the stick stage, are examined with special attention. The results indicate that the two points become random under random excitation, and the distributions are not normal even when the excitation is assumed as Gaussian white noise

Coupled Dynamic Analysis for the Riser-Conductor of Deepwater Surface BOP Drilling System

Deepwater surface BOP (surface blowout prevention, SBOP) drilling differs from conventional riser drilling system. To analyze the dynamic response of this system, the riser-conductor was considered as a beam with varied cross-sections subjected to loads throughout its length; then an equation of motion and free vibration of the riser-conductor string for SBOP was developed. The finite difference method was used to solve the equation of motion in time domain and a semianalytical approach based on the concept of section division and continuation was proposed to analyze free vibration. Case simulation results show that the method established for SBOP system natural frequency analysis is reasonable. The mode shapes of the riser-conductor are different between coupled and decoupled methods. The soil types surrounding the conductor under mudline have tiny effect on the natural frequency. Given that some papers have discussed the response of the SBOP riser, this work focused on the comparison of the dynamic responses on the wellhead and conductor with variable conditions. The dynamic lateral displacement, the bending moment, and the parameters’ sensitivity of the wellhead and the conductor were analyzed

A Nonlinear Constitutive Model for Disintegrated Carbonaceous Mudstone Based on Logarithmic Functions

To study the mechanical characteristics of the disintegrated carbonaceous mudstone (DCM), consolidated drained triaxial tests were conducted on the DCM with three degrees of compaction (i.e., 90%, 93%, and 96%). Then, the nonlinear constitutive model suitable for the DCM was established based on test results using a logarithmic function. The stress-strain characteristics of the DCM were analyzed. The results revealed that the axial strain of the DCM was positively correlated with the deviatoric stress and lateral strain. The slopes of deviatoric stress-axial strain curves decreased with the increase of axial strain and so did the slopes of the axial strain-volumetric strain curves. The strength of the DCM increased with the increase of the confining pressure and the degree of compaction. In addition, the axial strain induced by dilatancy was also positively correlated with the degree of compaction and the confining pressure. Furthermore, under triaxial loading conditions, the relationship between the stress and strain of the DCM can be expressed by a logarithmic function; based on this, a nonlinear constitutive model with ten material parameters was derived. In addition, the results of numerical tests using the model showed similar stress-strain characteristics of the DCM comparing with the triaxial tests. Hence, it indicated that the nonlinear constitutive model based on the logarithmic function can reflect the nonlinear stress-strain characteristics of the DCM

Derivative Parameters of Hyperspectral NDVI and Its Application in the Inversion of Rapeseed Leaf Area Index

AVNDVI (Accumulative Visible Normalized Difference Vegetation Index), a new type of derivative parameters of NDVI, was set up by improving the computational formulas and importing the spectral information of visible bands after analyzing the construction idea of NDVI and its derivative parameters. Then, the characteristic values of VNDVI (Visible NDVI) were calculated by applying a combinational method of sensitive bands of visible bands. The study carried out the fitting analysis between NDVI, VNDVI, AVNDVI, and LAI (Leaf Area Index). Several conclusions are obtained according to data analysis. Firstly, all of the determination coefficients between NDVI, VNDVI, AVNDVI, and LAI of rapeseed can reach or exceed 0.83. The distribution of their RMSE values ranges from 0.4 to 0.5 and absolute values of RE vary from 0.9% to 2.1%. Secondly, the inversion sensitivity SV of VNDVI and LAI ranges from 0.7 to 1.9 relative to NDVI, and the inversion sensitivity SA of AVNDVI decreases in varying degrees with the promotion of capacity of resisting disturbance accordingly. Its value varies from 0.1 to 0.9. Thirdly, the values of SA remain stable between 0.1 and 0.3 with the increase of NDVI. Applying the inversion model of AVNDVI will be a considerable scheme when faced with a complex environment and many interfering factors

Interdomain I/O Optimization in Virtualized Sensor Networks

In virtualized sensor networks, virtual machines (VMs) share the same hardware for sensing service consolidation and saving power. For those VMs that reside in the same hardware, frequent interdomain data transfers are invoked for data analytics, and sensor collaboration and actuation. Traditional ways of interdomain communications are based on virtual network interfaces of bilateral VMs for data sending and receiving. Since these network communications use TCP/IP (Transmission Control Protocol/Internet Protocol) stacks, they result in lengthy communication paths and frequent kernel interactions, which deteriorate the I/O (Input/Output) performance of involved VMs. In this paper, we propose an optimized interdomain communication approach based on shared memory to improve the interdomain communication performance of multiple VMs residing in the same sensor hardware. In our approach, the sending data are shared in memory pages maintained by the hypervisor, and the data are not transferred through the virtual network interface via a TCP/IP stack. To avoid security trapping, the shared data are mapped in the user space of each VM involved in the communication, therefore reducing tedious system calls and frequent kernel context switches. In implementation, the shared memory is created by a customized shared-device kernel module that has bidirectional event channels between both communicating VMs. For performance optimization, we use state flags in a circular buffer to reduce wait-and-notify operations and system calls during communications. Experimental results show that our proposed approach can provide five times higher throughput and 2.5 times less latency than traditional TCP/IP communication via a virtual network interface