Micro-macro evolution of mechanical behaviors of thermally damaged rock:A state-of-the-art review

The influence of thermal damage on macroscopic and microscopic characteristics of different rocks has received much attention in the field of rock engineering.When the rocks are subjected to thermal treatment,the change of macroscopic characteristics and evolution of micro-structure would be induced,ultimately resulting in different degrees of thermal damage in rocks.To better understand the thermal damage mechanism of different rocks and its effect on the rock performance,this study reviews a large number of test results of rock specimens experiencing heating and cooling treatment in the laboratory.Firstly,the variations of macroscopic behaviors,including physical parameters,mechanical parameters,thermal conductivity and permeability,are examined.The variations of mechanical parameters with thermal treatment variables(i.e.temperature or the number of thermal cycles)are divided into four types.Secondly,several measuring methods for microstructure,such as polarizing microscopy,fluorescent method,scanning electron microscopy(SEM),X-ray computerized tomography(CT),acoustic emission(AE)and ultrasonic technique,are introduced.Furthermore,the effect of thermal damage on the mechanical parameters of rocks in response to different thermal treatments,involving temperature magnitude,cooling method and thermal cycle,are discussed.Finally,the limitations and prospects for the research of rock thermal damage are proposed.

Analyzing characteristics of signal transmission in drill string system

It is a key problem to retrieve the drill-bit signal for seismic while drilling(SWD).The conventional approach is to extract the drill-bit signal from the pilot sensors deployed on the top of the drill string.Therefore,it is essential to obtain the propagating mechanism of drill-bit signal in the drill string.Based on the algorithm of transfer matrix,this paper analyzed the propagating rules of drill-bit signals in periodic drill pipe and derived the transmission equation for the drill-bit signal in Bottom Hole Assembly(BHA).To overcome the disadvantage that output energy is bigger than input energy,this equation is modified with an important scale factor.According to this method,the signal propagating in the drill string system was modeling with the real parameters for the drilling.The results show that the frequency filtering of the drill string system has a little impact on the drill-bit signal and its energy become 80%of the raw energy.

Microleveling aerogeophysical data using deep convolutional network and MoG-RPCA

Residual magnetic error remains after standard levelling process.The weak non-geological effect,manifesting itself as streaky noise along flight lines,creates a challenge for airborne geophysical data processing and interpretation.Microleveling is the process to eliminate this residual noise and is now a standard areogeophysical data processing step.In this paper,we propose a two-step procedure for single aerogeophysical data microleveling:a deep convolutional network is first adopted as approximator to map the original data into a low-level part with nature geological structures and a corrugated residual which still contains high-level detail geological structures;second,the mixture of Gaussian robust principal component analysis(MoG-RPCA)is then used to separate the weak energy fine structures from the residual.The final microleveling result is the addition of low-level structures from deep convolutional network and fine structures from MoG-RPCA.The deep convolutional network does not need dataset for training and the handcrafted network serves as prior(deep image prior)to capture the low-level nature geological structures in the areogeophysical data.Experiments on synthetic data and field data demonstrate that the combination of deep convolutional network and MoG-RPCA is an effective framework for single areogeophysical data microleveling.

Retrieving Drill Bit Seismic Signals Using Surface Seismometers

Seismic while drilling （SWD） is an emerging horehole seismic imaging technique that uses the downhole drill-bit vibrations as seismic source. Without interrupting drilling, SWD technique can make near-real-time images of the rock formations ahead of the bit and optimize drilling operation, with reduction of costs and the risk of drilling. However, the signal to noise ratio （SNR） of surface SWD-data is severely low for the surface acquisition of SWD data. Here, we propose a new method to retrieve the drill-bit signal from the surface data recorded by an array of broadband seismometers. Taking advantages of wavefield analysis, different types of noises are identified and removed from the surface SWD-data, resulting in the significant improvement of SNR. We also optimally synthesize seis- mic response of the bit source, using a statistical cross-coherence analysis to further improve the SNR and retrieve both the drill-bit direct arrivals and reflections which are then used to establish a reverse vertical seismic profile （RVSP） data set for the continuous drilling depth. The subsurface images derived from these data compare well with the corresponding images of the three-dimension surface seismic survey cross the well.