Determination of the maximum allowable gas pressure for an underground gas storage salt cavern——A case study of Jintan,China

Increasing the allowable gas pressure of underground gas storage(UGS) is one of the most effective methods to increase its working gas capacity. In this context, hydraulic fracturing tests are implemented on the target formation for the UGS construction of Jintan salt caverns, China, in order to obtain the minimum principal in situ stress and the fracture breakdown pressure. Based on the test results, the maximum allowable gas pressure of the Jintan UGS salt cavern is calibrated. To determine the maximum allowable gas pressure, KING-1 and KING-2 caverns are used as examples. A three-dimensional(3D)geomechanical model is established based on the sonar data of the two caverns with respect to the features of the target formation. New criteria for evaluating gas penetration failure and gas seepage are proposed. Results show that the maximum allowable gas pressure of the Jintan UGS salt cavern can be increased from 17 MPa to 18 MPa(i.e. a gradient of about 18 k Pa/m at the casing shoe depth). Based on numerical results, a field test with increasing maximum gas pressure to 18 MPa has been carried out in KING-1 cavern. Microseismic monitoring has been conducted during the test to evaluate the safety of the rock mass around the cavern. Field monitoring data show that KING-1 cavern is safe globally when the maximum gas pressure is increased from 17 MPa to 18 MPa. This shows that the geomechanical model and criteria proposed in this context for evaluating the maximum allowable gas pressure are reliable.

A state-of-the-art review of automated extraction of rock mass discontinuity characteristics using three-dimensional surface models

In the last two decades,significant research has been conducted in the field of automated extraction of rock mass discontinuity characteristics from three-dimensional(3D)models.This provides several methodologies for acquiring discontinuity measurements from 3D models,such as point clouds generated using laser scanning or photogrammetry.However,even with numerous automated and semiautomated methods presented in the literature,there is not one single method that can automatically characterize discontinuities accurately in a minimum of time.In this paper,we critically review all the existing methods proposed in the literature for the extraction of discontinuity characteristics such as joint sets and orientations,persistence,joint spacing,roughness and block size using point clouds,digital elevation maps,or meshes.As a result of this review,we identify the strengths and drawbacks of each method used for extracting those characteristics.We found that the approaches based on voxels and region growing are superior in extracting joint planes from 3D point clouds.Normal tensor voting with trace growth algorithm is a robust method for measuring joint trace length from 3D meshes.Spacing is estimated by calculating the perpendicular distance between joint planes.Several independent roughness indices are presented to quantify roughness from 3D surface models,but there is a need to incorporate these indices into automated methodologies.There is a lack of efficient algorithms for direct computation of block size from 3D rock mass surface models.

Quantum Operator Model for Data Analysis and Forecast

A new dynamic model identification method is developed for continuous-time series analysis and forward prediction applications. The quantum of data is defined over moving time intervals in sliding window coordinates for compressing the size of stored data while retaining the resolution of information. Quantum vectors are introduced as the basis of a linear space for defining a Dynamic Quantum Operator (DQO) model of the system defined by its data stream. The transport of the quantum of compressed data is modeled between the time interval bins during the movement of the sliding time window. The DQO model is identified from the samples of the real-time flow of data over the sliding time window. A least-square-fit identification method is used for evaluating the parameters of the quantum operator model, utilizing the repeated use of the sampled data through a number of time steps. The method is tested to analyze, and forward-predict air temperature variations accessed from weather data as well as methane concentration variations obtained from measurements of an operating mine. The results show efficient forward prediction capabilities, surpassing those using neural networks and other methods for the same task.

Variable Daily Air Temperature Model for Analysis and Design

An analytical model, TA(t), for the observed outside air temperature change, Ta(t), with time is developed using two components: one for the variation caused by the Earth’s movement, plus any other quasi-stationary thermodynamic effects due to industrialization;and one for the random variation caused by stochastic and/or chaotic, local environmental changes. The first component, TR(t), describes a regular trend, expressed by periodic functions of time and constants unchanged with time. The second component, TS, is a random, stochastic variation. For the observed outside air temperature, the analytical model of TA(t)=TR(t) +TS is such as to give a statistically best approximation for the observed time period with = min. Several versions for the TR(t) functions are defined and tested in the study for an example location for 20 years. The best model for TR(t) t is found as a linear function with time plus a variable-coefficient Fourier series with linearly changing amplitude with time. It is found that the final analytical temperature, TA(t), can be used not only to represent the historical daily mean temperature but also to predict the future daily mean temperature at the given location. The upper and lower boundaries give safety limits for the temperature prediction. The stochastic component identified in the model is stable and stationary. The method of model identification for TA(t) can be used for determining input temperature functions for supporting engineering design;or for an unbiased scientific inquiry of temperature change with time in climate studies.

Synthesis,characterization and life cycle assessment of electrochemically exfoliated KOH-activated holey graphene

Graphene materials have drawn tremendous attention in recent years.The formation of holes and pores on graphene sheets can provide transfer channels and facilitate the ion/electron transport kinetics.In this study,graphene nanosheets were prepared electrochemically,and then,they were used as the starting material for the preparation of holey graphene(HG)through the KOH activation process.The weight ratio of initial electrochemically exfoliated graphene(EEG)to KOH was optimized according to the morphological features,BET surface area examination,graphene number of layers calculated from XRD patterns,and the ID/IG ratio obtained from Raman analysis.Results showed that increasing the KOH amount led to the achievement of higher values of ID/IG and surface area and less re-stacking of graphene sheets which occurs because of the heat treatment process.The environmental burdens of the production routes for the preparation of EEG and HG were investigated by cradle-to-gate life cycle assessment(LCA).The LCA results of EEG production indicated that electricity with the contributions of 94%,91%,82%,and 75%of the total impact in four environmental categories,including fossil fuel depletion,ozone depletion,global warming,and smog was the main environmental weakness.In the pore generation process,KOH was recognized as the biggest contributor(about 51%to 83%of the total impact)in six impact categories,including ozone depletion,non-carcinogenics,smog,global warming,carcinogenics,and eutrophication which could be attributed to its high consumption amount(21.9 kg).This work offers environmental considerations for the development of sustainable graphene materials.

模拟盐岩蠕变力学特性的幂函数模型（英文）

本文采用二维颗粒流代码(PFC2D),在线性平行粘结模型(LPBM)的基础上,提出了一种新的盐岩微观蠕变模型。该模型假定平行粘结直径(PBD)随时间的衰减模式遵循幂函数变化规律。与应力腐蚀模型(PSC)相比较,在相同的应力水平下,幂函数模型表现出较小的应力波动以及更为平滑的蠕变应变-时间曲线特征。通过对比室内蠕变试验曲线以及Burgers模型的拟合结果,验证了该模型在预测盐岩蠕变变形特性方面具有有效性和适应性。数值结果表明,幂函数模型能够捕获室内试验中所观测到的蠕变损伤破坏模式。