Experimental study on the hydro-thermal-deformation characteristics of cement-stabilized soil exposed to freeze–thaw cycles

The exploration of the hydro-thermal characteristics and deformation behaviors of cement-stabilized soils is important for the prevention and control of freeze–thaw damage in cold region engineering. This study used six groups of cement-stabilized soil samples with different cement contents (i.e., 3%, 6%, 9%, 12%, 15%, and 18%) to investigate the variations in soil temperature, volumetric unfrozen water content, deformation, freezing temperature, and dry density. The results showed that the temperatures of the cement-stabilized soil samples during the freezing and thawing processes can be categorized into three stages and that the freezing temperature decreased with increasing cement content. Moreover, the cement content and ambient temperature significantly affected the volumetric unfrozen water content of the cement-stabilized soil samples during the freeze–thaw cycles, and the soil temperatures corresponding to the peak hysteresis degree were relatively consistent with the freezing temperature. The residual volumetric unfrozen water content primarily depended not only on the cement content but also on the freezing condition. Although the variations in volumetric unfrozen water contents during the freezing and thawing processes were similar, the ranges in temperature change differed significantly, particularly in the drastic phase transition zone. Additionally, adding cement into soils effectively inhibited deformation, mainly due to the dual positive effects of the liquid water reduction owing to hydration reaction and structure compaction owing to the filling of hydration products

Optimization of tight gas reservoir fracturing parameters via gradient boosting regression modeling

In China, the exploitation of most unconventional oil and gas reservoirs is dependent on hydraulic fracturing, which is a key method employed when developing tight gas formations. Numerous scholars and field engineers, both domestically and internationally, have conducted extensive numerical simulations and physical experiments to study crack propagation and predict post-fracturing productivity in hydraulic fracturing. Although some progress has been reported in this regard, it is difficult to accurately predict the well productivity using mechanistic models owing to the vertical multilayered development of tight gas reservoirs. In this study, vertical fractured wells in a block of Sulige gas field were examined. The block relied on hydraulic fracturing to produce tight gases. However, as development progressed, the available reservoir environment deteriorated, large differences emerged between wells after fracturing, and the fracturing results did not meet the expectations. In this study, geological, construction, and generation data for this block that had been collected since 2007 were analyzed. After applying multiple machine-learning methods to filter outliers and fill in missing values, k-means clustering, classification enhancement, extreme gradient enhancement, and LightGBM algorithms were used to establish a regression model. The analysis results revealed that the regression accuracy of the cluster test set was as high as 70% and that the LightGBM model had the best regression effect among the 227 stripper wells in the block. After optimizing the fracturing construction parameters (fracturing fluid volume, proppant volume, liquid-nitrogen volume, and pumping rate), the average fracturing fluid and liquid-nitrogen volumes per well decreased, whereas the unit reservoir proppant and liquid-nitrogen volumes increased. The results also revealed that 182 wells showed an improved initial production capacity during fracturing. The average gas production index per meter increased by 22.04%. This approach enabled rapid and efficient production forecasting and construction optimization. Moreover, this represents a novel fracture design method that is applicable to onsite engineers in tight gas production fields in the Ordos region

The State-of-the-Art Brief Review on Piezoelectric Energy Harvesting from Flow-Induced Vibration

Flow-induced vibration (FIV) is concerned in a broad range of engineering applications due to its resultant fatigue damage to structures. Nevertheless, such fluid-structure coupling process continuously extracts the kinetic energy from ambient fluid flow, presenting the conversion potential from the mechanical energy to electricity. As the air and water flows are widely encountered in nature, piezoelectric energy harvesters show the advantages in small-scale utilization and self-powered instruments. This paper briefly reviewed the way of energy collection by piezoelectric energy harvesters and the various measures proposed in the literature, which enhance the structural vibration response and hence improve the energy harvesting efficiency. Methods such as irregularity and alteration of cross-section of bluff body, utilization of wake flow and interference, modification and rearrangement of cantilever beams, and introduction of magnetic force are discussed. Finally, some open questions and suggestions are proposed for the future investigation of such renewable energy harvesting mode

Breakdown Pressure Prediction of Tight Sandstone Horizontal Wells Based on the Mechanism Model and Multiple Linear Regression Model

Accurately predicting the breakdown pressure in horizontal sections is essential when designing and optimizing fracturing jobs for horizontal wells in tight gas reservoirs. Taking the Sulige block in the Ordos Basin as an example, for different completion methods combined with indoor rock experience data and well data, a new method for predicting breakdown pressure based on a linear regression model is proposed. Based on the Hossain horizontal well stress field model, this paper established a calculation model of breakdown pressure under different completion methods by using experimental and well data. The average error between the calculation results and the actual breakdown pressure at the initiation point is 3.67%. A Pearson correlation analysis was conducted for eight sensitive factors of horizontal well stress, which showed that the maximum horizontal principal stress, minimum horizontal principal stress, tensile strength, and elastic modulus had strong linear correlations with breakdown pressure. In this study, multiple linear regression was used to establish the prediction model of breakdown pressure under different completion conditions, and the calculation method of the prediction model was optimized. The model was verified using the relevant data for four horizontal wells. The average relative error between the prediction model and the actual breakdown pressure was 4.33–6.30%, indicating that the breakdown pressure obtained by the new prediction model was similar to the actual conditions. Thus, the prediction model is reasonable and reliable

Breakdown Pressure Prediction of Tight Sandstone Horizontal Wells Based on the Mechanism Model and Multiple Linear Regression Model

Accurately predicting the breakdown pressure in horizontal sections is essential when designing and optimizing fracturing jobs for horizontal wells in tight gas reservoirs. Taking the Sulige block in the Ordos Basin as an example, for different completion methods combined with indoor rock experience data and well data, a new method for predicting breakdown pressure based on a linear regression model is proposed. Based on the Hossain horizontal well stress field model, this paper established a calculation model of breakdown pressure under different completion methods by using experimental and well data. The average error between the calculation results and the actual breakdown pressure at the initiation point is 3.67%. A Pearson correlation analysis was conducted for eight sensitive factors of horizontal well stress, which showed that the maximum horizontal principal stress, minimum horizontal principal stress, tensile strength, and elastic modulus had strong linear correlations with breakdown pressure. In this study, multiple linear regression was used to establish the prediction model of breakdown pressure under different completion conditions, and the calculation method of the prediction model was optimized. The model was verified using the relevant data for four horizontal wells. The average relative error between the prediction model and the actual breakdown pressure was 4.33–6.30%, indicating that the breakdown pressure obtained by the new prediction model was similar to the actual conditions. Thus, the prediction model is reasonable and reliable