Summary: The injection of carbon dioxide (CO2) into coal seams is a prominent technique that can provide carbon sequestration in addition to enhancing coalbed methane extraction. However, CO2 injection into the coal seams can alter the coal strength properties and their long-term integrity. In this work, the strength alteration of coals induced by CO2 exposure was modeled using 147 laboratory-measured unconfined compressive strength (UCS) data points and considering CO2 saturation pressure, CO2 interaction temperature, CO2 interaction time, and coal rank as input variables. Advanced white-box and black-box machine learning algorithms including Gaussian process regression (GPR) with rational quadratic kernel, extreme gradient boosting (XGBoost), categorical boosting (CatBoost), adaptive boosting decision tree (AdaBoost-DT), multivariate adaptive regression splines (MARS), K-nearest neighbor (KNN), gene expression programming (GEP), and group method of data handling (GMDH) were used in the modeling process. The results demonstrated that GPR-Rational Quadratic provided the most accurate estimates of UCS of coals having 3.53%, 3.62%, and 3.55% for the average absolute percent relative error (AAPRE) values of the train, test, and total data sets, respectively. Also, the overall determination coefficient (R2) value of 0.9979 was additional proof of the excellent accuracy of this model compared with other models. Moreover, the first mathematical correlations to estimate the change in coal strength induced by CO2 exposure were established in this work by the GMDH and GEP algorithms with acceptable accuracy. Sensitivity analysis revealed that the Spearman correlation coefficient shows the relative importance of the input parameters on the coal strength better than the Pearson correlation coefficient. Among the inputs, coal rank had the greatest influence on the coal strength (strong nonlinear relationship) based on the Spearman correlation coefficient. After that, CO2 interaction time and CO2 saturation pressure have shown relatively strong nonlinear relationships with model output, respectively. The CO2 interaction temperature had the smallest impact on coal strength alteration induced by CO2 exposure based on both Pearson and Spearman correlation coefficients. Finally, the leverage technique revealed that the laboratory database used for modeling CO2-induced strength alteration of coals was highly reliable, and the suggested GPR-Rational Quadratic model and GMDH correlation could be applied for predicting the UCS of coals exposed to CO2 with high statistical accuracy and reliability.

The integrated fracturing and oil recovery strategy is a new paradigm for achieving sustainable and cost-effective development of unconventional reservoirs. However, a single type of working fluid cannot simultaneously meet the different needs of fracturing and oil displacement processes. Here, we develop a pH-responsive fracturing-displacement integrated working fluid based on the self-assembled micelles of N,N-dimethyl oleoamine propylamine (DOAPA) and succinic acid (SA). By adjusting the pH of the working fluid, the DOAPA and SA molecules can be switched repeatedly between highly viscoelastic wormlike micelles and aqueous low-viscosity spherical micelles. The zero-shear viscosity of the working fluid enriched the wormlike micelles can reach more than 93,100 mPa·s, showing excellent viscoelasticity and sand-carrying properties. The working fluid is easy to gel-break when it encounters oil, generating a low-viscosity liquid without residue. In addition, the system has strong interfacial activity, which can greatly reduce the oil-water interfacial tension to form emulsions and can achieve reversible demulsification and re-emulsification by adjusting pH. Through the designed and fabricated microfluidic chip, it can be visualized that under the synergistic effect of viscoelasticity and interfacial activity DOAPA/SA can effectively expand the swept volume of tight fractured formations, promote pore wetting reversal and crude oil emulsification, and improve the displacement efficiency. The DOAPA/SA meets the design requirements of the fracturing-displacement integrated working fluids and provides a novel method and idea for constructing the integrated working fluids suitable for fracturing and displacement in unconventional reservoirs.

2020 just beginning, the economic has been sluggish especially hospitality and tourism industry because the attacked of public health emergency-COVID-19. The situation has gradually improved at the end of March, companies have various emergency responses and business strategies, most of business are work resumption. Human resource is the prime productivity in service intensive industry, take care of staff mental health and improve staff morale are as important as making new business strategy in hospitality and tourism industry. Mental health is measured with psychological well-being six dimensions [1]. Morale includes three dimensions: affective, future/goal and interpersonal [2]. Company emergency responses and business strategies are summary from interview with the management from hospitality and tourism. 400 respondents from hospitality and tourism industry in Guangdong province, Macao, and Hainan Island, the most three popular tourism destinations. Psychological well-being has significant positive affect on staff morale. The satisfaction of emergency responses and actives as a moderating variable.