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Shale oil reservoirs, as an unconventional hydrocarbon resource, have the potential to substitute conventional hydrocarbon resources and alleviate energy shortages, making their exploration and development critically significant. However, due to the low permeability and the development of nanopores in shale reservoirs, shale oil production is challenging and recovery efficiency is low. During the imbibition stage, fracturing fluid displaces the oil in the pores primarily under capillary forces, but the complex pore structure of shale reservoirs makes the imbibition mechanism unclear. This research studies the imbibition flow mechanism in nanopores based on the capillary force model and two-phase flow theory, coupled with numerical simulation methods. The results indicated that within a nanopore diameter range of 10–20 nm, increasing the pore diameter leads to a higher imbibition displacement volume. Increased pressure can enhance the imbibition displacement, but the effect diminishes gradually. Under the water-wet conditions, the imbibition displacement volume increases as the contact angle decreases. When the oil phase viscosity decreases from 10 mPa·s to 1 mPa·s, the imbibition displacement rate can increase by 72%. Moreover, merely increasing the water phase viscosity results in only a 5% increase in the imbibition displacement rate. The results provide new insights into the imbibition flow mechanism in nanopores within shale oil reservoirs and offer a theoretical foundation and technical support for efficient shale oil development. [ABSTRACT FROM AUTHOR]

Gulong shale oil is found in a typical continental shale oil reservoir, which is different from marine shale oil reservoirs. The Gulong shale oil reservoir is a pure shale-type oil reservoir with abundantly developed nanoscale pores, making it extremely difficult to unlock fluids. Pressure drive does not easily achieve fluid unlock conditions; thus, it is necessary to utilize imbibition to unlock nanoscale pore fluids. In this study, experiments were conducted on oil displacement by high-speed centrifugal pressure and imbibition under different conditions, respectively, and simulations were used to evaluate the effects of pressure differential drive and imbibition efficiency on the utilization of crude oil following fracturing. Combined with the mixed wettability of the reservoir, the imbibition efficiency was analyzed, and the imbibition efficiency at different soaking stages was evaluated. When the fracturing pressure was higher than the matrix pore pressure, the imbibition efficiency was the most obvious, which was 27.9%. Spontaneous imbibition depending solely on capillary force had poor efficiency, at 16.8%. When the fracturing pressure was lower than the matrix pore pressure, the imbibition efficiency was the lowest, at only 1.3%. It is proposed that strengthening fracture pressure and promoting pressurized imbibition are the keys to improving shale oil development. [ABSTRACT FROM AUTHOR]

The pore structure of shale oil reservoirs is complex, and the microscale and nanoscale effect is obvious in the development of shale oil reservoirs. Understanding the oil flow mechanism in shale reservoirs is essential for optimizing the development plan and enhancing the recovery rate of shale oil reservoirs. In this review, we briefly introduce the occurrence status of shale oil and shale oil flow in the inorganic matrix and the organic matrix (including the shrinkage of kerogen, oil diffusion in kerogen, oil transport in the organic pore channels, coupling of diffusion, and fluid transport in the organic matrix). Then, the shale oil microflow simulation and a coupling model of double-porous media for microflow and macroflow in the production process of shale oil are discussed. Finally, we summarize the main conclusions and perspectives on the oil flow mechanism and numerical simulations in shale oil reservoirs. An accurate description of shale oil occurrence status and shale oil flow in the inorganic and organic matrices is crucial for the numerical simulation of shale oil reservoirs. It can provide a basis and reference for the future directions of shale oil flow and numerical simulations during the development of shale oil reservoirs. [ABSTRACT FROM AUTHOR]

Lithofacies identification and classification are critical for characterizing the hydrocarbon potential of unconventional resources. Although extensive applications of machine learning models in predicting lithofacies have been applied to conventional reservoir systems, the effectiveness of machine learning models in predicting clay-rich, lacustrine shale lithofacies has yet to be tackled. Here, we apply machine learning models to conventional well log data to automatically identify the shale lithofacies of Gulong Shale in the Songliao Basin. The shale lithofacies were classified into six types based on total organic carbon and mineral composition data from core analysis and geochemical logs. We compared the accuracy of Multilayer Perceptron (MLP), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Random Forest models. We mitigated the bias of imbalanced data by applying oversampling algorithms. Our results show that ensemble methods (XGBoost and Random Forest) have a better performance in shale lithofacies identification than the other models do, with accuracies of 0.868 and 0.884, respectively. The organic siliceous shale proposed to have the best hydrocarbon potential in Gulong Shale can be identified with F1 scores of 0.853 by XGBoost and 0.877 by Random Forest. Our study suggests that ensemble machine learning models can effectively identify the lithofacies of clay-rich shale from conventional well logs, providing insight into the sweet spot prediction of unconventional reservoirs. Further improvements in model performances can be achieved by adding domain knowledge and employing advanced well log data. [ABSTRACT FROM AUTHOR]

In recent years, CO2 huff and puff has become one of the most important methods developed for unconventional shale oil reservoirs and has been widely used in all major shale oil fields. However, the microscopic mechanism of CO2 contacting with crude oil is complex, and the change law of the residual oil occurrence after CO2 injection is unclear. In this paper, a micro visualization fluid flow simulation experiment (microfluidic experiment) under high temperatures and high pressure of a shale reservoir was conducted to reveal the micro mechanism of CO2 and crude oil after contact at the microscale. This allows conclusion of more precise results than any experiment conducted in a room environment. Combined with gas–oil two-phase micro flow characteristics, the production mechanisms of crude oil by CO2 huff and puff at the pore scale are clarified, and the change characteristics of the remaining oil occurrence state after CO2 injection are quantified. The results show that CO2 mainly produces crude oil in macropores and microfractures in the injection stage of huff and puff, improves the mobility of crude oil through diffusion dissolution in the soaking stage, and that the driving of dissolved gas is dominant in depressurization production. The major micro-mechanisms for CO2 to improve shale oil are extraction and dissolution expansion, accompanied by a variety of secondary mechanisms, such as the miscibility effect, oil expansion, viscosity reduction and other contact effects, as well as the improvement of crude oil properties. The simulation results of huff and puff development show that soaking is an important stage to enhance oil recovery. With increasing soaking time or the soaking pressure, the recovery degree of crude oil will increase positively. [ABSTRACT FROM AUTHOR]

Vocal loading tasks are often used to investigate the relationship between voice use and vocal fatigue in laboratory settings. The present study investigated the concept of a novel quantitative dose-based vocal loading task for vocal fatigue evaluation. Ten female subjects participated in the study. Voice use was monitored and quantified using an online vocal distance dose calculator during six consecutive 30-min long sessions. Voice quality was evaluated subjectively using the CAPE-V and SAVRa before, between, and after each vocal loading task session. Fatigue-indicative symptoms, such as cough, swallowing, and voice clearance, were recorded. Statistical analysis of the results showed that the overall severity, the roughness, and the strain ratings obtained from CAPE-V obeyed similar trends as the three ratings from the SAVRa. These metrics increased over the first two thirds of the sessions to reach a maximum, and then decreased slightly near the session end. Quantitative metrics obtained from surface neck accelerometer signals were found to obey similar trends. The results consistently showed that an initial adjustment of voice quality was followed by vocal saturation, supporting the effectiveness of the proposed loading task. [ABSTRACT FROM AUTHOR]

The purpose of this study was to investigate the feasibility of using neck-surface acceleration signals to discriminate between modal, breathy and pressed voice. Voice data for five English single vowels were collected from 31 female native Canadian English speakers using a portable Neck Surface Accelerometer (NSA) and a condenser microphone. Firstly, auditory-perceptual ratings were conducted by five clinically-certificated Speech Language Pathologists (SLPs) to categorize voice type using the audio recordings. Intra- and inter-rater analyses were used to determine the SLPs' reliability for the perceptual categorization task. Mixed-type samples were screened out, and congruent samples were kept for the subsequent classification task. Secondly, features such as spectral harmonics, jitter, shimmer and spectral entropy were extracted from the NSA data. Supervised learning algorithms were used to map feature vectors to voice type categories. A feature wrapper strategy was used to evaluate the contribution of each feature or feature combinations to the classification between different voice types. The results showed that the highest classification accuracy on a full set was 82.5%. The breathy voice classification accuracy was notably greater (approximately 12%) than those of the other two voice types. Shimmer and spectral entropy were the best correlated metrics for the classification accuracy. [ABSTRACT FROM AUTHOR]