Expanded Salinity Window of Middle-Phase Microemulsions and Reduced Surfactant Adsorption by Hydrotrope

Yuan, Na; Wang, Shuoshi; Yu, Ying; Chen, Changlong; Shiau, Bor-Jier; Harwell, Jeffrey H.

doi:10.1021/acs.langmuir.3c02211

Cited by 3 publications

(2 citation statements)

References 54 publications

(119 reference statements)

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“…It enables evaluation in diverse improved oil recovery operations, including water injection, gas injection, and chemical flooding . In recent years, unconventional oil and gas have emerged as significant alternative resources, which led research in secondary and tertiary hydrocarbon recovery methods. − However, in experiments involving the development of unconventional reservoirs, core saturation was typically achieved through pressurized soaking. ,− When tight rock cores are saturated with the flow-through method, fluids tend to flow into preferential pathways and leave some pore spaces unsaturated, even with a high back pressure. Therefore, the flow-through type of saturation method cannot thoroughly saturate the tight core, leading to an unrepresentative initial condition for the experiment.…”

Section: Introductionmentioning

confidence: 99%

HLD-Based Formulation Prediction for Degassed Oil with Equivalent Minimum Miscibility Pressure

Wang,

Zhao,

Guo

et al. 2024

Energy Fuels

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Shale cores, with their ultralow permeability, often require pressurization for saturation with target fluids, as flowthrough saturation is inefficient. This saturation method, when applied by using live oil, leads to fluid loss during core transfer and system vacuuming in subsequent experimental steps. As a result, most shale-related experiments utilize degassed oils, typically dead oil or kerosene. However, researchers account for only density or viscosity similarities in degassed oil preparation, missing key aspects of the gas injection mechanism. This study focuses on the representativeness of degassed oil and develops a procedure to prepare degassed surrogate oil that accurately replicates the live oil's minimum miscibility pressure (MMP). We propose a predictive oil formulation procedure based on thermodynamic similarities between oil/gas miscible systems and oil/water middle-phase microemulsion systems under the hydrophilic−lipophilic deviation (HLD) theory. This procedure, validated with real reservoir oil samples, uses liquid light hydrocarbons to reduce the equivalent alkane carbon number (EACN) of dead oil for surrogate oil preparation. EACN for dead, live, and surrogate oils was determined through slim tube and microemulsion phase behavior experiments. Light hydrocarbon addition quantities were estimated by linear EACN mixing. The surrogate oil's MMP, tested against CO 2 in slim tube experiments and verified through salinity scanning phase behavior experiments, was 27.83 MPa, closely matching the live oil's MMP of 28.15 MPa. These results validate the predictivity of our procedure, eliminating trial and error in adjusting light hydrocarbon volumes. The surrogate oil's lack of dissolved gases addresses fluid loss issues during rock core transfers, solving saturation and transfer challenges in tight rock core experiments.

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Section: Introductionmentioning

confidence: 99%

HLD-Based Formulation Prediction for Degassed Oil with Equivalent Minimum Miscibility Pressure

Wang,

Zhao,

Guo

et al. 2024

Energy Fuels

Self Cite

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“…As an important chemical additive, foaming agents are widely used in the development of oil and gas fields to improve the production and recovery of oil and gas wells. − The application of foaming agents in the field of drainage gas recovery can be traced back to the 1950s . By the 1980s, the United States and the Soviet Union had conducted foam drainage gas recovery on a large scale, and China also began to implement the process in Sichuan .…”

Section: Introductionmentioning

confidence: 99%

Study on Dynamic Liquid-Carrying Process of Foaming Agent and Establishment of Mathematical Model

Liu,

Luo

et al. 2024

Langmuir

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The efficiency of foam drainage gas recovery is predominantly dictated by the performance of the foaming agent. To better understand their behavior, a novel testing apparatus was developed to simulate the foam drainage gas recovery process within the wellbore. Through the dynamic liquid-carrying performance tests of four foaming agents under uniform conditions, it was discerned that there existed significant disparities in the liquidcarrying performance and action duration. Further interface performance analysis disclosed that the liquid-carrying capacity and the duration were correlated with their adsorption capacity and interface activity at the gas−liquid interface. Notably, foaming agents with lower adsorption capacity and higher interfacial activity demonstrated superior liquid-carrying performance and longer action duration. By analyzing the consumption of foaming agents during the liquid-carrying process, five dynamic liquid-carrying equations were derived based on first-order reaction kinetics, the Malthusian population model, and the logistic function. The outcomes demonstrated that all these five equations could precisely delineate the dynamic liquid-carrying process of the foaming agent. During the research, we found that the consumption of the foaming agent in the foam drainage gas recovery process is related to its adsorption behavior at the gas−liquid interface, and revealed that the dynamic liquid-carrying process of foaming agent is the increasing process of liquid-carrying capacity under the continuous consumption of limited foaming agent resources. This laid a foundation for the further exploration of the functional mechanism of the foaming agent in the foam drainage gas recovery process.

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Leveraging Machine Learning for Predicting Gas Separation Efficiency of a Downhole Separator

Sharma,

Osorio,

Ashesh

et al. 2024

Day 1 Mon, April 22, 2024

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Artificial lift systems, specifically Electrical Submersible Pumps and Sucker Rod Pumps, often face operational challenges due to high Gas-Oil Ratio, leading to premature tool failure and increased non-productive time. These issues underscore the necessity for efficient gas separation at upstream of the artificial lift pump to ensure uninterrupted operations. Downhole gas separators, situated upstream of the artificial lift pump, are designed to segregate gas from the fluid, reducing downtime. This study aims to predict the gas separation efficiency of the downhole gas separator using experimental data from a centrifugal separator and testing the generated models on experimental data for a gravity separator. To predict the separation efficiency of the downhole separator, multiple machine learning models were evaluated, and a comparative analysis was performed. The experimental test data obtained for the centrifugal packer-type separator was used for model training and experimental test data for the gravity separator was used for model testing (blind test). To conduct experiments, multiphase flow setup (31ft. (9.4m) long horizontal and 27ft. (8.2m) tall vertical section of multiphase flow setup) was used. In this study, seven regression methods, including multilinear (MLR), random forest (RF), support vector machine (SVM), ridge, lasso, k-nearest neighbor (KNN) and XGBoost, were examined to assess their ability to effectively predict the gas separation efficiency. Several performance metrics, including Root mean square error (RMSE), Mean absolute percentage error (MAPE) and R-squared were evaluated to select the efficient model. In-depth exploratory data analysis and feature engineering were performed to identify significant input parameters for model training. The scatterplot and correlation plot indicated the response variable were mostly affected by inlet liquid and gas volume flow. Therefore, liquid and gas volume flow at inlet were selected as two independent parameters to predict gas volume flow per minute at the outlet (GVFO) as the response variable. After data scaling, a comparative analysis of the seven incorporated regression models revealed that, the Random Forest model to be the most efficient for predicting GVFO, closely followed by the KNN model. The Random Forest model exhibits R-squared values of 96% and RMSE value of 112. Hyperparameter tuning and tenfold cross-validation ensured optimal values for tuning parameters and prevented oveffitting, respectively. This developed machine learning workflow enables the prediction of the gas separation efficiency of different type of downhole gas separator by using experimental test data from a centrifugal downhole separator. It provides critical findings through a comparative analysis of seven regression models. This research offers valuable insights that can aid in optimizing the performance of artificial lift systems.

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Expanded Salinity Window of Middle-Phase Microemulsions and Reduced Surfactant Adsorption by Hydrotrope

Cited by 3 publications

References 54 publications

HLD-Based Formulation Prediction for Degassed Oil with Equivalent Minimum Miscibility Pressure

HLD-Based Formulation Prediction for Degassed Oil with Equivalent Minimum Miscibility Pressure

Study on Dynamic Liquid-Carrying Process of Foaming Agent and Establishment of Mathematical Model

Leveraging Machine Learning for Predicting Gas Separation Efficiency of a Downhole Separator

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