The flow of homogeneous fluids through porous media: analogies with other physical problems

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Simple kinetic models of petroleum formation. Part II: oil-gas cracking

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Principles Of Heat Transfer In Porous Media

Mechatronics Technology is a growing career field that deals with the integration of mechanical and electronic components managed by control systems. Mechatronics technicians troubleshoot, maintain and repair mechanical equipment controlled by electrical, electronic and computer systems. These types of systems are increasingly used in a wide variety of manufacturing and industrial settings. Clark College's Mechatronics Technology (MTX) classes emphasize current concepts and technology by providing practical, hands-on experiences with the latest, industry standard equipment. In addition to the technical knowhow needed to maintain and repair equipment, the certificate and degree programs will help prepare students to think critically, function as a successful team member and communicate clearly too internal and external customers.

Mechatronics기술개발 현황과 과제

Abstract Variable-order (VO) fractional differential equations (FDEs) with a time (t), space (x) or other variables dependent order have been successfully applied to investigate time and/or space dependent dynamics. This study aims to provide a survey of the recent relevant literature and findings in primary definitions, models, numerical methods and their applications. This review first offers an overview over the existing definitions proposed from different physical and application backgrounds, and then reviews several widely used numerical schemes in simulation. Moreover, as a powerful mathematical tool, the VO-FDE models have been remarkably acknowledged as an alternative and precise approach in effectively describing real-world phenomena. Hereby, we also make a brief summary on different physical models and typical applications. This review is expected to help the readers for the selection of appropriate definition, model and numerical method to solve specific physical and engineering problems.

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A review on variable-order fractional differential equations: mathematical foundations, physical models, numerical methods and applications


 Surfactants are commonly used in chemical enhanced oil recovery (cEOR). The quantity of surfactant loss due to adsorption on a rock directly influences a cEOR project economics. Therefore, surfactant adsorption quantification is an important area of interest. Surfactant adsorption is greatly influenced by the mineral composition present in the rock. This paper presents a novel machine learning (ML) intelligent model to predict surfactant adsorption as a function of mineral composition, maximum adsorption capacity, and surfactant concentration. Several pure minerals were used to determine the static adsorption of a novel cationic Gemini surfactant. The novel surfactant is compatible with high salinity and high-temperature environment. XRD was utilized to show the percentage of the rock-forming minerals. The solid-liquid ratio used in this study was 1 gm in 15 ml, and the time given for rock fluid interaction was 24 hours. The supernatants obtained after 24 hours of shaking and 20 minutes of centrifuging were analyzed using high-performance liquid chromatography to determine the remaining surfactant concentration. ML algorithms were applied to the dataset to predict surfactant adsorption. Hyperparameters tuning was performed using K-fold cross-validation integrated with an exhaustive grid search technique.
 Surfactant adsorption isotherms were constructed from the real experimental data for each pure mineral. The dataset was divided into an 80:20 ratio for training and testing, respectively. Extreme Gradient Boosting (XGBoost) and random forest (RF) techniques were applied to the training dataset to predict the surfactant adsorption as a function of mineral composition, maximum adsorption capacity, and surfactant concentration. The remaining 20% of the dataset was used to test the models. The evaluation error metrics comprising R2 and RMSE showed good agreement of predictions with the unseen data. Also, it was found that XGBoost outperformed RF in surfactant adsorption predictions with R2 of 0.9914 and 0.8990, respectively. The developed model can be used to predict surfactant adsorption by using mineral composition and surfactant concentration. The developed model saves a significant amount of time in running the tedious and time-consuming experiments and helps to provide a good quick estimate of surfactant adsorption. This model will add a great value in the practical application of a chemical EOR project.

Soft Computing Approach for the Prediction of Surfactant Adsorption


 In a move towards development of sustainable and efficient hydrocarbon production, the industry looks forward to the deployment of carbon neutral and even carbon negative solutions. Accordingly, CO2 EOR is a viable option to improve recovery and has been applied in mature fields for over four decades. The downsides of poor sweep efficiency linked to viscous fingering and gravity segregation can be sorted through generation of CO2 foams in the reservoir. This work proposes the utilization of machine learning techniques, to predict foam flood performance which will thereby aid in optimization of laboratory core-flood experiments.
 This work is based upon consumption of large set of existing laboratory data collected from literature, amounting to more than 200 data points. The dataset reports core oil recovery factor as a function of three reservoir parameters including porosity, permeability, initial oil saturation. While injected foam volume and total pore volume are also considered. Furthermore, the data records contain experiments for various foaming agent types which are catered for during the machine learning model development through the implementation of numerical tags. The input data is then divided in training subset for development of XGBoost model, complemented by integration of exhaustive grid search and k-fold cross validation techniques. Subsequently, the testing subset is reserved to measure efficacy of the developed model. The model development process involves tuning of machine learning algorithm hyperparameters which control the resultant accuracy, while at the same time it is ensured that the issue of model overfitting is avoided. Testing of the established model is carried out through an array of statistical measures including the R2 and RMSE values.
 The proposed model is compared with actual experimental data. The machine learning model can achieve high accuracy in predictive mode for the output parameters. Through statistical error analysis performance measurement, it is observed that the machine learning model can predict CO2 foam flood performance with high R2 of around 0.99 and low errors. The excellent accuracy of the XGBoost model is credited to the complex processing involved with intelligent algorithms that can discover underlying relationships among the input variables.

Machine Learning for Prediction of CO2 Foam Flooding Performance


 Surfactants are widely employed in chemical enhanced oil recovery (cEOR) technique. The economics of a cEOR project is directly impacted by the amount of surfactant loss caused by adsorption on a rock. Therefore, surfactant adsorption reduction is imperative. Both static and dynamic adsorption experiments were conducted to test the adsorption reduction of a novel Gemini surfactant on Indiana limestone. This novel surfactant is tolerant to high-salinity and high-temperature environments.
 Low salinity water was made by diluting sea water ten times. The salinity of Low salinity water was 6771 ppm. Rock characterization was performed first using XRD. Static adsorption tests were run using a crushed rock sample. Whereas core flood experiments were conducted to determine the dynamic adsorption behavior. High-performance liquid chromatography integrated with an evaporative light scattering detector was employed to calculate the unknown concentration of the surfactant.
 The effect of both high and low salinity water along with Gemini surfactant was investigated on the static adsorption of Gemini surfactant on Indiana limestone. It was shown that high salinity conditions result in the adsorption reduction in comparison with Gemini surfactant in deionized water. However, the use of low salinity water in the aqueous solution of Gemini surfactant further results in reducing surfactant adsorption. Dynamic adsorption test on Indiana limestone was found consistent with static tests. The ultimate reduced adsorption value of Gemini surfactant on Indiana limestone was found to be 0.11 mg/g-rock using low salinity conditions in dynamic experiments. Such low value lies under the economic limit, making a chemical EOR process efficient and economical. The novelty of this work is the use of low-salinity water in reducing the adsorption of a Gemini surfactant on Indiana limestone. The use of such a technique helps industrialists and researchers in designing an efficient and economical chemical EOR process.

Reducing Adsorption of a Gemini Surfactant on Carbonate Rocks Using Low Salinity Water


 Surfactants play a vital role in chemical enhanced oil recovery (cEOR) to improve oil production by lowering the oil-water interfacial tension and/or altering the rock wettability. However, surfactant adsorption has been a great challenge. The quantity of surfactant loss by adsorption on a rock directly influences a cEOR project’s economics. Therefore, surfactant adsorption minimization is an important area of interest, which is investigated in this paper. Saudi carbonate rock (outcrop) was tested with a novel in-house synthesized cationic Gemini surfactant to quantify surfactant adsorption. This novel surfactant is compatible with high salinity and high-temperature environment. Rock characterization was performed first using XRD and SEM analyses, while the point of zero charge of Saudi carbonate was found using the pH drift method. Static adsorption tests were conducted using powdered rock sample and run for 24 hours to achieve the equilibration time. The material balance method was used to determine surfactant adsorption. High-performance liquid chromatography along with an evaporative light scattering detector was utilized to quantify the remaining surfactant concentration post-adsorption. Adsorption isotherm modeling was also performed to investigate the adsorption mechanism.
 Rock characterization results showed that Saudi carbonate contains mainly dolomite along with some impurities like quartz and clay minerals. The point of zero charge of Saudi carbonate determined using the pH drift method was around 10. The static adsorption tests were conducted on both pure and Saudi dolomites to investigate the influence of impurities. It was found that the presence of quartz and clay particles significantly impacts the degree of surfactant adsorption on Saudi carbonates. A high adsorption of the novel Gemini surfactant used was obtained on Saudi carbonate (8.2 mg/g-rock). A powerful chelating agent made from natural, biodegradable, and renewable material was added to the surfactant solution to check its impact. The surfactant adsorption was significantly decreased using low pH chelating agent (5.8 mg/g-rock). It is proposed that low pH chelating agent renders the overall rock surface more positive and results in electrostatic repulsion between like charges of rock and surfactant. Therefore, the addition of a small quantity of the investigated chelating agent to the surfactant solution helps in reducing the cationic Gemini surfactant adsorption on Saudi carbonate. This study reports a novel strategy to minimize surfactant adsorption on Saudi carbonate through the use of an environmentally friendly and low-cost chelating agent, which will help in designing future chemical EOR projects.

Sidqi A. Abu-Khamsin

Papers

Soft Computing Approach for the Prediction of Surfactant Adsorption

Machine Learning for Prediction of CO2 Foam Flooding Performance

Reducing Adsorption of a Gemini Surfactant on Carbonate Rocks Using Low Salinity Water

A New Strategy to Minimize the Surfactant Adsorption on Saudi Carbonate