Chemical-enhanced oil recovery (cEOR) is a class of techniques commonly used to extract hydrocarbon fluids from reservoir rocks beyond conventional waterflooding. Surfactants are among the chemical agents employed in a cEOR process, as they aid in enhancing oil recovery by lowering the oil–water interfacial tension (IFT) and altering the rock wettability toward less oil-wet conditions. Understanding the flow characteristics and mechanisms involved during surfactant flooding helps improve the performance of injected surfactants and results in higher oil recovery. The objective of this review is to outline the recent applications of the different methods employed to understand the behavior and mechanisms involved during surfactant-enhanced oil recovery. The review begins with a general background highlighting the basic characteristics of surfactants and the main mechanisms by which they exert their influence. Recent studies conducted to investigate the oil recovery performance through different methods are then presented, including traditional coreflooding experiments, microfluidics studies, and oil recovery through sand packs. The methodology of the analysis and the interpretation of the data obtained from the different oil recovery tests, including oil recovery factor, pressure data, and relative permeability, are also described. Pore-scale analysis and imaging methods including nuclear magnetic resonance (NMR), magnetic resonance imaging (MRI), and X-ray medical and microcomputed tomography (μCT) scanning and their applicability in assessing the recovery performance are described. Finally, a few examples of field monitoring methods for surfactant flooding are highlighted. This review provides knowledge of the different multiscale evaluation methods and their applicability during surfactant flooding.
Continuous monitoring of the rheological properties of the drilling mud is essential so that any drilling operation can be completed more effectively and efficiently with the least problems. Mud rheological properties play a vital role in the in the efficiency of the drilling fluid to lift the cuttings from the wellbore. The mud rheological properties include the plastic viscosity, apparent viscosity, and the yield point. However, these properties are not measured continuously during the drilling process and they are only measured once or twice a day while other mud properties, such as the mud weight, the Marsh funnel viscosity, and solid content, are measured regularly and continuously. Therefore, it is valuable to come up with a relation that relates the mud rheological properties to these parameters. Many researchers tried to introduce models that allow for the prediction of the apparent viscosity from the Marsh funnel viscosity. However, these models have the deficiency that the prediction is with high errors. For the first time, the solid percent was used to predict the rheological properties of the oil-based drilling fluid based on the artificial neural network using actual field measurements. The purpose of this study is to use the Artificial Neural Networks (ANN) Technique to develop a model that allows the prediction of the mud rheological properties such as the plastic viscosity, apparent viscosity, the rheometer readings at 600 and 300 rpm and the flow behavior index for oil-based mud from the mud weight, the Marsh funnel viscosity and solid content. The study is based on 400 data points collected from the field measurements of actual drilling fluid samples. The obtained results showed that the five developed models using ANN technique can be used to predict the rheological properties of oil- based drilling fluid with a high accuracy; the average absolute error was less than 5% and the correlation coefficient was higher than 90%. The developed technique is inexpensive with no additional required equipment. It will help the drilling engineers to calculate the equivalent circulation density, surge and swab pressures, and hole cleaning which are strong functions of the rheological parameters in a real time. The method and approach used in this paper to predict and determine the unknown drilling fluid properties and trend out of accurately defined parameters is futuristic and progressive. The method is one step forward toward automating the drilling fluid system which is another step forward toward fully automating the drilling process overall.
Improper hole cleaning or drilled-cutting transportation impacts drilling operations. To illustrate, inefficient cleaning of the wellbore can lead to many drilling problems such as low drilling rate (i.e. low ROP), early bit wear and, in the severe cases, a complete loss of the well due to stuck pipe. To understand efficiency in cutting transport in drilling and to provide solutions for the problem, many studies have been conducted. In all cases, they provide empirical models based on experimental data. In this study, different artificial intelligence (AI) techniques are employed to estimate the concentration of cuttings present in the wellbore. The purpose of this study is to indirectly measure the hole-cleaning efficiency in order to predict the cutting concentration from drilling parameters using artificial intelligence techniques. The study is based on 116 experimental data records from the studies. Two AI techniques were selected, namely artificial neural network (ANN) and support vector machine (SVM), to estimate the cutting concentration in the wellbore. The input parameters comprise mud density and mud rheological properties (yield point and plastic viscosity) in addition to drilling parameters including the hole inclination angle, pipe eccentricity (i.e. location of the drill pipe from the axis of the well), the rate of penetration (ROP), flow rate (GPM), drill pipe rotary speed (RPM) and temperature. The results obtained show the ability of the two employed techniques to accurately predict the cutting concentration in the wellbore with average absolute errors (AAE) less than 5% and correlation coefficients (R) higher than 0.9. Comparison of these results with a literature model showed that the AI techniques provide better predictions of cutting concentration and higher accuracy than that model. Applying the developed AI technique will help the drilling engineers to assess the hole cleaning in a real time.
Hole cleaning, or drilled-cutting transportation, is one of the main concern in the petroleum industry. This is due to the high impact of improper downhole cleaning during drilling operations. To illustrate, many drilling problems can happen because of inefficient cleaning of the wellbore. These problems may include premature bit wear, slow drilling rate (i.e. low ROP) and, in most severe cases, a stuck pipe which in some cases can lead to complete loss of the well. Moreover, measuring the cleaning efficiency using field or experimental measurements is highly costly and time-consuming which makes it a very complicated problem. Therefore, a lot of studies have been conducted to understand cutting transport efficiency in drilling operations. However, most of these studies are experimental and try to come up with the best measures including experimental models or empirical correlations. In this study, artificial intelligence techniques were used to estimate the cutting concentration in the wellbore. The purpose of this study is to use Support Vector Machine (SVM) technique to indirectly measure the hole cleaning efficiency by predicting the cutting concentration in the wellbore from other operational parameters. Based on 116 experimental data points collected from the literature, the cutting concentration in the borehole was predicted from the properties of the mud itself such as the mud rheological properties (e.g. yield point and plastic viscosity) and mud density (mud weight) and other operational parameters during drilling including the drill pipe rotary speed (RPM), pipe eccentricity (i.e. the axial location of the drill pipe), hole inclination angle, the rate of penetration (ROP), flow rate (GPM), temperature and annular size. The results obtained from SVM show the ability of this method to accurately predict the cutting concentration in the wellbore with an average absolute error (AAE) of less than 5% and a correlation coefficient (R) of more than 0.9. For this specific group of data, comparing the results obtained from this technique with a correlation presented in the literature shows that the SVM method provides a better prediction of cutting concentration and higher accuracy than that in the literature. Finally, the method developed in this study to predict the cutting concentration is based on continuously measured parameters during any drilling operation. Therefore, integration of the developed model into the drilling system will allow for real-time prediction of the concentration of the cuttings (i.e. the amount of cuttings present in the wellbore) and, hence, the cleaning efficiency during the drilling operation.
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