Application of Gene Expression Programming (GEP) in Modeling Hydrocarbon Recovery in WAG Injection Process

Water alternating gas (WAG) injection has been successfully applied as a tertiary recovery technique. Forecasting WAG flooding performance using fast and robust models is of great importance to attain a better understanding of the process, optimize the operational conditions, and avoid high-cost blind tests in laboratory or pilot scales. In this study, we introduce a novel correlation to determine the performance of the near-miscible WAG flooding in strongly water-wet sandstones. We conduct dimensional analysis with Buckingham’s π theorem technique to generate dimensionless numbers using eight key parameters. Seven dimensionless numbers are employed as the input variables of the desired correlation for predicting the recovery factor of a near-miscible WAG injection. A verified mathematical model is used to generate the required training and testing data for the development of the correlation using a gene expression programming (GEP) algorithm. The provided data points are then separated into two subsets: training (67%) to develop the model and testing (33%) to assess the models’ capability. Conducting error analysis, statistical measures and graphical illustrations are provided to assess the effectiveness of the introduced model. The statistical analysis shows that the developed GEP-based correlation can generate target data with high precision such that the training phase leads to R2 = 92.85% and MSE = 1.38 × 10−3 and R2 = 91.93% and MSE = 4.30 × 10−3 are attained for the testing phase. The relative importance of the input dimensionless groups is also determined. According to the sensitivity analysis, decreasing the oil–water capillary number results in a significant reduction in RF in all cycles. Increasing the magnitudes of oil to gas viscosity ratio and oil to water viscosity ratio lowers the RF of each cycle. It is found that oil to gas viscosity ratio has a higher impact on RF value compared to oil to water viscosity ratio due to a higher viscosity gap between the gas and oil phases. It is expected that the GEP, as a fast and reliable tool, will be useful to find vital variables including relative permeability in complex transport phenomena such as three-phase flow in porous media.

Prediction of Gas Viscosity of Yemeni Gas Fields Using Machine Learning Techniques

Gas viscosity is an important physical property that controls and influences the flow of gas through porous media and pipe networks. An accurate gas viscosity model is essential for use with reservoir and process simulators. The objective of this study is to assess the predictability of gas viscosity of Yemeni gas fields using machine learning techniques. Performance of some machine learning techniques in the prediction of gas viscosity investigated in this work. The techniques include K-nearest neighbors (KNN), Random Forest (RF), Multiple Linear Regression (MLR), and Decision Tree (DT). About 440 data points were collected from different Yemeni gas fields were used to develop the machine-learning model. The input data used in the training include pressure, temperature, gas density, specific gravity, gas formation volume factor, gas deviation factor, gas molecular weight, pseudo-reduced temperature and pressure, pseudo-critical temperature and pressure, and non-hydrocarbon gas components (N2, CO2, and H2S). Part of the data (75%) was used to train the developed models using the algorithms while another part of the data (25%) was used to predict the viscosity of gas for samples. Trained machine learning models were constructed using the Python programming language. The performance and accuracy of the machine learning models were tested and compared their results based on four different functional input datasets. The result of this study found that that the DT model predicted the gas viscosity with higher accuracy, and gave very good results better than other models based on input parameters of the dataset (A) and (B). This was evidenced by lower the Root mean square error (0.000832), lower mean absolute percent relative error (0.042%), and higher coefficient of determination (R2=0.9465). The proposed approach in the present study provides an accurate and inexpensive model for estimating the viscosity of gases as a function of all input parameters of the dataset (A). Overall, the relative effects of these different input parameters have verified that the gas viscosity has the uppermost relevant to the gas density and specific gravity that have the highest percentage of 51%.

Modeling Immiscible Fluid Displacement in a Porous Medium Using Lattice Boltzmann Method

The numerical investigation of the interpenetrating flow dynamics of a gas injected into a homogeneous porous media saturated with liquid is presented. The analysis is undertaken as a function of the inlet velocity, liquid–gas viscosity ratio (D) and physical properties of the porous medium, such as porous geometry and surface wettability. The study aims to improve understanding of the interaction between the physical parameters involved in complex multiphase flow in porous media (e.g., CO2 sequestration in aquifers). The numerical simulation of a gaseous phase being introduced through a 2D porous medium constructed using seven staggered columns of either circular- or square-shaped micro-obstacles mimicking the solid walls of the pores is performed using the multiphase Lattice Boltzmann Method (LBM). The gas–liquid fingering phenomenon is triggered by a small geometrical asymmetry deliberately introduced in the first column of obstacles. Our study shows that the amount of gas penetration into the porous medium depends on surface wettability and on a set of parameters such as capillary number (Ca), liquid–gas viscosity ratio (D), pore geometry and surface wettability. The results demonstrate that increasing the capillary number and the surface wettability leads to an increase in the effective gas penetration rate, disregarding porous medium configuration, while increasing the viscosity ratio decreases the penetration rate, again disregarding porous medium configuration.

Role of Gas Viscosity for Shale Gas Percolation

Viscosity is an important index to evaluate gas flowability. In this paper, a double-porosity model considering the effect of pressure on gas viscosity was established to study shale gas percolation through reservoir pressure, gas velocity, and bottom hole flowing pressure. The experimental results show that when pressure affects gas viscosity, shale gas viscosity decreases, which increases the percolation velocity and pressure drop velocity of the free state shale gas in matrix and fracture systems. And it is conducive to the desorption of adsorbed shale gas and effectively supplemented the bottom hole flowing pressure with the pressure wave propagation range and velocity increasing, so that the rate of pressure drop at the bottom of the well slows down, which makes the time that bottom hole flowing pressure reaches stability shortened. Therefore, the gas viscosity should be fully considered when studying the reservoir gas percolation.

Maxwell: the rise of statistical mechanics

Maxwell produced two publications on the kinetic theory of gases in which he proposed his eponymous velocity distribution of velocities, his theory on the equipartition of energy, and finally his famed transport equations. In this work he surprisingly predicted that gas viscosity is independent of density and then confirmed this finding in experiments he conducted with his wife.

Analysis of the influence of ultrahigh pressure on wellbore Fluid properties——Take Well ST1 in north Sichuan as an example

According to the research on wellbore pressure temperature prediction of ultra-high pressure gas Wells, the influence of ultra-high pressure on wellbore fluid physical property parameters cannot be ignored, the component model is adopted to calculate wellbore fluid PVT physical property, and the multi-phase flow model is modified to accurately predict wellbore pressure temperature distribution. For the prediction of gas deviation factor of well flow and gas viscosity of well flow, the component model has a high precision. By comparing with the prediction results of 8 black oil model methods, the pressure has a great influence on the black oil model. When the pressure is equal to 100MPa, the deviation value between the predicted results of Gopal method and Dranchuk-Abu-Kassem method and the component model is greater than 0.1, which can no longer guarantee the accuracy of gas deviation factor and gas viscosity prediction. Therefore, it is recommended to use the component model to predict the deviation factor and gas viscosity of gas well flow.

Accretion disks around young stars: the cradles of planet formation

Protoplanetary disks around young stars are the birth sites of planetary systems like our own. Disks represent the gaseous dusty matter left after the formation of their central stars. The mass and luminosity of the star, initial disk mass and angular momentum, and gas viscosity govern disk evolution and accretion. Protoplanetary disks are the cosmic nurseries where microscopic dust grains grow into pebbles, planetesimals, and planets.