ASPHALTENE PRECIPITATION PREDICTION DURING BITUMEN RECOVERY: EXPERIMENTAL APPROACH VERSUS POPULATION BALANCE AND CONNECTIONIST MODELS

Deasphalting bitumen using paraffinic solvent injection is a commonly used technique to reduce both its viscosity and density and ease its flow through pipelines. Common modeling approaches for asphaltene precipitation prediction such as population balance model (PBM) contains complex mathematical relation and require conducting precise experiments to define initial and boundary conditions. Machine learning (ML) approach is considered as a robust, fast, and reliable alternative modeling approach. The main objective of this research work was to model the effect of paraffinic solvent injection on the amount of asphaltene precipitation using ML and PBM approaches. Five hundred and ninety (590) experimental data were collected from the literature for model development. The gathered data was processed using box plot, data scaling, and data splitting. Data preprocessing led to the use of 517 data points for modeling. Then, multilayer perceptron, random forest, decision tree, support vector machine, committee machine intelligent system optimized by annealing, and random search techniques were used for modeling. Precipitant molecular weight, injection rate, API gravity, pressure, C5 asphaltene content, and temperature were determined as the most relevant features for the process. Although the results of the PBM model are precise, the AI/ML model (CMIS) is the preferred model due to its robustness, reliability, and relative accuracy. The committee machine intelligent system is the superior model among the developed smart models with an RMSE of 1.7% for the testing dataset and prediction of asphaltene precipitation during bitumen recovery

Application of Radial Basis Function (RBF) neural networks to estimate oil field drilling fluid density at elevated pressures and temperatures

International audienceThe petroleum industry today has no choice, but to explore new and ever more deep and challenging pay zones as the most of the shallow oil and gas producing pay zones are severely depleted during the years of production. For improved drilling fluid performance in deep and hostile environment wells, accurate knowledge about the fluid density at high temperature and pressure conditions is an essential step. To achieve this mission, this study is aiming at developing a new computer-based tool is designed and applied for accurate calculation of drilling fluid density at HPHT conditions. In order to seek the comprehensiveness of the developed tool, four different kinds of fluids including water based, oil based, Colloidal Gas Aphron (CGA) based and also synthetic fluids are selected for modeling purpose. Radial Basis Function (RBF) network is considered as the modeling network. The results calculated via the proposed algorithm are compared to data reported in the literature. To make a judgment based on various statistical quality measures, it is concluded that the developed tool is reliable and efficient for density calculations of various fluids at extreme conditions

Connectionist Models for Asphaltene Precipitation Prediction by <i>n</i>‑Alkane TitrationPressure and Crude Oil Properties Considered

Experimental approaches for determining asphaltene precipitation in a laboratory are time-consuming and expensive due to consumption of a large amount of solvents. Development of robust, reliable, fast, and economic predictive tools to forecast the amount of asphaltene precipitation for a wide range of pressures, temperatures, and operational parameters and properties of petroleum fluids is inevitable. The main objective of this research work was to develop machine learning models using experimental data to predict asphaltene precipitation amount due to titration. After collecting 1439 data samples from 27 experimental research works, a quality check was performed for possible logical filling of the missing values and detecting the problematic data samples. Three categories, operational parameters, oil properties, and gas properties, were recognized to be the most influential parameters. The database used in this work is so far the largest ever reported in the literature. In addition, pressure is considered as one of the major parameters in this work, which was not considered in the previously reported models (i.e., all were conducted under ambient pressure). For the first time, 39 different oil samples were considered in the modeling (i.e., the existing works are mostly for one oil sample). We proposed new indices in the modeling to account for different oil types and n-alkanes. Due to the pressure data distribution, the database was split into two clusters. Each cluster went through several statistical preprocessing stages including treating duplicates and zero-variance features, imputing the missing data, assessing the collinearity, feature selection, and data splitting and scaling. Then, five different models, multilayer perceptron (MLP), support vector machine (SVM), decision tree (DT), random forest (RF), and committee machine intelligent system (CMIS), were used for model development. Based on the acquired results, the RF was determined as the best predictor for both clusters, consequently, for the whole database with root-mean-square error (RMSE) and R2 values of 0.94 and 0.97, respectively, for the testing data set. The developed models can be used to accurately predict asphaltene precipitation by n-alkane titration for a wide range of pressure and crude oil properties

DATA-DRIVEN CONNECTIONIST MODELS FOR PERFORMANCE PREDICTION OF LOW SALINITY WATERFLOODING IN SANDSTONE RESERVOIRS

Low salinity waterflooding (LSWF) and its variants also known as smart water or ion tuned water injection have emerged as promising enhanced oil recovery (EOR) methods. LSWF is a complex process controlled by several mechanisms and parameters involving oil, brine, and rock composition. The major mechanisms and processes controlling LSWF are still being debated in the literature. Thus, the establishment of an approach that relates these parameters to the final recovery factor (RFf ) is vital. The main objective of this research work was to use a number of artificial intelligence models to develop robust predictive models based on experimental data and main parameters controlling the LSWF determined through sensitivity analysis and feature selection. The parameters include properties of oil, rock, injected brine, and connate water. Different operational parameters were considered to increase the model accuracy as well. After collecting the relevant data from 99 experimental studies reported in the literature, the database underwent a comprehensive and rigorous data preprocessing stage, which included removal of duplicates and low-variance features, missing value imputation, collinearity assessment, data characteristic assessment, outlier removal, feature selection, data splitting (80−20 rule was applied), and data scaling. Then, a number of methods such as linear regression (LR), multilayer perceptron (MLP), support vector machine (SVM), and committee machine intelligent system (CMIS) were used to link 1316 data samples assembled in this research work. Based on the obtained results, the CMIS model was proven to produce superior results compared to its counterparts such that the root mean squared rrror (RMSE) values for both training and testing data are 4.622 and 7.757, respectively. Based on the feature importance results, the presence of Ca2+ in the connate water, Na+ in the injected brine, core porosity, and total acid number of the crude oil are detected as the parameters with the highest impact on the RFf . The CMIS model proposed here can be applied with a high degree of confidence to predict the performance of LSWF in sandstone reservoirs. The database assembled for the purpose of this research work is so far the largest and most comprehensive of its kind, and it can be used to further delineate mechanisms behind LSWF and optimization of this EOR process in sandstone reservoir

Prediction of supercritical CO2/brine relative permeability in sedimentary basins during carbon dioxide sequestration

This study aims to accurately determine supercritical CO2/brine relative permeability, using a hybrid Genetic Algorithm-Radial Basis Function (GA-RBF) neural network. CO2 sequestration, along with some enhanced oil recovery (EOR) processes, demands an exact knowledge of relative permeability in order to ensure the viability of the operation. Previous studies have shown that errors in CO2/brine relative permeability data might result in a four-fold error in injectivity estimation. This, as well as several recent studies regarding the relative permeability of CO2/brine systems, has indicated the importance of this parameter. The developed GA-RBF model was determined to be in excellent accordance with experimental data, yielding average absolute relative deviations (AARD) of 4.66% and 2.11% for CO2 and brine relative permeability, respectively. In addition, comprehensive comparisons between classic models and the proposed GA-RBF model have been carried out. Based on these comparisons, it may be concluded that the proposed model is superior to the classic method (simple correlation) in terms of its accuracy in determining the viability of CO2 sequestration operations. © 2015 Society of Chemical Industry and John Wiley & Sons, Lt

Prediction of carbon dioxide solubility in aqueous mexture of methyldiethanolamine and N-methylpyrrolidone using intelligent models

Clear knowledge about the solubility of acid gases such as CO2 in different solvents at different states is very important, especially for carbon capture from flue gases. This study highlights the application of artificial intelligence in prediction of carbon dioxide solubility in a mix solvent of methyldiethanolamine and N-methylpyrrolidone at wide range of temperature and pressure. The input data of the models were temperature, pressure, and saturation pressure and the output parameter was the solubility of CO2. Different intelligent approaches such as MLP-ANN, GA-RBF, CSA-LSSVM, Hybrid-ANFIS, PSO-ANFIS, and CMIS were developed and the reliability of models was investigated through different graphical and statistical methods. Result showed that the developed models are accurate and predictive for estimation of experimental solubility data. However, the CMIS approach exhibited better results compared to other intelligent approaches. Results of this study showed that intelligent based algorithms are powerful alternatives for time-consuming and difficult experimental processes of solubility measurement

Prediction of reservoir brine properties using radial basis function (RBF) neural network

Aquifers, which play a prominent role as an effective tool to recover hydrocarbon from reservoirs, assist the production of hydrocarbon in various ways. In so-called water flooding methods, the pressure of the reservoir is intensified by the injection of water into the formation, increasing the capacity of the reservoir to allow for more hydrocarbon extraction. Some studies have indicated that oil recovery can be increased by modifying the salinity of the injected brine in water flooding methods. Furthermore, various characteristics of brines are required for different calculations used within the petroleum industry. Consequently, it is of great significance to acquire the exact information about PVT properties of brine extracted from reservoirs. The properties of brine that are of great importance are density, enthalpy, and vapor pressure. In this study, radial basis function neural networks assisted with genetic algorithm were utilized to predict the mentioned properties. The root mean squared error of 0.270810, 0.455726, and 1.264687 were obtained for reservoir brine density, enthalpy, and vapor pressure, respectively. The predicted values obtained by the proposed models were in great agreement with experimental values. In addition, a comparison between the proposed model in this study and a previously proposed model revealed the superiority of the proposed GA-RBF model

Efficient estimation of hydrolyzed polyacrylamide (HPAM) solution viscosity for enhanced oil recovery process by polymer flooding

International audiencePolymers applications have been progressively increased in sciences and engineering including chemistry, pharmacology science, and chemical and petroleum engineering due to their attractive properties. Amongst the all types of polymers, partially Hydrolyzed Polyacrylamide (HPAM) is one of the widely used polymers especially in chemistry, and chemical and petroleum engineering. Capability of solution viscosity increment of HPAM is the key parameter in its successful applications; thus, the viscosity of HPAM solution must be determined in any study. Experimental measurement of HPAM solution viscosity is time-consuming and can be expensive for elevated conditions of temperatures and pressures, which is not desirable for engineering computations. In this communication, Multilayer Perceptron neural network (MLP), Least Squares Support Vector Machine approach optimized with Coupled Simulated Annealing (CSA-LSSVM), Radial Basis Function neural network optimized with Genetic Algorithm (GA-RBF), Adaptive Neuro Fuzzy Inference System coupled with Conjugate Hybrid Particle Swarm Optimization (CHPSO-ANFIS) approach, and Committee Machine Intelligent System (CMIS) were used to model the viscosity of HPAM solutions. Then, the accuracy and reliability of the developed models in this study were investigated through graphical and statistical analyses, trend prediction capability, outlier detection, and sensitivity analysis. As a result, it has been found that the MLP and CMIS models give the most reliable results with determination coefficients (R2) more than 0.98 and Average Absolute Relative Deviations (AARD) less than 4.0%. Finally, the suggested models in this study can be applied for efficient estimation of aqueous solutions of HPAM polymer in simulation of polymer flooding into oil reservoirs

Implementing radial basis function neural network for prediction of surfactant retention in petroleum production and processing industries

Chemical flooding is an effective way to gain higher oil recovery as part of a tertiary oil recovery scheme. There are several variables contribute in surfactant retention in petroleum production including type of rock, pH, chemical structure of surfactant, salinity of formation water, acidity of oil, mobility, microemulsion viscosity, and cosolvent concentration. Although different theoretical studies on the mechanisms of surfactant retention are reported in the literature there is little research on the development of an accurate and effective model for prediction of surfactant retention in petroleum production. In this study, radial basis function was developed based on experimental dynamic surfactant retention data. The experimental data include a wide range of conditions. Results of the modeling study showed that the developed model is very accurate and robust in prediction of actual surfactant retention data. In addition, the comparison between the proposed model in this study and available models in literature showed the superiority of this model