Modeling temperature dependency of oil-water relative permeability in thermal enhanced oil recovery processes using group method of data handling and gene expression programming

In the implementation of thermal enhanced oil recovery (TEOR) techniques, the temperature impact on relative permeability in oil–water systems (K[sub: rw] and K[sub: ro]) is of special concern. Hence, developing a fast and reliable tool to model the temperature effect on K[sub: rw] and K[sub: ro] is still a major challenge for precise studying of TEOR processes. To reach the goal of this work, two promising soft-computing algorithms, namely Group Method of Data Handling (GMDH) and Gene Expression Programming (GEP) were employed to develop reliable and simple to use paradigms to predict the temperature dependency of K[sub: rw] and K[sub: ro]. To do so, a large database encompassing wide-ranging temperatures and fluids/rock parameters, was considered to establish these correlations. Statistical results and graphical analyses disclosed the high degree of accuracy for the proposed correlations in emulating the experimental results. In addition, GEP correlations were found to be the most consistent with root mean square error (RMSE) values of 0.0284 and 0.0636 for K[sub: rw] and K[sub: ro], respectively. Lastly, the performance comparison against the preexisting correlations indicated the large superiority of the newly introduced correlations. The findings of this study can help for better understanding the temperature dependency of K[sub: rw] and K[sub: ro] in TEOR

Rigorous Connectionist Models to Predict Carbon Dioxide Solubility in Various Ionic Liquids

Estimating the solubility of carbon dioxide in ionic liquids, using reliable models, is of paramount importance from both environmental and economic points of view. In this regard, the current research aims at evaluating the performance of two data-driven techniques, namely multilayer perceptron (MLP) and gene expression programming (GEP), for predicting the solubility of carbon dioxide (CO2) in ionic liquids (ILs) as the function of pressure, temperature, and four thermodynamical parameters of the ionic liquid. To develop the above techniques, 744 experimental data points derived from the literature including 13 ILs were used (80% of the points for training and 20% for validation). Two backpropagation-based methods, namely Levenberg–Marquardt (LM) and Bayesian Regularization (BR), were applied to optimize the MLP algorithm. Various statistical and graphical assessments were applied to check the credibility of the developed techniques. The results were then compared with those calculated using Peng–Robinson (PR) or Soave–Redlich–Kwong (SRK) equations of state (EoS). The highest coefficient of determination (R2 = 0.9965) and the lowest root mean square error (RMSE = 0.0116) were recorded for the MLP-LMA model on the full dataset (with a negligible difference to the MLP-BR model). The comparison of results from this model with the vastly applied thermodynamic equation of state models revealed slightly better performance, but the EoS approaches also performed well with R2 from 0.984 up to 0.996. Lastly, the newly established correlation based on the GEP model exhibited very satisfactory results with overall values of R2 = 0.9896 and RMSE = 0.0201.publishedVersio

Bottom hole pressure estimation using hybridization neural networks and grey wolves optimization

An effective design and optimum production strategies of a well depend on the accurate prediction of its bottom hole pressure (BHP) which may be calculated or determined by several methods. However, it is not practical technically or economically to apply for a well test or to deploy a permanent pressure gauge in the bottom hole to predict the BHP. Consequently, several correlations and mechanistic models based on the known surface measurements have been developed. Unfortunately, all these tools (correlations & mechanistic models) are limited to some conditions and intervals of application. Therefore, establish a global model that ensures a large coverage of conditions with a reduced cost and high accuracy becomes a necessity.In this study, we propose new models for estimating bottom hole pressure of vertical wells with multiphase flow. First, Artificial Neural Network (ANN) based on back propagation training (BP-ANN) with 12 neurons in its hidden layer is established using trial and error. The next methods correspond to optimized or evolved neural networks (optimize the weights and thresholds of the neural networks) with Grey Wolves Optimization (GWO), and then its accuracy to reach the global optima is compared with 2 other naturally inspired algorithms which are the most used in the optimization field: Genetic Algorithm (GA) and Particle Swarms Optimization (PSO). The models were developed and tested using 100 field data collected from Algerian fields and covering a wide range of variables.The obtained results demonstrate the superiority of the hybridization ANN-GWO compared with the 2 other hybridizations or with the BP learning alone. Furthermore, the evolved neural networks with these global optimization algorithms are strongly shown to be highly effective to improve the performance of the neural networks to estimate flowing BHP over existing approaches and correlations. Keywords: Flowing bottom hole pressure (BHP), BHP correlations & mechanistic models, Artificial neural network, Neural network training, BP (back propagation), GWO, GA, PS

Applying hybrid support vector regression and genetic algorithm to water alternating CO2 gas EOR

Water alternating CO2 gas injection (WAG CO2) is one of the most promising enhanced oil recovery techniques. The optimization of this process requires performing many time‐consuming simulations. In this paper, an intelligent hybridization based on support vector regression (SVR) and genetic algorithm (GA) is introduced for the WAG process optimization in the presence of time‐dependent constraints. Multiple SVRs are used as dynamic proxy to mimic numerical simulator behavior in real time. Latin hypercube design (LHD) is applied to generate the proper runs to train the proxy and ten supplementary runs are randomly chosen to validate it. The goal of GA in this study is twofold. First, it is employed during the training of multiple SVRs to find their appropriate hyper‐parameters. Second, once the training and validation of the dynamic proxy are done, the GA is coupled with it to find the optimum WAG parameters which maximize field oil production total (FOPT) subject to time‐dependent water‐cut constraint and some domain constraints. The task is formulated as a non‐linear constrained optimization problem. A semi‐synthetic WAG CO2 case is used to examine the reliability of the approach. The results show that the established dynamic proxy is fast and accurate in reproducing the simulator outputs. The hybridization proxy‐GA is demonstrated to be reliable for the real‐time optimization of the formulated WAG process. © 2020 The Authors. Greenhouse Gases: Science and Technology published by Society of Chemical Industry and John Wiley & Sons, Ltd

Predicting thermal conductivity of carbon dioxide using group of data-driven models

Thermal conductivity of carbon dioxide (CO2) is a vital thermophysical parameter that significantly affects the heat transfer modeling related to CO2 transportation, pipelines design and associated process industries. The current study lays emphasis on implementing powerful soft computing approaches to develop novel paradigms for estimation of CO2 thermal conductivity. To achieve this, a massive database including 5893 experimental datapoints was acquired from the experimental investigations. The collected data, covering pressure values from 0.097 to 209.763 MPa and temperature between 217.931 and 961.05 K, were employed for establishing various models based on multilayer perceptron (MLP) optimized by different back-propagation algorithms, and radial basis function neural network (RBFNN) coupled with particle swarm optimization (PSO). Then, the two best found models were linked under two committee machine intelligent systems (CMIS) using weighted averaging and group method of data handling (GMDH). The obtained results showed that CMIS-GMDH is the most accurate paradigm with an overall AARD% and R2 values of 0.8379% and 0.9997, respectively. In addition, CMIS-GMDH outperforms the best prior explicit models. Finally, the leverage technique confirmed the validity of the model and more than 96% of the data are within its applicability realm

Production optimization under waterflooding with long short-term memory and metaheuristic algorithm

In petroleum domain, optimizing hydrocarbon production is essential because it does not only ensure the economic prospects of the petroleum companies, but also fulfills the increasing global demand of energy. However, applying numerical reservoir simulation (NRS) to optimize production can induce high computational footprint. Proxy models are suggested to alleviate this challenge because they are computationally less demanding and able to yield reasonably accurate results. In this paper, we demonstrated how a machine learning technique, namely long short-term memory (LSTM), was applied to develop proxies of a 3D reservoir model. Sampling techniques were employed to create numerous simulation cases which served as the training database to establish the proxies. Upon blind validating the trained proxies, we coupled these proxies with particle swarm optimization to conduct production optimization. Both training and blind validation results illustrated that the proxies had been excellently developed with coefficient of determination, R2 of 0.99. We also compared the optimization results produced by NRS and the proxies. The comparison recorded a good level of accuracy that was within 3% error. The proxies were also computationally 3 times faster than NRS. Hence, the proxies have served their practical purposes in this study

Application of nature-inspired algorithms and artificial neural network in waterflooding well control optimization

With the aid of machine learning method, namely artificial neural networks, we established data-driven proxy models that could be utilized to maximize the net present value of a waterflooding process by adjusting the well control injection rates over a production period. These data-driven proxies were maneuvered on two different case studies, which included a synthetic 2D reservoir model and a 3D reservoir model (the Egg Model). Regarding the algorithms, we applied two different nature-inspired metaheuristic algorithms, i.e., particle swarm optimization and grey wolf optimization, to perform the optimization task. Pertaining to the development of the proxy models, we demonstrated that the training and blind validation results were excellent (with coefficient of determination, R2 being about 0.99). For both case studies and the optimization algorithms employed, the optimization results obtained using the proxy models were all within 5% error (satisfied level of accuracy) compared with reservoir simulator. These results confirm the usefulness of the methodology in developing the proxy models. Besides that, the computational cost of optimization was significantly reduced using the proxies. This further highlights the significant benefits of employing the proxy models for practical use despite being subject to a few constraints

Predicting wax deposition using robust machine learning techniques

Accurate prediction of wax deposition is of vital interest in digitalized systems to avoid many issues that interrupt the flow assurance during production of hydrocarbon fluids. The present investigation aims at establishing rigorous intelligent schemes for predicting wax deposition under extensive production conditions. To do so, multilayer perceptron (MLP) optimized with Levenberg-Marquardt algorithm (MLP-LMA) and Bayesian Regularization algorithm (MLP-BR) were taught using 88 experimental measurements. These latter were described by some independent variables, namely temperature (in K), specific gravity, and compositions of C1–C3, C4–C7, C8–C15, C16–C22, C23–C29 and C30+. The obtained results showed that MLP-LMA achieved the best performance with an overall root mean square error of 0.2198 and a coefficient of determination (R2) of 0.9971. The performance comparison revealed that MLP-LMA outperforms the prior approaches in the literature

Well production forecast in Volve field: Application of rigorous machine learning techniques and metaheuristic algorithm

Developing a model that can accurately predict the hydrocarbon production by only employing the conventional mathematical approaches can be very challenging. This is because these methods require some underlying assumptions or simplifications, which might cause the respective model to be unable to capture the actual physical behavior of fluid flow in the subsurface. However, data-driven methods have provided a solution to this challenge. With the aid of machine learning (ML) techniques, data-driven models can be established to help forecasting the hydrocarbon production within acceptable range of accuracy. In this paper, different ML techniques have been implemented to build the models that predict the oil production of a well in Volve field. These techniques comprise support vector regression (SVR), feedforward neural network (FNN), and recurrent neural network (RNN). Particle swarm optimization (PSO) has also been integrated in training the SVR and FNN. These developed models can practically estimate the oil production of a well in Volve field as a function of time and other parameters: on stream hours, average downhole pressure, average downhole temperature, average choke size percentage, average wellhead pressure, average wellhead temperature, daily gas production, and daily water production. All these models illustrate splendid training, validation, and testing results with correlation coefficients, R2 being greater than 0.98. Moreover, these models show good predictive performance with R2 exceeding 0.94. Comparative analysis is also done to evaluate the predictability of these models