Artificial Neural Network Model for Alkali-Surfactant-Polymer Flooding in Viscous Oil Reservoirs: Generation and Application

Abstract

Chemical flooding has been widely utilized to recover a large portion of the oil remaining in light and viscous oil reservoirs after the primary and secondary production processes. As core-flood tests and reservoir simulations take time to accurately estimate the recovery performances as well as analyzing the feasibility of an injection project, it is necessary to find a powerful tool to quickly predict the results with a level of acceptable accuracy. An approach involving the use of an artificial neural network to generate a representative model for estimating the alkali-surfactant-polymer flooding performance and evaluating the economic feasibility of viscous oil reservoirs from simulation is proposed in this study. A typical chemical flooding project was referenced for this numerical study. A number of simulations have been made for training on the basis of a base case from the design of 13 parameters. After training, the network scheme generated from a ratio data set of 50%-20%-30% corresponding to the number of samples used for training-validation-testing was selected for estimation with the total coefficient of determination of 0.986 and a root mean square error of 1.63%. In terms of model application, the chemical concentration and injection strategy were optimized to maximize the net present value (NPV) of the project at a specific oil price from the just created ANN model. To evaluate the feasibility of the project comprehensively in terms of market variations, a range of oil prices from 30 $/bbl to 60 $/bbl referenced from a real market situation was considered in conjunction with its probability following a statistical distribution on the NPV computation. Feasibility analysis of the optimal chemical injection scheme revealed a variation of profit from 0.42 $MM to 1.0 $MM, corresponding to the changes in oil price. In particular, at the highest possible oil prices, the project can earn approximately 0.61 $MM to 0.87 $MM for a quarter five-spot scale. Basically, the ANN model generated by this work can be flexibly applied in different economic conditions and extended to a larger reservoir scale for similar chemical flooding projects that demand a quick prediction rather than a simulation process.

Experimental investigation, binary modelling and artificial neural network prediction of surfactant adsorption for enhanced oil recovery application

“…ANN structure also consists of biases allocated to the neurons, the weights associated with neuron links and the transfer function (activating) with the existence of a bias value in order to turn inputs into a unified output. The neurons can be trained in way that they can perform a specific task by adjusting the values of the nodes, allowing the information path to be recognized [89] , [90] . In a three-layer network, the neurons of the input layer are connected with the other neurons in the hidden layer via specific weights and that determines the contribution of each individual neuron in the input layer to the other neuron in the hidden layer [90] .…”

“…The neurons can be trained in way that they can perform a specific task by adjusting the values of the nodes, allowing the information path to be recognized [89] , [90] . In a three-layer network, the neurons of the input layer are connected with the other neurons in the hidden layer via specific weights and that determines the contribution of each individual neuron in the input layer to the other neuron in the hidden layer [90] . The number (size) of these hidden layers and connecting nodes must be determined in order to optimize the capacities and abilities of the network for any set of data.…”

“…The validation step is applied to optimize the network performance by controlling the number of epochs (one training cycle). Epochs demonstrate the iteration process used during the training process, where this validation step ensures obtaining minimal errors of the system [90] . Lastly, the testing set of data is utilised to guarantee the high accuracy of the trained neural network on predicting the unavailable data [60] .…”

Apparent Viscosity Prediction of Water-Based Muds Using Empirical Correlation and an Artificial Neural Network

“…An Artificial neural network (ANN) is considered an important tool to solve non-linear and complex problems between any input and output parameters [13][14][15][16][17][18][19][20][21]. ANNs have various applications in different fields (e.g., medicine, electronics, aerospace, petroleum industry, and chemistry) [22][23][24][25].…”

“…ANNs have various applications in different fields (e.g., medicine, electronics, aerospace, petroleum industry, and chemistry) [22][23][24][25]. The structure of the ANN model comprises of three different components, which are a learning algorithm, transfer function, and network architecture [13][14][15][16][17][18][19][20][21]. In addition, each ANN model should be made up of at least three layers (i.e., input, hidden and output layers).…”