Search

A reservoir non-damaging Nano treated aqueous-based drilling fluid was proposed. The study was carried out to obtain a drilling fluid with rheological properties able to keep cuttings in suspension for transport to the surface, minimize filtration and fluid loss. These rheological properties were also predicted using Artificial Neural Network (ANN) due to limitations of existing flow models in predicting Nano-based mud systems. Different concentrations of the Nanoparticles were added to these suspensions of water to act as filtration loss materials. A total number of 160 data were used to train the ANN which consists of both the input data and output data acquired from the experiment. The study shows good agreement between experimental data and the artificial neural network prediction of plastic viscosity (PV) and yield point (YP), for multiwall carbon nanotubes (MWCNT) formulated muds. The addition of different concentrations of MWCNT (0.5 – 3 g) as rheology modifier-additive was put to test in a field applicable aqueous mud system. The developed neural network has a Mean Absolute Deviation (MAD) of 0.61529, MSE of 0.57174, Root Mean Square Error (RMSE) of 0.75614 and Mean Absolute Percentage Error (MAPE) of 1.92331 for the predicted plastic viscosity and yield point which are all indicative of good levels of accuracy. It is important to explore the reservoir impairment mechanisms so as to improve and optimize the reservoir performance during the production of hydrocarbons. Having satisfied all the conditions, permeability was determined using Darcy's equation. A reduction in permeability within the range of 12 – 16 mD was recorded for the Nano treated water-based mud system.

Exergy is a simultaneous measure of the quantity and quality of energy. This helps to identify the inefficiency of the process and allows engineers to determine the cause and magnitude of the loss for each operating unit. Natural gas dehydration via absorption using glycol is the most economically attractive approach, and this advantage can only stand if lower energy consuption relative to adsorption process can be obtained; thus, timely prediction and identification of energy consumption is vital. In this study, an energy utilization predictive model for natural gas dehydration unit energy consumption was developed. This numeric approach will increase accuracy and reduce the high simulation time often encountered in using other simulation software. To achieve this novel idea, a multilayer perceptron approach which is a deep learning neural network model built on python using Tensorflow was adopted. The model used for this study is implemented to further increase the accuracy of the output set variables which are matched with simulation result. Since we are dealing with a non-linear function, rectified linear unit (ReLU) function was used to activate the neurons in hidden layers so as to strengthen the model to be more flexible in finding relationships which are arbitrary in the input parameter. These input parameters are fed into the steady state model and sent to various branches of fully connected neural network models using a linear activation function. Each branch produces a result for each output parameter thereby fitting the model by reducing the mean squared error loss. The training data were not normalized but left in their original form. Results showed that the adopted double hidden layer with 5 branches are uniquely branched in such a way that it predicts values for a single output variable, which is an upgrade to the former work done with a single hidden layer in literature. The accuracy analysis showed that the proposed double hidden layer approach in this study out-performed the single hidden layer.