Exploitation of mature oil fields around the world has forced researchers to develop new ways to optimize reservoir performance from such reservoirs. To achieve that, drilling horizontal wells is an effective method. The effectiveness of this kind of wells is to increase oil withdrawal. The objective of this study is to optimize the location, design, and completion of a new horizontal well as an oil producer to improve oil recovery in a real field located in Iraq. “A” is an oil and gas condensate field located in the Northeast of Iraq. From field production history, it is realized the difficulty to control gas and water production in this kind of complex carbonate reservoir with vertical producer wells. In this study, a horizont

     Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod