Obtaining the maximum Rate of Penetration (ROP) by optimization of drilling parameters is the aim of every drilling engineer. This helps to save time, reduces cost and minimizes drilling problems. Since ROP depends on a lot of parameters, it is very difficult to predict it correctly. Therefore, it is necessary and important to investigate a solution for predicting ROP with high accuracy in order to determine the suitable drilling parameters. In this study, a new approach using Artificial Neural Network (ANN) has been proposed to predict ROP from real - time drilling data of several wells in Nam Rong - Doi Moi field with more than 900 datasets included important parameters such as weight on bit (WOB), weight of mud (MW), rotary speed (RPM), stand pipe pressure (SPP), flow rate (FR), torque (TQ). In the process of training the network, algorithms and the number of neurons in the hidden layer were varied to find the optimal model. The ANN model shows high accuracy when comparing to actual ROP, therefore it can be recommended as an effective and suitable method to predict ROP of other wells in research area. Besides, base on the proposed ANN model, authors carried out experiments and determine the optimal weight on bit value for the drilling interval from 1800 to 2300 m of wells in in Nam Rong Doi Moi field.

Nowadays, polycrystalline diamond compact (PDC) drill bits are widely used in the oil and gas industry when drilling in soft rocks. However, parameters used for the PDC bit are usually based on the instructions of the drill manufacturer with a very wide adjustment range. Therefore, it is necessary to have a specific formula in order to determine the rate of penetration parameter (ROP) for the PDC bit in evaluating the influence of the parameters, rock mechanical properties and other parameters on the rate of penetration parameter (ROP). From there, it gives reasonable parameters and improves the design of the PDC bit to improve drilling efficiency. The article applies theoretical analysis method and Dalamber's principle to illuminate and build up the impact force model for PDC bits in the rock destruction process. From the impact force model, a formula to determine ROP for PDC bits was proposed. Finally, the authors applied the research results to the actual data obtained from the Nam Rong - Doi Moi oil field. The formula for determining the rate of penetration parameter (ROP) for the PDC bit that the authors have built has high accuracy and can be applied to many different rock.

In petroleum industry, the prediction of oil production flow rate plays an important role in tracking the good performance as well as maintaining production flow rate. In addition, a flow rate modelling with high accuracy will be useful in optimizing production properties to achieve the expected flow rate, enhance oil recovery factor and ensure economic efficiency. However, the oil production flow rate is traditionally predicted by theoretical or empirical models. The theoretical model usually gives predicted results with a wide variation of error, this model also requires a lot of input data that might be time-consuming and costly. The empirical models are often limited by the volume of data set used to construct the model, therefore predicted values from the applications of these models in practical condition are not highly accurate. In this research, the authors propose the use of an artificial neural network (ANN) to establish a better relationship between production properties and oil production flow rate and predict oil production flow rate. Using production data of 5 wells which use continuous gas lift method in X oil field, Vietnam, an ANN system was developed by using back-propagation algorithm and tansig function to predict production flow rate from the above data set. This ANN system is called a back-propagation neural network (BPNN). In comparison with the oil production flow rate data collected from these studied continuous gas lift oil wells, the predicted results from the constructed ANN achieved a very high correlation coefficient (98%) and low root mean square error (33.41 bbl/d). Therefore, the developed ANN models can serve as a practical and robust tool for oilfield prediction of production flow rate.