FUTURE WORK

The main objective of the first phase of the project is to define the problem and to produce a new methodology, using neural network, to predict the rock mechanical property parameters. Comparative analysis of neural network algorithms will be investigated to produce a robust workflow for modeling neural network. Furthermore, the produced workflow provides a systematic approach to tackle various problems under investigation requiring the neural network modeling.

With neural network models developed from the previous wells, preliminary predictions showed promising results that a particular rock mechanical property parameter namely ROP of a new well can be predicted.

However, the neural network model was tested on a single well. Therefore, tests should additionally be extended to predict ROP for the same formation in different fields as well as in several wells within the same area. Training the neural network model with a large number of representative data will produce networks capable of capturing the rock bit interaction in a given formation.

This may also lead to better prediction for complex sets of formations.

Application of the proposed network model has been developed for a particular given formation, allowing for neural network modeling to predict ROP for geological formations having similar properties rather than the whole drilling sections. In order to expand knowledge for rate of penetration prediction and optimization, tests should further be extended to predict ROP for other heterogeneous formations.

Predicting ROP for the whole drilling sections was challenging as more sophisticated neural models were required. Therefore finding a new method to predict rate of penetration for a different set of formations was an important achievement. This involved building a more complex ANN model using several networks with different combinations of activation functions to deal with the various problems in complex drilling sections. Preliminary prediction for the 6.5” and 12.25” drilling sections showed promising results that other drilling sections may also be investigated. Further testing and developing of an integrated method for complex heterogeneous set of formations for other

drilling sections, such as 9.25” and 16” drilling sections should be carried out to predict ROP particularly for deep exploratory wells. Deep exploratory wells present a number of challenges which involve having to drill through hard interbedded formations.

In addition to ROP, ANN models able to predict other rock mechanical property parameters such as bit tooth wear rate (BTWR) for other drilling sections may be investigated as more data is needed to train the network in order to prove the robustness of the model. Since Weight on Bit (WOB) and Revolution per Minute (RPM) have a great impact on ROP and BTWR performance, models could be proposed for selecting optimal drilling parameters (WOB and RPM). This will provide the driller with a range of optimal parameters while drilling, enhancing the experience of the driller.

Three different ways of coding the bit types were applied in this study. Further tests may be carried out to predict bit type for future study. Predicting bit type using field data would enhance the bit type evaluation and give an alternative method for bit type selection. This may also give a prediction of a new bit type, allowing bit manufacturing companies to investigate a wider spectrum of bit designs.

BHA and its associated dynamics plays an important role in hole stability and therefore in optimizing ROP. BHA provides force for the bit to break the rock, survive a hostile mechanical environment and provide the driller with directional control of the well (Oil and Gas Glossary, 2010). In interbedded formations, lateral motions together with whirl are considered to be the most damaging vibrations for drill string components. BHA dynamics are also affected by other vibrational motions such as axial and stick slip (Mathur, et. al., 2009). Carful selection of the correct drilling parameters to be applied is key to control the damaging vibration effects. Future work needs to investigate the role of BHA and its associated dynamics for enhancing the stability of the system and therefore optimizing ROP.

In this study, the neural network developed was trained with various drilling parameters, allowing the neural network to predict ROP for a given formation, a particular zone, as well as a specific layer having similar properties. These micro subdivisions of the formation were made to perform a

comprehensive and detailed analysis on the prediction of rate of penetration as well as investigating the possibility of predicting other reservoir characteristics.

Since the formations under study contained productive zones, tests can be further extended to investigate whether a relationship exists between drilling parameters and other reservoir petrophysical characteristics. The neural network could be trained with various drilling parameters and wireline data testing for correlations to predict reservoir characteristics.

Predicting reservoir petrophysical characteristics from drilling data may facilitate the prediction of such parameters where it has previously been a complex task. In addition to the current sets of data, wireline interpretation and core data may also be investigated in future studies. Finally a library of neural network models capable of predicting ROP, TWR, WOB, RPM, Bit Type, and other petrophysical reservoir characteristics will hopefully be achieved. Using drilling data, the neural network models developed in this study have demonstrated their ability to provide more substantial predictions capable of modeling the various drilling phenomena that are not well understood.