Application of Neural Networks to Evaluate Factors Affecting Drilling Performance

Achieving the highest Rate of Penetration (ROP) with the least possible Bit Tooth Wear Rate (BTWR) is the aim of every drilling engineer when selecting a drilling bit. Predicting the optimal ROP has become increasingly important given the rise in expenses involved in drilling a well. This has meant that oil companies engage in a perpetual struggle to predict the optimum rock mechanical property parameters. Predicting optimal rock mechanical property parameters, specifically Rate of Penetration (ROP), has become increasingly important given the rise in expenses involved in drilling a well. The prediction of ROP from the current available data is an important criterion for reduction of drilling costs. ROP represents rock bit interaction which relates rock compressive strength and bit aggressivity. ROP prediction is complex because of the numerous variables which lead to difficulties in evaluating drilling parameters. Several models and methods have been published for predicting, and therefore potentially optimizing rate of penetration. However, these models and methods have limitations, too many variables are included, their input parameters are often not readily available, and their relationships are complex and not easily modeled. Therefore, the application of Neural Network is suggested in this study. A new methodology has been developed to predict the rate of penetration using the Artificial Neural Network (ANN). Three case studies representing different formations in Kuwait have been conducted to investigate ROP prediction for various applications. These cases have investigated the prediction of ROP for a specific heterogeneous formation (CASE I); a semihomogenous formation (CASE II); a drilling section composed of a heterogeneous formation and for a drilling section composed of a complex heterogeneous set of formations (CASE III). Predicting ROP parameters is of particular interest, therefore finding a new method to predict ROP for the cases investigated in this study will be a valuable achievement. Application of the new network models would then be used for selecting the best parameters for an optimal drilling strategy based on field data. In addition to the prediction of ROP, several runs were carried out to predict Tooth Wear Rate (TWR) for a drilling section in case III. Rock bit interactions in the field as a function of rock mechanical property parameters was achieved by predicting ROP which relates to rock compressive strength and bit aggressivity; as well as TWR which relates to rock abrasiveness and wear resistance. History of bit runs, mud logging data, geological information, offset well bit records, drill bit characteristics, and wireline data all play an important role in the prediction of rock bit interactions in this study. Based on field data, the prediction of rock mechanical property parameters can be accomplished by the use of a neural network as an alternative prediction and optimization method. Neural network offers a new form of information processing that is fundamentally different from a traditional processing system. The system uses a knowledge base of various drilling parameters, to produce a “correlation” description of the optimal Rate of Penetration

Computational issues in process optimisation using historical data.

This thesis presents a new generic approach to improve the computational efficiency of neural-network-training algorithms and investigates the applicability of its 'learning from examples'' featured in improving the performance of a current intelligent diagnostic system. The contribution of this thesis is summarised in the following two points: For the first time in the literature, it has been shown that significant improvements in the computational efficiency of neural-network algorithms can be achieved using the proposed methodology based on using adaptive-gain variation. The capabilities of the current Knowledge Hyper-surface method (Meghana R. Ransing, 2002) are enhanced to overcome its existing limitations in modelling an exponential increase in the shape of the hyper-surface. Neural-network techniques, particularly back-propagation algorithms, have been widely used as a tool for discovering a mapping function between a known set of input and output examples. Neural networks learn from the known example set by adjusting its internal parameters, referred to as weights, using an optimisation procedure based on the 'least square fit principle'. The optimisation procedure normally involves thousands of iterations to converge to an acceptable solution. Hence, improving the computational efficiency of a neural-network algorithm is an active area of research. Various options for improving the computational efficiency of neural networks have been reviewed in this thesis. It has been shown in the existing literature that the variation of the gain parameter improves the learning efficiency of the gradient-descent method. However, it can be concluded from previous researchers' claims that the adaptive-gain variation improved the learning rate and hence the efficiency. It was discovered in this thesis that the gain variation has no influence on the learning rate; however, it actually influences the search direction. This made it possible to develop a novel approach that modifies the gradient-search direction by introducing the adaptive-gain variation. The proposed method is robust and has been shown that it can easily be implemented in all commonly used gradient- based optimisation algorithms. It has also been shown that it significantly improves the computational efficiency as compared to existing neural-network training algorithms. Computer simulations on a number of benchmark problems are used throughout to illustrate the improvement proposed in this thesis. In a foundry a large amount of data is generated within the foundry every time a casting is poured. Furthermore, with the increased number of computing tools and power there is a need to develop an efficient, intelligent diagnostic tool that can learn from the historical data to gain further insight into cause and effect relationships. In this study the performance of the current Knowledge Hyper-surface method was reviewed and the mathematical formulation of the current Knowledge Hyper-surface method was analysed to identify its limitations. An enhancement is proposed by introducing mid-points in the existing shape formulation. It is shown that the midpoints' shape function can successfully constrain the shape of decision hyper-surface to become more realistic with an acceptable result in a multi-dimensional case. This is a novel and original approach and is of direct relevance to the foundry industry

The generalization ability of artificial neural networks in forecasting TCP/IP network traffic trends

Artificial Neural Networks (ANNs) have been used in many fields for a variety of applications, and proved to be reliable. They have proved to be one of the most powerful tools in the domain of forecasting and analysis of various time series. The forecasting of TCP/IP network traffic is an important issue receiving growing attention from the computer networks. By improving upon this task, efficient network traffic engineering and anomaly detection tools can be created, resulting in economic gains from better resource management. The use of ANNs requires some critical decisions on the part of the user. These decisions, which are mainly concerned with the determinations of the components of the network structure and the parameters defined for the learning algorithm, can significantly affect the ability of the ANN to generalize, i.e. to have the outputs of the ANN approximate target values given inputs that are not in the training set. This has an impact on the quality of forecasts produced by the ANN. Although there are some discussions in the literature regarding the issues that affect network generalization ability, there is no standard method or approach that is universally accepted to determine the optimum values of these parameters for a particular problem. This research examined the impact a selection of key design features has on the generalization ability of ANNs. We examined how the size and composition of the network architecture, the size of the training samples, the choice of learning algorithm, the training schedule and the size of the learning rate both individually and collectively affect the ability of an ANN to learn the training data and to generalize well to novel data. To investigate this matter, we empirically conducted several experiments in forecasting a real world TCP/IP network traffic time series and the network performance validated using an independent test set. MATLAB version 7.4.0.287’s Neural Network toolbox version 5.0.2 (R2007a) was used for our experiments. The results are found to be promising in terms of ease of design and use of ANNs. Our results indicate that in contrast to Occam’s razor principle for a single hidden layer an increase in number of hidden neurons produces a corresponding increase in generalization ability of ANNs, however larger networks do not always improve the generalization ability of ANNs even though an increase in number of hidden neurons results in a concomitant rise in network generalization. Also, contradicting commonly accepted guidelines, networks trained with a larger representation of the data, exhibit better generalization than networks trained on smaller representations, even though the larger networks have a significantly greater capacity. Furthermore, the results obtained indicate that the learning rate, momentum, training schedule and choice of learning algorithm have as much a significant effect on ANN generalization ability. A number of conclusions were drawn from the results and later used to generate a comprehensive set of guidelines that will facilitate the process of design and use of ANNs in TCP/IP network traffic forecasting. The main contribution of this research lies in the identification of optimal strategies for the use of ANNs in forecasting TCP/IP network traffic trends. Although the information obtained from the tests carried out in this research is specific to the problem considered, it provides users of back-propagation networks with a valuable guide on the behaviour of networks under a wide range of operating conditions. It is important to note that the guidelines accrued from this research are of an assistive and not necessarily restrictive nature to potential ANN modellers