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

Energy and Sampling Constrained Asynchronous Communication

The minimum energy, and, more generally, the minimum cost, to transmit one bit of information has been recently derived for bursty communication when information is available infrequently at random times at the transmitter. This result assumes that the receiver is always in the listening mode and samples all channel outputs until it makes a decision. If the receiver is constrained to sample only a fraction f>0 of the channel outputs, what is the cost penalty due to sparse output sampling? Remarkably, there is no penalty: regardless of f>0 the asynchronous capacity per unit cost is the same as under full sampling, ie, when f=1. There is not even a penalty in terms of decoding delay---the elapsed time between when information is available until when it is decoded. This latter result relies on the possibility to sample adaptively; the next sample can be chosen as a function of past samples. Under non-adaptive sampling, it is possible to achieve the full sampling asynchronous capacity per unit cost, but the decoding delay gets multiplied by 1/f. Therefore adaptive sampling strategies are of particular interest in the very sparse sampling regime.Comment: Submitted to the IEEE Transactions on Information Theor

Doctor of Philosophy

dissertationWireless communication has become an essential part of everyday life. The hunger for more data, more phone calls, more video, and more access in more places, including vehicles, is growing massively. Communication in vehicles is particularly challenging because of their extremely high multipath environment. In addition, there is significant interest in reducing the number of wires in vehicles to reduce weight, complexity, maintenance, etc. and replace them with wireless systems. Preliminary research shows that MIMO systems take advantage of the extreme multipath environment found in aircraft and other vehicles and also provides more consistent channel capacity than SISO systems. The purpose of this research was to quantify complex channels (including the aircraft/vehicle environment) and their relation to other environments, evaluate MIMO in aircraft, provide design constraints for accurately modeling complex channels, and provide information to predict optimum antenna type and location to enable communication in aircraft/cars/buses/ships/trains/etc. and other extreme channels. The ability to evaluate and design MIMO technologies from the guidelines in this paper is potentially transformative for aircraft safety - enabling a new generation of location specific monitoring and maintenance. Average measured capacity was found to be between 18 and 21 bits/s/Hz using a 4x4 array of antennas, and had no direct relation to the size of the channel. Site-specific capacity showed a multipath rich channel, varying between 15 to 23 bits/s/Hz. The capacity decreased for increasing measurement distance, with exceptions near reflective objects that increase multipath. Due to these special circumstances for site-specific locations within complex channels, it is recommended that 3D ray tracing be used for modeling as it is more accurate than commonly used statistical models, within 1.1 bits/s/Hz. This showed that our 3D ray tracing is adaptable to various environments and gives a more accurate depiction than statistical models that average channel variations. This comes at the cost of greater model complexity. If increased complexity is not desirable, Nakagami 1.4 could be used as the next most accurate model. Design requirements for modeling different complex channels involve a detailed depiction of channel geometry, including height, width, length, shape (square, cylindrical, slanted walls, etc.), large windows, and reflective objects inside the channel space, especially those near the transmitter. Overall, the multipath rich channel found in vehicles is an excellent environment for MIMO systems. These complex channels can be simulated accurately without measurement and before they are even built using our sitespecific 3D ray tracing software combined with a detailed signal model to incorporate antenna effects

A hardware spinal decoder

Spinal codes are a recently proposed capacity-achieving rateless code. While hardware encoding of spinal codes is straightforward, the design of an efficient, high-speed hardware decoder poses significant challenges. We present the first such decoder. By relaxing data dependencies inherent in the classic M-algorithm decoder, we obtain area and throughput competitive with 3GPP turbo codes as well as greatly reduced latency and complexity. The enabling architectural feature is a novel alpha-beta incremental approximate selection algorithm. We also present a method for obtaining hints which anticipate successful or failed decoding, permitting early termination and/or feedback-driven adaptation of the decoding parameters. We have validated our implementation in FPGA with on-air testing. Provisional hardware synthesis suggests that a near-capacity implementation of spinal codes can achieve a throughput of 12.5 Mbps in a 65 nm technology while using substantially less area than competitive 3GPP turbo code implementations.Irwin Mark Jacobs and Joan Klein Jacobs Presidential FellowshipIntel Corporation (Fellowship)Claude E. Shannon Research Assistantshi

Cluster requiem and the rise of cumulative growth theory

Industry cluster theory has been the predominant model guiding economic development policy throughout the world for nearly two decades. As appealing as the cluster approach has been to regional scientists and policy makers it suffers from a number of theoretical and empirical shortcomings, including an inability to explain economic dispersion and the presence of high-growing firms that thrive in non-clustered industries and locations. This dissertation tracks the growth and survival of a cohort of more than 300,000 establishments operating in Pennsylvania during the 1997-2007 period. It reveals that firm characteristics are 10-times more powerful than industry and cluster characteristics, and 50-times more powerful than location characteristics, in explaining and predicting establishment-level growth and survival. It also finds a Power Law is present in the distribution of establishment growth, indicating that a sub- set of businesses systematically accumulate a disproportionate share of employment growth. Roughly 1% of establishments created 169% of all net new jobs added in the state over a ten- year period. Growth is further concentrated among businesses that are able to sustain growth over multiple years. This suggests that the principal driver of regional growth is cumulative firm growth – the accumulation of a disproportionate amount of growth among a small number of firms through sustained expansion over multiple years. I conclude that the path to building better theory and more effective development policies is one that explicitly links regional growth to the growth of firms. Such an approach should focus on endogenous firm dynamics rather than exogenous heuristics such as industry and location