Digital Image Processing

This book presents several recent advances that are related or fall under the umbrella of 'digital image processing', with the purpose of providing an insight into the possibilities offered by digital image processing algorithms in various fields. The presented mathematical algorithms are accompanied by graphical representations and illustrative examples for an enhanced readability. The chapters are written in a manner that allows even a reader with basic experience and knowledge in the digital image processing field to properly understand the presented algorithms. Concurrently, the structure of the information in this book is such that fellow scientists will be able to use it to push the development of the presented subjects even further

A Data-Driven Approach For Monitoring And Predictive Diagnosis Of Sucker Rod Pump System

Given its long operational history, a sucker-rod pump (SRP) has been widely utilized as a lifting solution to bring reservoir fluids to the surface with low cost and high efficiency. However, debugging the rod pump issues requires on-site activities that could cost time and money for operators. With the vast dataset collected from years of operation, numerous companies are looking to turn these engineering processes into automated systems in the oilfield network, despite the complexity of data and lack of knowledge. The integral approach is to develop real-time diagnostics for downhole conditions. The emerging artificial intelligence and big-data analytics have provided relatively precise downhole condition forecasting based on available data, enabling better decision-making. This thesis focuses on collecting representative data and utilizing machine learning techniques to predict operational anomalies of sucker-rod pumps. An experimental design at the University of Oklahoma, referred to as Interactive Digital Sucker Rod Pumping Unit (IDSRP), was used to facilitate a data-driven solution and monitor SRP performance and diagnostics. The physical framework includes a 50-ft transparent casing and tubing with a downhole rod pump at the bottom. A linear actuator provides the rod string’s reciprocal movement and simulates different surface units and operating scenarios. This facility uses proper instrumentation and a data acquisition system for signal sensor readings. A workflow is developed to translate surface dynamometer cards to downhole ones and train predictive models in time-driven pressure and rate data. Though primarily focusing on the normal pump operation, the test matrix varies in stroke length, pump speed, and rod movement shape. The tests validate the model to classify and detect various operational conditions in sucker-rod pumps. The model dynamically categorizes the pumps into key states of ideal condition and over-pumping with a regression fit of accuracy higher than 0.7 and overall classification accuracy of 92%. Moreover, the real-time model anticipates an event in which the pump experiences a slight pumping-off that could potentially deteriorate the rod. The results also help understand key features that drive sucker rod pump performance prediction and help detect anomalous pump behavior. The machine learning algorithms, developed by the physics-based inputs, generate predictive models, thus classifying operational conditions or failures of the pump. The diagnosis for the pump’s anomalies is also predicted by a real time analysis. The visualization enables to recognize the patterns and abnormal phases early. The explainable machine learning (i.e. Shapley additive explanation) helps decoding the predictive models with feature importance, local and global sensitivities in categorizing SRP conditions. The developed unit has the capability of working with different well conditions, combining with real-time training and applying models, to initiate early warnings. The developed processes and workflows have the potential of becoming a generic optimizing and monitoring model for rod pumps. The novelty of this setup consists not only in its mechatronic design but also in through monitoring of the pump operations

Faculty Publications and Creative Works 2004

Faculty Publications & Creative Works is an annual compendium of scholarly and creative activities of University of New Mexico faculty during the noted calendar year. Published by the Office of the Vice President for Research and Economic Development, it serves to illustrate the robust and active intellectual pursuits conducted by the faculty in support of teaching and research at UNM

Technology 2003: The Fourth National Technology Transfer Conference and Exposition, volume 2

Proceedings from symposia of the Technology 2003 Conference and Exposition, Dec. 7-9, 1993, Anaheim, CA, are presented. Volume 2 features papers on artificial intelligence, CAD&E, computer hardware, computer software, information management, photonics, robotics, test and measurement, video and imaging, and virtual reality/simulation

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp