Download or read book Application of Statistical Learning Models to Predict and Optimize Rate of Penetration of Drilling written by Chiranth Manjunath Hegde and published by . This book was released on 2016 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modeling the rate of penetration of the drill bit has been essential to optimizing drilling operations. Optimization of drilling - a cost intensive operation in the oil and gas industry- is essential, especially during downturns in the oil and gas industry. This thesis evaluates the use of statistical learning models to predict and optimize ROP in drilling operations. Statistical Learning Models can range from simple models (linear regression) to complex models (random forests). A range of statistical learning models have been evaluated in this thesis in order to determine an optimum method for prediction of rate of penetration (ROP) in drilling. Linear techniques such as regression have been used to predict ROP. Special linear regression models such as lasso and ridge regression have been evaluated. Dimension reduction techniques like principal components regression are evaluated for ROP prediction. Non-linear algorithms like trees have been introduced to address the low accuracy of linear models. Trees suffer from low accuracy and high variance. Trees are bootstrapped and averaged to create the random forests algorithm. Random forests algorithm is a powerful algorithm which predicts ROP with high accuracy. A parametric study was used to determine the ideal training sets for ROP prediction. It was conclude that data within a formation forms the best training set for ROP prediction. Parametric analysis of the length of the training set revealed that 20% of the formation interval depth was enough to train an accurate predictor for ROP. The ROP model built using statistical learning models were then used as an equation to optimize ROP. An optimization algorithm was used to compute ideal values of input feature to improve ROP in the test set. Surface controllable input features were varied in an effort to improve ROP. ROP was improved to save a predicted total of 22 hours of active drilling time using this method. This thesis introduces statistical learning techniques for predicting and optimizing ROP during drilling. These methods use input data to model ROP. Input features (surface parameters which are controllable on the rig) are then changed to optimize ROP. This methodology can be utilized for reducing nonproductive time (NPT) in drilling, and applied to optimize drilling procedures

Download or read book Continuous Learning of Analytical and Machine Learning Rate of Penetration ROP Models for Real time Drilling Optimization written by Cesar Mattos de Salles Soares and published by . This book was released on 2018 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Oil and gas operators strive to reach hydrocarbon reserves by drilling wells in the safest and fastest possible manner, providing indispensable energy to society at reduced costs while maintaining environmental sustainability. Real-time drilling optimization consists of selecting operational drilling parameters that maximize a desirable measure of drilling performance. Drilling optimization efforts often aspire to improve drilling speed, commonly referred to as rate of penetration (ROP). ROP is a function of the forces and moments applied to the bit, in addition to mud, formation, bit and hydraulic properties. Three operational drilling parameters may be constantly adjusted at surface to influence ROP towards a drilling objective: weight on bit (WOB), drillstring rotational speed (RPM), and drilling fluid (mud) flow rate. In the traditional, analytical approach to ROP modeling, inflexible equations relate WOB, RPM, flow rate and/or other measurable drilling parameters to ROP and empirical model coefficients are computed for each rock formation to best fit field data. Over the last decade, enhanced data acquisition technology and widespread cheap computational power have driven a surge in applications of machine learning (ML) techniques to ROP prediction. Machine learning algorithms leverage statistics to uncover relations between any prescribed inputs (features/predictors) and the quantity of interest (response). The biggest advantage of ML algorithms over analytical models is their flexibility in model form. With no set equation, ML models permit segmentation of the drilling operational parameter space. However, increased model complexity diminishes interpretability of how an adjustment to the inputs will affect the output. There is no single ROP model applicable in every situation. This study investigates all stages of the drilling optimization workflow, with emphasis on real-time continuous model learning. Sensors constantly record data as wells are drilled, and it is postulated that ROP models can be retrained in real-time to adapt to changing drilling conditions. Cross-validation is assessed as a methodology to select the best performing ROP model for each drilling optimization interval in real-time. Constrained to rig equipment and operational limitations, drilling parameters are optimized in intervals with the most accurate ROP model determined by cross-validation. Dynamic range and full range training data segmentation techniques contest the classical lithology-dependent approach to ROP modeling. Spatial proximity and parameter similarity sample weighting expand data partitioning capabilities during model training. The prescribed ROP modeling and drilling parameter optimization scenarios are evaluated according to model performance, ROP improvements and computational expense

Download or read book ROP in Horizontal Shale Wells written by Scott Pine Wallace and published by . This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Rate of Penetration (ROP) is one of the most important indicators of drilling efficiency available to drillers and engineers. Optimizing the ROP on a well allows the operator to decrease the amount of time spent drilling, which reduces cost. Further reductions in cost can come from utilizing and accurate performance model to understand whether a trip to the surface for a new bit is necessary, or if a bit trip would just increase Non Productive Time (NPT) without significantly benefitting performance. Clearly, understanding the factors that affect ROP is an essential part of drilling a successful well. Models for ROP have been developed over the academic history of Petroleum Engineering. One of the first models was the model developed by Bingham (1964), which offered a simple formula relating the RPM, Weight on Bit (WOB), and the diameter of the bit to a calculated value of ROP. Further work has continued in ROP modeling by Bourgoyne and Young (1974), who created a much more detailed ROP model including eight input parameters, Hareland and Rampersad (1994), who developed a drag-bit specific model, and Motahhari et al. (2010), who developed a model specific to wells drilled with a positive displacement motor (PDM) and a polycrystalline diamond compact (PDC) bit. These models have a varying number of input parameters, and each rely on the tuning of between three and eight empirical coefficients in order to optimize them to the well which is being studied. This study applies these traditional ROP models to data collected while drilling modern horizontal shale wells. These wells were drilled with a rotary steerable system, as well as a downhole PDM, and PDC bit. The traditional models were first fit to the drilling data by using the full range of the horizontal section of the well to optimize the empirical coefficients. This method resulted in the traditional models acting largely like a moving average of the drilling performance over the horizontal region. Then, the empirical coefficients were optimized based on 50 ft sections of the horizontal region, which produced a much tighter fit between the calculated and actual ROPs. However this fitting methodology was found to be erroneous, since it was generating a forced overfit of the model to the actual data. Finally, the Wider Windows Statistical Learning Model was applied to the drilling data. This produced the best fits of any of the models which were considered, and was the only one of the models which followed the high-frequency changes in the actual ROP data. As a result, this was the only one of the models which could be considered accurate for not only the estimation, but also the prediction and optimization of ROP in horizontal shale wells.

Download or read book Environmental Issues of Blasting written by Ramesh M. Bhatawdekar and published by Springer Nature. This book was released on 2022-01-04 with total page 83 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gives a rigorous and up-to-date study of the various AI and machine learning algorithms for resolving environmental challenges associated with blasting. Blasting is a critical activity in any mining or civil engineering project for breaking down hard rock masses. A small amount of explosive energy is only used during blasting to fracture rock in order to achieve the appropriate fragmentation, throw, and development of muck pile. The surplus energy is transformed into unfavourable environmental effects such as back-break, flyrock, air overpressure, and ground vibration. The advancement of artificial intelligence and machine learning techniques has increased the accuracy of predicting these environmental impacts of blasting. This book discusses the effective application of these strategies in forecasting, mitigating, and regulating the aforementioned blasting environmental hazards.

Download or read book An Introduction to Machine Learning written by Gopinath Rebala and published by Springer. This book was released on 2019-05-07 with total page 263 pages. Available in PDF, EPUB and Kindle. Book excerpt: Just like electricity, Machine Learning will revolutionize our life in many ways – some of which are not even conceivable today. This book provides a thorough conceptual understanding of Machine Learning techniques and algorithms. Many of the mathematical concepts are explained in an intuitive manner. The book starts with an overview of machine learning and the underlying Mathematical and Statistical concepts before moving onto machine learning topics. It gradually builds up the depth, covering many of the present day machine learning algorithms, ending in Deep Learning and Reinforcement Learning algorithms. The book also covers some of the popular Machine Learning applications. The material in this book is agnostic to any specific programming language or hardware so that readers can try these concepts on whichever platforms they are already familiar with. Offers a comprehensive introduction to Machine Learning, while not assuming any prior knowledge of the topic; Provides a complete overview of available techniques and algorithms in conceptual terms, covering various application domains of machine learning; Not tied to any specific software language or hardware implementation.

Download or read book End to end Drilling Optimization Using Machine Learning written by Chiranth Manjunath Hegde and published by . This book was released on 2018 with total page 556 pages. Available in PDF, EPUB and Kindle. Book excerpt: Drilling costs occupy a significant portion of oil and gas project’s budget. Optimization of drilling - increasing speed, reducing vibrations, and minimizing borehole instability - can lead to significant savings and hence have been extensively studied. Currently, most drilling optimization tools (or models) only tackle a single drilling metric: they seek to optimize either the rate of penetration (ROP), torque on bit (TOB), mechanical specific energy (MSE) or drilling vibrations. Models are often built independent of other metrics (without coupling) and do not accurately represent downhole conditions since drilling metrics are interrelated. This may lead to over or underestimation of the metric optimized which can severely reduce the effect of optimization. The objective of this dissertation is to introduce techniques, strategies, and algorithms that can be used to build a fully coupled drilling optimization model. Drilling optimization is studied by first optimizing ROP– where models for ROP prediction and inference are constructed using machine learning. Strategies and algorithms for determining optimal drilling parameters using ROP models are discussed. The unique problem posed by data-driven models are solved using meta-heuristic algorithms. A coupled model is constructed by building ROP, TOB, and MSE models conjointly using the random forests algorithm. Drilling vibrations – axial, lateral, and torsional – are modeled using a machine learning classification algorithm. This classification algorithm used to restrict the optimization space, ensuring that optimal parameters do not induce vibrations ahead of the bit. This model is used to investigate the effect of optimizing ROP and MSE on field data. A workflow is introduced linking all the aforementioned models into an end-to-end drilling optimization tool. The tool can be used as a recommendation system where field-measured data are used to determine and implement optimal drilling parameters ahead of the bit. The dissertation illustrates the use of statistical (or machine) learning techniques to address the problems encountered in drilling optimization

Download or read book Rate of Penetration Prediction Utilizing Hydromechanical Specific Energy written by Omogbolahan Ahmed and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The prediction and the optimization of the rate of penetration (ROP), an important measure of drilling performance, have increasingly generated great interest. Several empirical techniques have been explored in the literature for the prediction and the optimization of ROP. In this study, four commonly used artificial intelligence (AI) algorithms are explored for the prediction of ROP based on the hydromechanical specific energy (HMSE) ROP model parameters. The AIs explored are the artificial neural network (ANN), extreme learning machine (ELM), support vector regression (SVR), and least-square support vector regression (LS-SVR). All the algorithms provided results with accuracy within acceptable range. The utilization of HMSE in selecting drilling variables for the prediction models provided an improved and consistent methodology of predicting ROP with drilling efficiency optimization objectives. This is valuable from an operational point of view, because it provides a reference point for measuring drilling efficiency and performance of the drilling process in terms of energy input and corresponding output in terms of ROP. The real-time drilling data utilized are must-haves, easily acquired, accessible, and controllable during drilling operations.

Download or read book Behavior Engineering and Applications written by Raymond Wong and published by Springer. This book was released on 2018-07-10 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many industrial applications built today are increasingly using emerging behavior engineering technologies: this book looks at various research and practical issues for researchers and students working in computer science and engineering, and for industry technology providers interested in behavior engineering and applications. Behavior Engineering and Applications encompasses intelligent and efficient computational solutions, including models, architectures, algorithms and specific applications, focused on processing, discovering, understanding and analyzing the behavior captured by the above data. Focusing on applying any engineering paradigm to systemically process, discover, understand and analyze these data, this book also addresses problems in a variety of areas and applications that related to behavior engineering. This book includes chapters derived from selected papers from The 2016 International Conference on Behavior Engineering (ICBE), as well as separate contributions the editors selected cutting-edge research related to behavior engineering.

Download or read book Rock Dynamics Progress and Prospect Volume 2 written by Jianchun Li and published by CRC Press. This book was released on 2023-05-28 with total page 784 pages. Available in PDF, EPUB and Kindle. Book excerpt: Rock Dynamics: Progress and Prospect contains 153 scientific and technical papers presented at the Fourth International Conference on Rock Dynamics and Applications (RocDyn-4, Xuzhou, China, 17-19 August 2022). The two-volume set has 7 sections. Volume 1 includes the first four sections with 6 keynotes and 5 young scholar plenary session papers, and contributions on analysis and theoretical development, and experimental testing and techniques. Volume 2 contains the remaining three sections with 74 papers on numerical modelling and methods, seismic and earthquake engineering, and rock excavation and engineering. Rock Dynamics: Progress and Prospect will serve as a reference on developments in rock dynamics scientific research and on rock dynamics engineering applications. The previous volumes in this series (RocDyn-1, RocDyn-2, and RocDyn-3) are also available via CRC Press.

Download or read book Hydraulic Fracturing in Unconventional Reservoirs written by Hoss Belyadi and published by Gulf Professional Publishing. This book was released on 2019-06-18 with total page 632 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hydraulic Fracturing in Unconventional Reservoirs: Theories, Operations, and Economic Analysis, Second Edition, presents the latest operations and applications in all facets of fracturing. Enhanced to include today’s newest technologies, such as machine learning and the monitoring of field performance using pressure and rate transient analysis, this reference gives engineers the full spectrum of information needed to run unconventional field developments. Covering key aspects, including fracture clean-up, expanded material on refracturing, and a discussion on economic analysis in unconventional reservoirs, this book keeps today's petroleum engineers updated on the critical aspects of unconventional activity. Helps readers understand drilling and production technology and operations in shale gas through real-field examples Covers various topics on fractured wells and the exploitation of unconventional hydrocarbons in one complete reference Presents the latest operations and applications in all facets of fracturing

Download or read book Computational Intelligence written by Nazmul Siddique and published by John Wiley & Sons. This book was released on 2013-05-06 with total page 524 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing presents an introduction to some of the cutting edge technological paradigms under the umbrella of computational intelligence. Computational intelligence schemes are investigated with the development of a suitable framework for fuzzy logic, neural networks and evolutionary computing, neuro-fuzzy systems, evolutionary-fuzzy systems and evolutionary neural systems. Applications to linear and non-linear systems are discussed with examples. Key features: Covers all the aspects of fuzzy, neural and evolutionary approaches with worked out examples, MATLAB® exercises and applications in each chapter Presents the synergies of technologies of computational intelligence such as evolutionary fuzzy neural fuzzy and evolutionary neural systems Considers real world problems in the domain of systems modelling, control and optimization Contains a foreword written by Lotfi Zadeh Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing is an ideal text for final year undergraduate, postgraduate and research students in electrical, control, computer, industrial and manufacturing engineering.

Download or read book Fundamentals of Drilling Engineering written by Robert F. Mitchell and published by . This book was released on 2010-12-31 with total page 696 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Process Modelling and Simulation written by César de Prada and published by MDPI. This book was released on 2019-09-23 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since process models are nowadays ubiquitous in many applications, the challenges and alternatives related to their development, validation, and efficient use have become more apparent. In addition, the massive amounts of both offline and online data available today open the door for new applications and solutions. However, transforming data into useful models and information in the context of the process industry or of bio-systems requires specific approaches and considerations such as new modelling methodologies incorporating the complex, stochastic, hybrid and distributed nature of many processes in particular. The same can be said about the tools and software environments used to describe, code, and solve such models for their further exploitation. Going well beyond mere simulation tools, these advanced tools offer a software suite built around the models, facilitating tasks such as experiment design, parameter estimation, model initialization, validation, analysis, size reduction, discretization, optimization, distributed computation, co-simulation, etc. This Special Issue collects novel developments in these topics in order to address the challenges brought by the use of models in their different facets, and to reflect state of the art developments in methods, tools and industrial applications.

Download or read book Improved Torque and Drag Modeling Using Traditional and Machine Learning Methods written by Mayowa Olugbenga Oyedere and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: During the drilling process, the drillstring inadvertently comes in contact with the wellbore generating frictional losses in the rotating moment (torque) and axial force (drag). These losses reduce the rotational power available at the drill bit, thus making adequate torque and drag modeling a critical piece in the drilling puzzle. The simplifying assumptions of the widely used soft-string model for torque and drag modeling make it less accurate for new complex well designs, creating the need for the use of the more robust stiff-string model. This first part of this dissertation focuses on a new approach to developing a stiff-string model that can be easily implemented for well planning. The stiff-string model addresses the pitfalls of the soft-string model by using cubic splines for its well-path trajectory. It solves the three coupled, non-linear ordinary differential equations that describe the motion of the drillstring at each survey point to account for the shear forces and bending stiffness. The stiff-string model is then applied to design four horizontal wells. Drilling Optimization has consistently generated research interest over the years because of the cost-saving benefits of improving drilling efficiency. Rate of penetration (ROP) and torque-on-bit (TOB) predictions have become critical to the successful drilling optimization efforts. The second part of this dissertation focuses on the prediction and optimization of TOB using five regression-based machine learning algorithms. TOB was modeled as a function of rotary speed (RPM), weight-on-bit (WOB), flow rate, pump pressure, and unconfined compressive strength (UCS). Three direct search optimization algorithms—Nelder Mead, differential evolution, and particle swarm optimization (PSO)—were used to optimize TOB. The final part of this dissertation introduces a novel approach to ROP and TOB prediction by modeling it as a classification problem with two regions (low and high ROP and TOB respectively) based on a user-defined threshold. Five different classification algorithms were implemented and compared using the area under curve (AUC) classification metric. Finally, a probability gradient tool was developed to help inform the drilling engineer on the best combination of WOB and RPM to yield the desired drilling performance

Download or read book Machine Learning Guide for Oil and Gas Using Python written by Hoss Belyadi and published by Gulf Professional Publishing. This book was released on 2021-04-09 with total page 478 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning Guide for Oil and Gas Using Python: A Step-by-Step Breakdown with Data, Algorithms, Codes, and Applications delivers a critical training and resource tool to help engineers understand machine learning theory and practice, specifically referencing use cases in oil and gas. The reference moves from explaining how Python works to step-by-step examples of utilization in various oil and gas scenarios, such as well testing, shale reservoirs and production optimization. Petroleum engineers are quickly applying machine learning techniques to their data challenges, but there is a lack of references beyond the math or heavy theory of machine learning. Machine Learning Guide for Oil and Gas Using Python details the open-source tool Python by explaining how it works at an introductory level then bridging into how to apply the algorithms into different oil and gas scenarios. While similar resources are often too mathematical, this book balances theory with applications, including use cases that help solve different oil and gas data challenges. Helps readers understand how open-source Python can be utilized in practical oil and gas challenges Covers the most commonly used algorithms for both supervised and unsupervised learning Presents a balanced approach of both theory and practicality while progressing from introductory to advanced analytical techniques

Download or read book Applications of Artificial Intelligence Techniques in the Petroleum Industry written by Abdolhossein Hemmati-Sarapardeh and published by Gulf Professional Publishing. This book was released on 2020-08-26 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: Applications of Artificial Intelligence Techniques in the Petroleum Industry gives engineers a critical resource to help them understand the machine learning that will solve specific engineering challenges. The reference begins with fundamentals, covering preprocessing of data, types of intelligent models, and training and optimization algorithms. The book moves on to methodically address artificial intelligence technology and applications by the upstream sector, covering exploration, drilling, reservoir and production engineering. Final sections cover current gaps and future challenges. Teaches how to apply machine learning algorithms that work best in exploration, drilling, reservoir or production engineering Helps readers increase their existing knowledge on intelligent data modeling, machine learning and artificial intelligence, with foundational chapters covering the preprocessing of data and training on algorithms Provides tactics on how to cover complex projects such as shale gas, tight oils, and other types of unconventional reservoirs with more advanced model input

Download or read book ICT for an Inclusive World written by Youcef Baghdadi and published by Springer Nature. This book was released on 2020-01-30 with total page 597 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses the impact of information and communication technologies (ICTs) on organizations and on society as a whole. Specifically, it examines how such technologies improve our life and work, making them more inclusive through smart enterprises. The book focuses on how actors understand Industry 4.0 as well as the potential of ICTs to support organizational and societal activities, and how they adopt and adapt these technologies to achieve their goals. Gathering papers from various areas of organizational strategy, such as new business models, competitive strategies and knowledge management, the book covers a number of topics, including how innovative technologies improve the life of the individuals, organizations, and societies; how social media can drive fundamental business changes, as their innovative nature allows for interactive communication between customers and businesses; and how developing countries can use these technologies in an innovative way. It also explores the impact of organizations on society through sustainable development and social responsibility, and how ICTs use social media networks in the process of value co-creation, addressing these issues from both private and public sector perspectives and on national and international levels, mainly in the context of technology innovations.