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 Methods for Petroleum Well Optimization written by Rasool Khosravanian and published by Gulf Professional Publishing. This book was released on 2021-09-22 with total page 554 pages. Available in PDF, EPUB and Kindle. Book excerpt: Drilling and production wells are becoming more digitalized as oil and gas companies continue to implement machine learning andbig data solutions to save money on projects while reducing energy and emissions. Up to now there has not been one cohesiveresource that bridges the gap between theory and application, showing how to go from computer modeling to practical use. Methodsfor Petroleum Well Optimization: Automation and Data Solutions gives today’s engineers and researchers real-time data solutionsspecific to drilling and production assets. Structured for training, this reference covers key concepts and detailed approaches frommathematical to real-time data solutions through technological advances. Topics include digital well planning and construction,moving teams into Onshore Collaboration Centers, operations with the best machine learning (ML) and metaheuristic algorithms,complex trajectories for wellbore stability, real-time predictive analytics by data mining, optimum decision-making, and case-basedreasoning. Supported by practical case studies, and with references including links to open-source code and fit-for-use MATLAB, R,Julia, Python and other standard programming languages, Methods for Petroleum Well Optimization delivers a critical training guidefor researchers and oil and gas engineers to take scientifically based approaches to solving real field problems. Bridges the gap between theory and practice (from models to code) with content from the latest research developments supported by practical case study examples and questions at the end of each chapter Enables understanding of real-time data solutions and automation methods available specific to drilling and production wells, suchas digital well planning and construction through to automatic systems Promotes the use of open-source code which will help companies, engineers, and researchers develop their prediction and analysissoftware more quickly; this is especially appropriate in the application of multivariate techniques to the real-world problems of petroleum well optimization

Download or read book Advances in Artificial Systems for Logistics Engineering III written by Zhengbing Hu and published by Springer Nature. This book was released on 2023-07-15 with total page 1107 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book comprises high-quality refereed research papers presented at the 3rd International Conference on Artificial Intelligence and Logistics Engineering (ICAILE2023), held in Wuhan, China, on March 11–12, 2023, organized jointly by Wuhan University of Technology, Nanning University, the National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", Huazhong University of Science and Technology, the Polish Operational and Systems Society, Wuhan Technology and Business University, and the International Research Association of Modern Education and Computer Science. The topics discussed in the book include state-of-the-art papers in artificial intelligence and logistics engineering. It is an excellent source of references for researchers, graduate students, engineers, management practitioners, and undergraduate students interested in artificial intelligence and its applications in logistics engineering.

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 0 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 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 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 Advanced Data Analytics for Optimized Drilling Operations Using Surface and Downhole Data written by Rahmat Ashari and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Minimizing well construction cost continues to be a dominant performance motivator for operators who rely on subsurface energy. Drilling optimization is a primary approach that drilling engineers use to lower costs, and this can be achieved by focusing on two goals: maximizing rate of penetration (ROP) and minimizing non-productive time (NPT). With the advances in sensors and computational technologies, data-driven approaches have become increasingly popular to achieve these goals. Nonetheless, experiences reveal that many operators underutilize the values that can be derived from their data. This research study develops data analytics approaches that incorporate surface and downhole data from drilling operations. In particular, two topics were studied in detail: connection recipes and bit damage tracking. The first topic aimed to develop an optimum approach to execute drillstring connections –i.e., the “connection recipes”– that yields a minimum level of vibrations so as to prevent downhole tool failures. The second topic specifically concerns drill bits, where the goal is to develop a workflow to track the state of bit damage in real time. An actionable outcome from such a workflow is the construction of a pull bit criterion, which serves as a guideline for drillers to decide whether a trip out is necessary. The data analytics workflows presented in this study will equip engineers with the capabilities to not only standardize connection-making practices to prevent downhole tools failures, but also optimize drilling performance by real-time bit damage monitoring that further helps lower NPT. For the connection recipes project, the data studied presented several unfavorable practices relating to surface rotational speed and weight on bit promoting the occurrences of stick-slip events, whirling, and shocks when going back to bottom and going off-bottom. Based on these observations, safer connection practices were identified. For the bit damage tracking project, bit wear and tooth wear metrics were computed. When applied on historical real-time drilling data, they revealed trip outs that were conducted either too early or too late. Based on several case studies, a bit pull criterion was subsequently developed. The two projects leveraged surface and downhole drilling data to produce insights and workflows that are deployable in real-time for drilling optimization

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 Industry 4 0 and Advanced Manufacturing written by Amaresh Chakrabarti and published by Springer Nature. This book was released on 2022-07-23 with total page 478 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents selected papers from the 2nd International Conference on Industry 4.0 and Advanced Manufacturing held at the Indian Institute of Science, Bangalore and includes deliberations from stakeholders in manufacturing and Industry 4.0 on the nature, needs, challenges, opportunities, problems, and solutions in these transformational areas. Special emphasis is placed on exploring avenues for creating a vision of, and enablers for, sustainable, affordable, and human-centric Industry 4.0. The book showcases cutting edge practice, research, and educational innovation in this crucial and rapidly evolving area. This book will be useful to researchers in academia and industry, and will also be useful to policymakers involved in creating ecosystems for implementation of Industry 4.0.

Download or read book Drilling Engineering Problems and Solutions written by M. E. Hossain and published by John Wiley & Sons. This book was released on 2018-06-19 with total page 551 pages. Available in PDF, EPUB and Kindle. Book excerpt: Completely up to date and the most thorough and comprehensive reference work and learning tool available for drilling engineering, this groundbreaking volume is a must-have for anyone who works in drilling in the oil and gas sector. Petroleum and natural gas still remain the single biggest resource for energy on earth. Even as alternative and renewable sources are developed, petroleum and natural gas continue to be, by far, the most used and, if engineered properly, the most cost-effective and efficient, source of energy on the planet. Drilling engineering is one of the most important links in the energy chain, being, after all, the science of getting the resources out of the ground for processing. Without drilling engineering, there would be no gasoline, jet fuel, and the myriad of other "have to have" products that people use all over the world every day. Following up on their previous books, also available from Wiley-Scrivener, the authors, two of the most well-respected, prolific, and progressive drilling engineers in the industry, offer this groundbreaking volume. They cover the basic tenets of drilling engineering, the most common problems that the drilling engineer faces day to day, and cutting-edge new technology and processes through their unique lens. Written to reflect the new, changing world that we live in, this fascinating new volume offers a treasure of knowledge for the veteran engineer, new hire, or student. This book is an excellent resource for petroleum engineering students, reservoir engineers, supervisors & managers, researchers and environmental engineers for planning every aspect of rig operations in the most sustainable, environmentally responsible manner, using the most up-to-date technological advancements in equipment and processes.

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 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 Fundamentals of Sustainable Drilling Engineering written by M. Enamul Hossain and published by . This book was released on 2015 with total page 785 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Industrial Applications of Machine Learning written by Pedro Larrañaga and published by CRC Press. This book was released on 2018-12-12 with total page 349 pages. Available in PDF, EPUB and Kindle. Book excerpt: Industrial Applications of Machine Learning shows how machine learning can be applied to address real-world problems in the fourth industrial revolution, and provides the required knowledge and tools to empower readers to build their own solutions based on theory and practice. The book introduces the fourth industrial revolution and its current impact on organizations and society. It explores machine learning fundamentals, and includes four case studies that address a real-world problem in the manufacturing or logistics domains, and approaches machine learning solutions from an application-oriented point of view. The book should be of special interest to researchers interested in real-world industrial problems. Features Describes the opportunities, challenges, issues, and trends offered by the fourth industrial revolution Provides a user-friendly introduction to machine learning with examples of cutting-edge applications in different industrial sectors Includes four case studies addressing real-world industrial problems solved with machine learning techniques A dedicated website for the book contains the datasets of the case studies for the reader's reproduction, enabling the groundwork for future problem-solving Uses of three of the most widespread software and programming languages within the engineering and data science communities, namely R, Python, and Weka

Download or read book Petroleum Abstracts written by and published by . This book was released on 1992 with total page 1716 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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 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.