Continuous learning of analytical and machine learning rate of penetration (ROP) models for real-time drilling optimization

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 expensePetroleum and Geosystems Engineerin

Optimization and analysis by CFD of mixing-controlled combustion concepts in compression ignition engines

El trabajo presentado en esta Tesis está motivado por la necesidad de los motores de combustión interna alternativos de reducir el consumo de combustible y las emisiones de CO2 mientras se satisfacen las cada vez más restrictivas regulaciones de emisiones contaminantes. Por lo tanto, el objetivo principal de este estudio es optimizar un sistema de combustión de encendido por compresión controlado por mezcla para probar su potencial como motores de futura generación. Con esta meta se ha desarrollado un sistema automático que combina CFD con métodos de optimización avanzados para analizar y entender las configuraciones óptimas. Los resultados presentados en este trabajo se dividen en dos bloques principales. El primero corresponde a la optimización de un sistema de encendido por compresión convencional alimentado con diésel. El segundo se centra en un concepto de combustión avanzado donde se ha sustituido el fuel por Dimetil-eter. En ambos casos, el estudio no sólo halla una configuración óptima sino que también se describen las relaciones causa/efecto entre los parámetros más relevantes del sistema de combustión. El primer bloque aplica métodos de optimización no-evolutivos a un motor medium-duty alimentado por diésel tratando de minimizar consumo a la vez que se mantienen las emisiones contaminantes por debajo de los estándares de emisiones contaminantes impuestos. Una primera parte se centra en la optimización de la geometría de la cámara de combustión y el inyector. Seguidamente se extiende el estudio añadiendo los settings de renovación de la carga de y de inyección al estudio, ampliando el potencial de la optimización. El estudio demuestra el limitado potencial de mejora de consumo que tiene el motor de referencia al mantener los niveles de emisiones contaminantes. Esto demuestra la importancia de incluir parámetros de renovación de la carga e inyección al proceso de optimización. El segundo bloque aplica una metodología basada en algoritmos genéticos al diseño del sistema de combustión de un motor heavy-duty alimentado con Dimetileter. El estudio tiene dos objetivos, primero la optimización de un sistema de combustión convencional controlado por mezcla con el objetivo de lograr mejorar el consumo y reducir las emisiones contaminantes hasta niveles inferiores a los estándares US2010. Segundo la optimización de un sistema de combustión trabajando en condiciones estequiométricas acoplado con un catalizador de tres vías buscando reducir consumo y controlar las emisiones contaminantes por debajo de los estándares 2030. Ambas optimizaciones incluyen tanto la geometría como los parámetros más relevantes de renovación de la carga y de inyección. Los resultados presentan un sistema de combustión convencional óptimo con una notable mejora en rendimiento y un sistema de combustión estequiométrica que es capaz de ofrecer niveles de NOx menores al 1% de los niveles de referencia manteniendo niveles competitivos de rendimiento. Los resultados presentados en esta Tesis ofrecen una visión extendida de las ventajas y limitaciones de los motores MCCI y el camino a seguir para reducir las emisiones de futuros sistemas de combustión por debajo de los estándares establecidos. A su vez, este trabajo también demuestra el gran potencial que tiene el Dimetil-eter como combustible para futuras generaciones de motores.The work presented in this Thesis was motivated by the needs of internal combustion engines (ICE) to decrease fuel consumption and CO2 emissions, while fulfilling the increasingly stringent pollutant emission regulations. Then, the main objective of this study is to optimize a mixing-controlled compression ignition (MCCI) combustion system to show its potential for future generation engines. For this purpose an automatic system based on CFD coupled with different optimization methods capable of optimizing a complete combustion system with a reasonable time cost was designed together with the methodology to analyze and understand the new optimum systems. The results presented in this work can be divided in two main blocks, firstly an optimization of a conventional diesel combustion system and then an optimization of a MCCI system using an alternative fuel with improved characteristics compared to diesel. Due to the methodologies used in this Thesis, not only the optimum combustion system configurations are described, but also the cause/effect relations between the most relevant inputs and outputs are identified and analyzed. The first optimization block applies non-evolutionary optimization methods in two sequential studies to optimize a medium-duty engine, minimizing the fuel consumption while fulfilling the emission limits in terms of NOx and soot. The first study targeted four optimization parameters related to the engine hardware including piston bowl geometry, injector nozzle configuration and mean swirl number. After the analysis of the results, the second study extended to six parameters, limiting the optimization of the engine hardware to the bowl geometry, but including the key air management and injection settings. The results confirmed the limited benefits, in terms of fuel consumption, with constant NOx emission achieved when optimizing the engine hardware, while keeping air management and injection settings. Thus, including air management and injection settings in the optimization is mandatory to significantly decrease the fuel consumption while keeping the emission limits. The second optimization block applies a genetic algorithm optimization methodology to the design of the combustion system of a heavy-duty Diesel engine fueled with dimethyl ether (DME). The study has two objectives, the optimization of a conventional mixing-controlled combustion system aiming to achieve US2010 targets and the optimization of a stoichiometric mixing-controlled combustion system coupled with a three way catalyst to further control NOx emissions and achieve US2030 emission standards. These optimizations include the key combustion system related hardware, bowl geometry and injection nozzle design as input factors, together with the most relevant air management and injection settings. The target of the optimizations is to improve net indicated efficiency while keeping NOx emissions, peak pressure and pressure rise rate under their corresponding target levels. Compared to the baseline engine fueled with DME, the results of the study provide an optimum conventional combustion system with a noticeable NIE improvement and an optimum stoichiometric combustion system that offers a limited NIE improvement keeping tailpipe NOx values below 1% of the original levels. The results presented in this Thesis provide an extended view of the advantages and limitations of MCCI engines and the optimization path required to achieve future emission standards with these engines. Additionally, this work showed how DME is a promising fuel for future generation engines since it is able to achieve future emission standards while maintaining diesel-like efficiencyEl treball presentat en esta Tesi està motivat per la necessitat dels motors de combustió interna alternatius de reduir el consum de combustible i les emissions de CO2 mentres se satisfan les cada vegada mes restrictives regulacions d'emissions contaminants. Per tant, l'objectiu principal d'este estudi es optimitzar un sistema de combustió d'encesa per compressió controlat per mescla per a provar el seu potencial com a motors de futura generació. Amb esta meta s'ha desenrotllat un sistema automàtic que combina CFD amb mètodes d'optimització avançats per a analitzar i entendre les configuracions òptimes. Els resultats presentats en este treball es dividixen en dos blocs principals. El primer correspon a l'optimització d'un sistema d'encesa per compressió convencional alimentat amb dièsel. El segon se centra en un concepte de combustió avançat on s'ha substituït el fuel per Dimetil-eter. En ambdós casos, l'estudi no sols troba una configuració òptima sinó que també es descriuen les relacions causa/efecte entre els paràmetres més rellevants del sistema de combustió. El primer bloc aplica mètodes d'optimització no-evolutius a un motor mediumduty alimentat per dièsel tractant de minimitzar consum al mateix temps que es mantenen les emissions contaminants per davall dels estàndards d'emissions contaminants impostos. Una primera part se centra en l'optimització de la geometria de la cambra de combustió i l'injector. A continuació s'estén l'estudi afegint els settings de renovació de la càrrega de i d'injecció a l'estudi, ampliant el potencial de l'optimització. L'estudi demostra el limitat potencial de millora de consum que té el motor de referència al mantindre els nivells d'emissions contaminants. Açò demostra la importància d'incloure paràmetres de renovació de la càrrega i injecció al procés d'optimització. El segon bloc aplica una metodologia basada en algoritmes genètics al disseny del sistema de combustió d'un motor heavy-duty alimentat amb Dimetil-eter. L'estudi té dos objectius, primer l'optimització d'un sistema de combustió convencional controlat per mescla amb l'objectiu d'aconseguir millorar el consum i reduir les emissions contaminants fins nivells inferiors als estàndards US2010. Segon l'optimització d'un sistema de combustió treballant en condicions estequiomètriques acoblat amb un catalitzador de tres vies buscant reduir consum i controlar les emissions contaminants per davall dels estàndards 2030. Ambdós optimitzacions inclouen tant la geometria com els paràmetres més rellevants de renovació de la càrrega i d'injecció. Els resultats presenten un sistema de combustió convencional òptim amb una notable millora en rendiment i un sistema de combustió estequiomètrica que és capaç d'oferir nivells de NOx menors al 1% dels nivells de referència mantenint nivells competitius de rendiment. Els resultats presentats en esta Tesi oferixen una visió estesa dels avantatges i limitacions dels motors MCCI i el camï que s'ha de seguir per a reduir les emissions de futurs sistemes de combustió per davall dels estàndards establits. Al seu torn, este treball també demostra el gran potencial que té el Dimetil-eter com a combustible per a futures generacions de motors.Hernández López, A. (2018). Optimization and analysis by CFD of mixing-controlled combustion concepts in compression ignition engines [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/103826TESI

Evolution of the SpaceLiner towards a Reusable TSTO-Launcher

Since a couple of years the DLR launcher systems analysis division is investigating a visionary and extremely fast passenger transportation concept based on rocket propulsion. The fully reusable concept consists of two vertically launched winged stages in parallel arrangement. The space transportation role of the SpaceLiner concept as a TSTO-launcher is now, for the first time, addressed in technical detail. Different mission options to LEO and beyond are traded and necessary modifications of the passenger stage to an unmanned cargo-carrier are investigated and described in this paper. Meanwhile, technical progress of the SpaceLiner ultra-high-speed passenger transport is ongoing at Phase A conceptual design level. Iterative sizings of all major subcomponents in nominal and off-nominal flight conditions have been performed. Potential intercontinental flight routes, taking into account range-safety and sonic boom constraints as well as good reachability from major business centers, are evaluated and flight guidance schemes are established. Alternative passenger cabin and rescue capsule options with innovative morphing shapes were also investigated. The operational and business concept of the SpaceLiner is under definition. The project is on a structured development path and as one key initial step the Mission Requirements Review has been successfully concluded

Integrated service selection, pricing and fullfillment planning for express parcel carriers - Enriching service network design with customer choice and endogenous delivery time restrictions

Express parcel carriers offer a wide range of guaranteed delivery times in order to separate customers who value quick delivery from those that are less time but more price sensitive. Such segmentation, however, adds a whole new layer of complexity to the task of optimizing the logistics operations. While many sophisticated models have been developed to assist network planners in minimizing costs, few approaches account for the interplay between service pricing, customer decisions and the associated restrictions in the distribution process. This paper attempts to fill this research gap by introducing a heuristic solution approach that simultaneously determines the ideal set of services, the associated pricing and the fulfillment plan in order to maximize profit. By integrating revenue management techniques into vehicle routing and eet planning, we derive a new type of formulation called service selection, pricing and fulfillment problem (SSPFP). It combines a multi-product pricing problem with a cycle-based service network design formulation. In order derive good-quality solutions for realistically-sized instances we use an asynchronous parallel genetic algorithm and follow the intuition that small changes to prices and customer assignments cause minor changes in the distribution process. We thus base every new solution on the most similar already evaluated fulfillment plan. This adapted initial solution is then iteratively improved by a newly-developed route-pattern exchange heuristic. The performance of the developed algorithm is demonstrated on a number of randomly created test instances and is compared to the solutions of a commercial MIP-solver.Series: Schriftenreihe des Instituts für Transportwirtschaft und Logistik - Supply Chain Managemen

Scientometric Analysis of Optimisation and Machine Learning Publications

Introduction: Optimisation is an important aspect of machine learning because it helps improve accuracy and reduce errors in the model's predictions. Purpose: The purpose of this research is to identify the global structure of optimization and machine learning. The work specifically looks at the collaborative network of countries in these fields, the top 20 authors in terms of production from 2015–2021, and the co-citation network of articles. Methodology: In this study, co-word analysis and social network analysis were used to conduct a descriptive study based on the scientometric approach and the content analysis method. In this research, around 17,500 articles on optimization and machine learning published between 2015 and 2021 were extracted. An ANOVA was performed to evaluate whether there was a significant difference between betweenness, closeness, and pagerank. The Dimensions database was utilised for the investigation without language constraints. Moreover, Bibliometrix was used for calculation and visualization. Findings: The results revealed a substantial difference between betweenness, proximity, and pagerank, indicating that this research has the potential to bring vital insights into future optimization and machine learning research

Development of a modular Knowledge-Discovery Framework based on Machine Learning for the interdisciplinary analysis of complex phenomena in the context of GDI combustion processes

Die physikalischen und chemischen Phänomene vor, während und nach der Verbrennung in Motoren mit Benzindirekteinspritzung (BDE) sind komplex und umfassen unterschiedliche Wechselwirkungen zwischen Flüssigkeiten, Gasen und der umgebenden Brennraumwand. In den letzten Jahren wurden verschiedene Simulationstools und Messtechniken entwickelt, um die an den Verbrennungsprozessen beteiligten Komponenten zu bewerten und zu optimieren. Die Möglichkeit, den gesamten Gestaltungsraum zu erkunden, ist jedoch durch den hohen Aufwand zur Generierung und zur Analyse der nichtlinearen und multidimensionalen Ergebnisse begrenzt. Das Ziel dieser Arbeit ist die Entwicklung und Validierung eines Datenanalysewerkzeugs zur Erkenntnisgewinnung. Im Rahmen dieser Arbeit wird der gesamte Prozess als auch das Werkzeug als "Knowledge-Discovery Framework" bezeichnet. Dieses Werkzeug soll in der Lage sein, die im BDE-Kontext erzeugten Daten durch Methoden des maschinellen Lernens zu analysieren. Anhand einer begrenzten Anzahl von Beobachtungen wird damit ermöglicht, die untersuchten Gestaltungsräume zu erkunden sowie Zusammenhänge in den Beobachtungen der komplexen Phänomene schneller zu entdecken. Damit können teure und zeitaufwendige Auswertungen durch schnelle und genaue Vorhersagen ersetzt werden. Nach der Einführung der wichtigsten Datenmerkmale im Bereich der BDE Anwendungen wird das Framework vorgestellt und seine modularen und interdisziplinären Eigenschaften dargestellt. Kern des Frameworks ist eine parameterfreie, schnelle und dynamische datenbasierte Modellauswahl für die BDE-typischen, heterogenen Datensätze. Das Potenzial dieses Ansatzes wird in der Analyse numerischer und experimenteller Untersuchungen an Düsen und Motoren gezeigt. Insbesondere werden die nichtlinearen Einflüsse der Auslegungsparameter auf Einström- und Sprayverhalten sowie auf Emissionen aus den Daten extrahiert. Darüber hinaus werden neue Designs, basierend auf Vorhersagen des maschinellen Lernens identifiziert, welche vordefinierte Ziele und Leistungen erfüllen können. Das extrahierte Wissen wird schließlich mit der Domänenexpertise validiert, wodurch das Potenzial und die Grenzen dieses neuartigen Ansatzes aufgezeigt werden