Derivative-Free Optimization with Proxy Models for Oil Production Platforms Sharing a Subsea Gas Network

The deployment of offshore platforms for the extraction of oil and gas from subsea reservoirs presents unique challenges, particularly when multiple platforms are connected by a subsea gas network. In the Santos basin, the aim is to maximize oil production while maintaining safe and sustainable levels of CO2 content and pressure in the gas stream. To address these challenges, a novel methodology has been proposed that uses boundary conditions to coordinate the use of shared resources among the platforms. This approach decouples the optimization of oil production in platforms from the coordination of shared resources, allowing for more efficient and effective operation of the offshore oilfield. In addition to this methodology, a fast and accurate proxy model has been developed for gas pipeline networks. This model allows for efficient optimization of the gas flow through the network, taking into account the physical and operational constraints of the system. In experiments, the use of the proposed proxy model in tandem with derivativefree optimization algorithms resulted in an average error of less than 5% in pressure calculations, and a processing time that was over up to 1000 times faster than the phenomenological simulator. These results demonstrate the effectiveness and efficiency of the proposed methodology in optimizing oil production in offshore platforms connected by a subsea gas network, while maintaining safe and sustainable levels of CO2 content and pressure in the gas stream.N/

Robust production optimization of gas-lifted oil fields

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia de Automação e Sistemas, Florianópolis, 2015.Com a crescente demanda por energia fóssil as operadoras petrolíferas têm buscado determinar planos operacionais que otimizam a produção dos campos em operação para satisfazer a demanda do mercado e reduzir os custos operacionais. Neste contexto, a pesquisa operacional tem se mostrado uma importante ferramenta para determinação dos planos de produção de curto prazo para campos de petróleo complexos. Alguns trabalhos já desenvolveram estratégias para a otimização integrada da produção que visam auxiliar engenheiros de produção e operadores a atingir condições de operação ótimas. Estes avanços científicos atestam o potencial da área de otimização integrada da produção de campos, justificando a busca por estratégias de otimização global e integradas de ativos. Contudo, a incerteza dos parâmetros que caracterizam o reservatório, os poços, fluidos e os diversos processos de produção não vem sendo considerada pelos modelos e algoritmos de otimização da produção diária. Considerando os modelos de produção de curto prazo, estas incertezas podem ser atribuídas a erros de medição , comportamento oscilatório dos sistemas, modelos imprecisos, entre outros. A influência da incerteza dos parâmetros em problemas de otimização tem, desde tempos, sido foco da comunidade de programação matemática. E já foi verificado que soluções de problemas de otimização podem apresentar significativa sensibilidade à pertubações nos parâmetros do dado problema, podendo levar a soluções não factíveis, subótimas ou ambas. Assim, buscando tornar as abordagens de otimização existentes mais confiáveis e robustas às incertezas intrínsecas dos sistemas de produção, esta dissertação investiga a modelagem e tratamento de incertezas na otimização diária da produção e propõe formulações em programação matemática para otimização robusta da produção de poços operados por gas-lift. As formulações representam curvas amostradas através de dados simulados ou medidos que refletem as incertezas dos sistemas de produção. Estas representações levam a formulações robustas em programação matemática inteira mista obtidas pela aproximação das curvas de produção através de linearização por partes. Além disso, este trabalho apresenta os resultados de uma analise computacional comparativa da aplicação da formulação robusta e da formulação nominal a um campo de petróleo em ambiente de simulação, porém considerando simuladores multifásicos amplamente empregados pela indústria do petróleo e gás, que representam a fenomenologia muito próximo da realidade. O primeiro capítulo apresenta a problemática em que estão envolvidos os desenvolvimentos realizados nesta dissertação e um resumo dos capítulos subsequentes. No segundo capítulo alguns conceitos fundamentais são apresentados para a compreensão do trabalho desenvolvido. Este capítulo é dividido em três partes. A primeira parte inicia apresentando brevemente a indústria de petróleo e gás com uma perspectiva histórica, econômica e dos processos envolvidos. Na sequência são expostos conceitos básicos de engenharia de petróleo necessários para o entendimento do sistema de produção utilizado ao longo a dissertação  i.e. gas-lift. Finalmente, o problema de otimização da produção é situado dentro do problema maior, que é o gerenciamento completo das operações de um campo de petróleo, seguido de uma revisão da literatura no que se refere a abordagens clássicas para otimização da produção de campos operados por gas-lift. A segunda parte é uma descrição compacta sobre modelagem de problemas de otimização utilizando programação matemática e na menção dos métodos de solução deste tipo de problema utilizados na parte experimental desta dissertação. A terceira parte começa com uma revisão sobre incerteza em problemas de otimização e sobre as decisões de modelagem enfrentadas quando na presença de problemas de otimização incertos. Na sequência o paradigma de otimização robusta é introduzido e é apresentada uma compilação de alguns dos principais resultados da área de otimização robusta linear. Além disso, ao fim, alguns pontos específicos da teoria de otimização robusta são apresentados pela suas relevâncias para o desenvolvimento da teoria dos capítulos seguintes. O terceiro capítulo inicia com uma discussão sobre as origens das incertezas nos modelos de produção para então prover uma revisão bibliográfica dos poucos trabalhos que mencionam ou lidam com incerteza em sistemas de produção. Na sequência, a incerteza é examinada na perspectiva do problema de otimização. Um sistema simples é usado para exemplificar a metodologia de otimização robusta desenvolvida nesta dissertação. O quarto capítulo apresenta dois problemas padrões de otimização da produção, um contendo poços satélites e outro com poços e completação submarina. Para ambos uma formulação em programação linear inteira mista é descrita considerando valores nominais para todos os parâmetros. Então, para cada problema uma reformulação robusta é implementada considerando incerteza nas curvas de produção do poço. A metodologia utilizada para o primeiro problema é a mesma detalhada no capítulo três, e para o segundo uma extensão da metodologia é proposta para poder lidar com restrições de igualdade incertas. No quinto capítulo são apresentados resultados experimentais de um problema de otimização da produção de um campo com poços satélites. Os resultados obtidos com otimização clássica (nominal) e com otimização robusta são então comparados em um campo de produção sintético instanciado em um simulador multifásico comercial. A solução robusta se mostrou indicada para cenários de operação mais críticos onde factibilidade e segurança são prioridade. No capítulo final uma análise dos resultados obtidos na dissertação é feita sob a perspectiva do possível emprego das técnicas desenvolvidas na indústria de óleo e gás. Apesar de à primeira vista os resultados serem conservadores e de sua utilização parecer limitada, existe potencial para a metodologia ser empregada no caso de situações que priorizam segurança. Além disso a metodologia aqui desenvolvida pode servir como ponto inicial para pesquisas e desenvolvimentos futuros. Uma breve descrição de possíveis trabalhos futuros é feita ao final deste capítulo. O apêndice traz a descrição de algoritmos de amostragem de curvas côncavas desenvolvidos para os experimentos numéricos realizados na dissertação.Abstract : Managing production of complex oil fields with multiple wells and coupled constraints remains a challenge for oil and gas operators. Some technical works developed strategies for integrated production optimization to assist production engineers in reaching best operating conditions. However, these works have neglected the uncertainties in the well-performance curves and production processes, which may have a significant impact on the operating practices. The uncertainties may be attributed to measurement errors, oscillating behavior, and model inaccuracy, among others. To this end, this dissertation investigates how uncertainty might be considered in daily production optimization and proposes formulations in mathematical programming for robust production optimization of gas-lifted oil fields. The formulations represent system-measured and simulated sample curves that reflect the underlying uncertainties of the production system. The representations lead to robust mixed-integer linear programming formulations obtained from piecewise-linear approximation of the production functions. Further, this work presents results from a computational analysis of the application of the robust and nominal formulations to a representative oil fields available in simulation software

Exploring the adoption of a conceptual data analytics framework for subsurface energy production systems: a study of predictive maintenance, multi-phase flow estimation, and production optimization

Als die Technologie weiter fortschreitet und immer stärker in der Öl- und Gasindustrie integriert wird, steht eine enorme Menge an Daten in verschiedenen Wissenschaftsdisziplinen zur Verfügung, die neue Möglichkeiten bieten, informationsreiche und handlungsorientierte Informationen zu gewinnen. Die Konvergenz der digitalen Transformation mit der Physik des Flüssigkeitsflusses durch poröse Medien und Pipeline hat die Entwicklung und Anwendung von maschinellem Lernen (ML) vorangetrieben, um weiteren Mehrwert aus diesen Daten zu gewinnen. Als Folge hat sich die digitale Transformation und ihre zugehörigen maschinellen Lernanwendungen zu einem neuen Forschungsgebiet entwickelt. Die Transformation von Brownfields in digitale Ölfelder kann bei der Energieproduktion helfen, indem verschiedene Ziele erreicht werden, einschließlich erhöhter betrieblicher Effizienz, Produktionsoptimierung, Zusammenarbeit, Datenintegration, Entscheidungsunterstützung und Workflow-Automatisierung. Diese Arbeit zielt darauf ab, ein Rahmenwerk für diese Anwendungen zu präsentieren, insbesondere durch die Implementierung virtueller Sensoren, Vorhersageanalytik mithilfe von Vorhersagewartung für die Produktionshydraulik-Systeme (mit dem Schwerpunkt auf elektrischen Unterwasserpumpen) und präskriptiven Analytik für die Produktionsoptimierung in Dampf- und Wasserflutprojekten. In Bezug auf virtuelle Messungen ist eine genaue Schätzung von Mehrphasenströmen für die Überwachung und Verbesserung von Produktionsprozessen entscheidend. Diese Studie präsentiert einen datengetriebenen Ansatz zur Berechnung von Mehrphasenströmen mithilfe von Sensormessungen in elektrischen untergetauchten Pumpbrunnen. Es wird eine ausführliche exploratorische Datenanalyse durchgeführt, einschließlich einer Ein Variablen Studie der Zielausgänge (Flüssigkeitsrate und Wasseranteil), einer Mehrvariablen-Studie der Beziehungen zwischen Eingaben und Ausgaben sowie einer Datengruppierung basierend auf Hauptkomponentenprojektionen und Clusteralgorithmen. Feature Priorisierungsexperimente werden durchgeführt, um die einflussreichsten Parameter in der Vorhersage von Fließraten zu identifizieren. Die Modellvergleich erfolgt anhand des mittleren absoluten Fehlers, des mittleren quadratischen Fehlers und des Bestimmtheitskoeffizienten. Die Ergebnisse zeigen, dass die CNN-LSTM-Netzwerkarchitektur besonders effektiv bei der Zeitreihenanalyse von ESP-Sensordaten ist, da die 1D-CNN-Schichten automatisch Merkmale extrahieren und informative Darstellungen von Zeitreihendaten erzeugen können. Anschließend wird in dieser Studie eine Methodik zur Umsetzung von Vorhersagewartungen für künstliche Hebesysteme, insbesondere bei der Wartung von Elektrischen Untergetauchten Pumpen (ESP), vorgestellt. Conventional maintenance practices for ESPs require extensive resources and manpower, and are often initiated through reactive monitoring of multivariate sensor data. Um dieses Problem zu lösen, wird die Verwendung von Hauptkomponentenanalyse (PCA) und Extreme Gradient Boosting Trees (XGBoost) zur Analyse von Echtzeitsensordaten und Vorhersage möglicher Ausfälle in ESPs eingesetzt. PCA wird als unsupervised technique eingesetzt und sein Ausgang wird weiter vom XGBoost-Modell für die Vorhersage des Systemstatus verarbeitet. Das resultierende Vorhersagemodell hat gezeigt, dass es Signale von möglichen Ausfällen bis zu sieben Tagen im Voraus bereitstellen kann, mit einer F1-Bewertung größer als 0,71 im Testset. Diese Studie integriert auch Model-Free Reinforcement Learning (RL) Algorithmen zur Unterstützung bei Entscheidungen im Rahmen der Produktionsoptimierung. Die Aufgabe, die optimalen Injektionsstrategien zu bestimmen, stellt Herausforderungen aufgrund der Komplexität der zugrundeliegenden Dynamik, einschließlich nichtlinearer Formulierung, zeitlicher Variationen und Reservoirstrukturheterogenität. Um diese Herausforderungen zu bewältigen, wurde das Problem als Markov-Entscheidungsprozess reformuliert und RL-Algorithmen wurden eingesetzt, um Handlungen zu bestimmen, die die Produktion optimieren. Die Ergebnisse zeigen, dass der RL-Agent in der Lage war, den Netto-Barwert (NPV) durch kontinuierliche Interaktion mit der Umgebung und iterative Verfeinerung des dynamischen Prozesses über mehrere Episoden signifikant zu verbessern. Dies zeigt das Potenzial von RL-Algorithmen, effektive und effiziente Lösungen für komplexe Optimierungsprobleme im Produktionsbereich zu bieten.As technology continues to advance and become more integrated in the oil and gas industry, a vast amount of data is now prevalent across various scientific disciplines, providing new opportunities to gain insightful and actionable information. The convergence of digital transformation with the physics of fluid flow through porous media and pipelines has driven the advancement and application of machine learning (ML) techniques to extract further value from this data. As a result, digital transformation and its associated machine-learning applications have become a new area of scientific investigation. The transformation of brownfields into digital oilfields can aid in energy production by accomplishing various objectives, including increased operational efficiency, production optimization, collaboration, data integration, decision support, and workflow automation. This work aims to present a framework of these applications, specifically through the implementation of virtual sensing, predictive analytics using predictive maintenance on production hydraulic systems (with a focus on electrical submersible pumps), and prescriptive analytics for production optimization in steam and waterflooding projects. In terms of virtual sensing, the accurate estimation of multi-phase flow rates is crucial for monitoring and improving production processes. This study presents a data-driven approach for calculating multi-phase flow rates using sensor measurements located in electrical submersible pumped wells. An exhaustive exploratory data analysis is conducted, including a univariate study of the target outputs (liquid rate and water cut), a multivariate study of the relationships between inputs and outputs, and data grouping based on principal component projections and clustering algorithms. Feature prioritization experiments are performed to identify the most influential parameters in the prediction of flow rates. Model comparison is done using the mean absolute error, mean squared error and coefficient of determination. The results indicate that the CNN-LSTM network architecture is particularly effective in time series analysis for ESP sensor data, as the 1D-CNN layers are capable of extracting features and generating informative representations of time series data automatically. Subsequently, the study presented herein a methodology for implementing predictive maintenance on artificial lift systems, specifically regarding the maintenance of Electrical Submersible Pumps (ESPs). Conventional maintenance practices for ESPs require extensive resources and manpower and are often initiated through reactive monitoring of multivariate sensor data. To address this issue, the study employs the use of principal component analysis (PCA) and extreme gradient boosting trees (XGBoost) to analyze real-time sensor data and predict potential failures in ESPs. PCA is utilized as an unsupervised technique and its output is further processed by the XGBoost model for prediction of system status. The resulting predictive model has been shown to provide signals of potential failures up to seven days in advance, with an F1 score greater than 0.71 on the test set. In addition to the data-driven modeling approach, The present study also in- corporates model-free reinforcement learning (RL) algorithms to aid in decision-making in production optimization. The task of determining the optimal injection strategy poses challenges due to the complexity of the underlying dynamics, including nonlinear formulation, temporal variations, and reservoir heterogeneity. To tackle these challenges, the problem was reformulated as a Markov decision process and RL algorithms were employed to determine actions that maximize production yield. The results of the study demonstrate that the RL agent was able to significantly enhance the net present value (NPV) by continuously interacting with the environment and iteratively refining the dynamic process through multiple episodes. This showcases the potential for RL algorithms to provide effective and efficient solutions for complex optimization problems in the production domain. In conclusion, this study represents an original contribution to the field of data-driven applications in subsurface energy systems. It proposes a data-driven method for determining multi-phase flow rates in electrical submersible pumped (ESP) wells utilizing sensor measurements. The methodology includes conducting exploratory data analysis, conducting experiments to prioritize features, and evaluating models based on mean absolute error, mean squared error, and coefficient of determination. The findings indicate that a convolutional neural network-long short-term memory (CNN-LSTM) network is an effective approach for time series analysis in ESPs. In addition, the study implements principal component analysis (PCA) and extreme gradient boosting trees (XGBoost) to perform predictive maintenance on ESPs and anticipate potential failures up to a seven-day horizon. Furthermore, the study applies model-free reinforcement learning (RL) algorithms to aid decision-making in production optimization and enhance net present value (NPV)

Choke management and production optimization in oil and gas fields

When a well is brought on production, the selection of the optimum choke management strategy should aim towards maximizing well productivity and minimizing the risk of completion or wellbore failures. Until recently, ramp-up practices were based on liquid rate recommendations or empirical guidelines on choke sizes for the early life of a well. The objective of this dissertation is to establish a systematic method for the design of choke management strategies and flowback operations under wellbore completion and reservoir constraints. In order to account for multi-well pressure interference through the surface facilities, an integration scheme is proposed for the effective coupling of the well models with the surface gathering network. Finally, an optimization framework is deployed to maximize the daily operating income by properly adjusting well and network controls. In the first part of the dissertation, we study choke management on an individual well basis. A general framework is introduced for comparing drawdown strategies for conventional and unconventional wells. Using analytical and numerical reservoir models we conclude that in conventional open-hole completions no more than 70% of the drawdown should be applied in less than 30% of the ramp-up period. In formations characterized by high diffusivity (e.g. high permeability gas formations), the bottom-hole-pressure should be reduced linearly with time. Using nodal analysis, a systematic method is proposed for translating a set of wellbore, completion and reservoir constraints into a choke management schedule. Illustrative examples are presented both for conventional and unconventional wells. For hydraulically fractured wells, we introduced a coupled rate-stress criterion for mitigating proppant flowback and fracture closure near the wellbore. Application of the method suggests drawdown rates which are in agreement with successfully implemented field practices (5-10 psi/hour). In order to capture well interference through the surface network, a multiphase (black-oil) pipeline network model has been developed. The network solver is formulated using fractional-flow theory, assuming steady state flow and concurrent flow of oil, water and gas phases. Using network topology, closed pipeline loops are unified into clusters where loop equations are solved using the Fletcher-Reeves conjugate gradient method. The network solver is validated using published network solutions, compared with field data and benchmarked against commercial network solvers. The well models are integrated with the surface gathering network using an explicit scheme that performs multi-point surface nodal analysis using fixed-point iteration. The integration scheme converges linearly and accurately captures well interference both for naturally flowing wells and wells on artificial lift. The integration scheme (forward model) is combined with various gradient based and derivative free optimization routines to optimize the well and network controls for a synthetic field. We observe that the use of integrated modeling can achieve significant improvements in terms of daily operating income (by up to 30%). Finally, we introduce a reduced variable range approach which can accelerate the performance of sampling and global search methods in complex production systems. This work introduces a systematic method for the design of choke management practices and presents new methods for integrating well models with the surface pipeline networkPetroleum and Geosystems Engineerin

Modelling of flows in vertical pipes and its application to multiphase flow metering at high gas content and to the prediction of well performance

Imperial Users onl

Mechanisms and control of water inflow to wells in gas reservoirs with bottom water drive

Water inflow may cease production of gas wells, leaving a significant amount of gas in the reservoir. Conventional technologies of gas well dewatering remove water from inside the wellbore without controlling water at its source. This study addresses mechanisms of water inflow to gas wells and a new completion method to control it. In a vertical oil well, the water cone top is horizontal, but in a gas well, the gas/water interface tends to bend downwards. It could be economically possible to produce gas-water systems without water breakthrough. Non-Darcy flow effect (NDFE), vertical permeability, aquifer size, density of well perforation, and flow behind casing increase water coning/inflow to wells in homogeneous gas reservoirs with bottom water. NDFE is important in low-productivity gas reservoirs with low porosity and permeability. Also, NDFE should be considered in the reservoir (outside the well) to describe properly gas wells performance. A particular pattern of water rate in a gas well with leaking cement is revealed. The pattern might be used to diagnose the leak. The pattern explanation considers cement leak flow hydraulics. Water production depends on leak properties. Advanced methods at parametric experimental design and statistical analysis of regression, variance, with uncertainty (Monte Carlo) were used building economic model at gas wells with bottom water. Completion length optimization reveled that penetrating 80% of the gas zone gets the maximum net present value. The most promising Downhole Water Sink (DWS) installation in gas wells includes dual completion with an isolating packer and gravity gas-water separation at the bottom completion. In comparison to Downhole Gas/Water Separation wells, the DWS wells would recover about the same amount of gas but much sooner. The best DWS completion design should comprise a short top completion penetrating 20% - 40% of the gas zone, a long bottom completion penetrating the remaining gas zone, and vigorous pumping of water at the bottom completion. Being as close as practically possible the two completions are only separated by a packer. DWS should be installed early after water breakthrough