Numerical and Computational Strategy for Pressure-Driven Steady-State Simulation of Oilfield Production

Within the TINA (Transient Integrated Network Analysis) research project and in partnership with Total, IFP is developing a new generation of simulation tool for flow assurance studies. This integrated simulation software will be able to perform multiphase simulations from the wellbore to the surface facilities. The purpose of this paper is to define, in a CAPE-OPEN compliant environment, a numerical and computational strategy for solving pressure-driven steady-state simulation problems, i.e. pure simulation and design problems, in the specific context of hydrocarbon production and transport from the wellbore to the surface facilities

A STUDY OF PRODUCTION OPTIMIZATION USING PROSPER

The production optimization of oil and gas wells using computerized well model has become a successful technique contributing towards the better efficiency and higher production of many wells. Well modeling using PROSPER, one of components of the Integrated Production Modeling (IPM) was implemented in Field X which is located in Peninsular Malaysia. The model carries all the properties of the well with detailed description of the reservoir and vertical lift performance. The process includes four phases. First phase was building well model by using PVT, IPR, surface and equipment data. Second phase was constructing well matching based on the monthly well test data. This helps to ensure that the model is well calibrated and constructed. Third phase was performing well analysis based on the well matching results. Well analysis can be performed by evaluating each components of the producing well. Often this procedure will identify possible problems occurred in the production components which restricting flow and causing the well to produce in a manner that the maximum potential rate not achieved. Overall, this production optimization technique permits engineer to come out with some modifications and suggestions which is expected to increase the productio

A new optimisation procedure for uncertainty reduction by intelligent wells during field development planning

The uncertainty in the produced oil volume can be minimised by substituting intelligent wells (IWs) for conventional wells. A previous study showed that IWs reduce the impact of geological uncertainty on the production forecast (Birchenko, Demyanov et al. 2008). This investigation has now been extended to the “dynamic” parameters (fluid contacts, relative permeabilities, aquifer strength and zonal skin). The efficiency of the IWs in reducing the total production uncertainty due to the reservoir’s dynamic parameters was found to be comparable to that reported for the static parameters. However, this later study identified that the result was strongly dependent on the strategy employed to optimise the field’s performance. Experience has shown that challenges arise while using commercial software for optimisation of a typical, modern field with multiple reservoirs and a complex surface production network. Inclusion of the optimisation algorithm dramatically increases the calculation time in addition to showing stability and convergence problems. This thesis describes the development of a novel method of a reactive control strategy for ICVs that is both robust and computationally fast. The developed method identifies the critical water cut threshold at which a well will operate optimally when on/off valves are used. This method is not affected by the convergence problems which have lead to many of the difficulties associated with previous efforts to solve our non-linear optimisation problem. Run times similar to the (non-optimised) base case are now potentially possible and, equally importantly, the optimal value calculated is similar to the result from the various optimisation software referred to above. The approach is particularly valuable when analysing the impact of uncertainty on the reservoir’s dynamic and static parameters, the method being convergent and independent of the point used to initiate the optimization process. “Tuning” the algorithm’s optimisation parameters in the middle of the calculation is no longer required; thus ensuring the results from the many realisations are comparable

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)

Screening of reservoir types for decision-making on the application of intelligent wells

Abstract unavailable please refer to PD

Nonlinear Model Predictive Control of an Oil Well with Echo State Networks

In oil production platforms, processes are nonlinear and prone to modeling errors, as the flowregime and components are not entirely known and can bring about structural uncertainties,making designing predictive control algorithms for this type of system a challenge. In thiswork, an efficient data-driven framework for Model Predictive Control (MPC) using Echo StateNetworks (ESN) as prediction model is proposed. Differently from previous work, the ESN model for MPC is only linearized partially: while the free response of the system is kept fullynonlinear, only the forced response is linearized. This MPC framework is known in the literatureas the Practical Nonlinear Model Predictive Controller (PNMPC). In this work, by using theanalytically computed gradient from the ESN model, no finite difference method to compute derivatives is needed as in PNMPC. The proposed method, called PNMPC-ESN, is applied tocontrol a simplified model of a gas lifted oil well, managing to successfully control the plant,obeying the established constraints while maintaining setpoint tracking

Controle ativo de golfadas em poços de petróleo offshore

A produção de petróleo e gás é caracterizada pelo transporte dos fluidos do reservatório até as instalações de processamento, onde as correntes produzidas são tratadas e enquadradas de acordo com as especificações de comercialização, descarte ou reinjeção. A etapa de transporte dos fluidos até a planta de processamento é governada por complexos fenômenos de escoamento multifásico em longas tubulações, principalmente quando o ambiente de produção é marítimo. Esta combinação de cenários pode induzir o surgimento de padrões cíclicos de oscilação de pressão-vazão no escoamento do poço. Este fenômeno é classificado como um ciclo limite estável, que no estudo da dinâmica de sistemas é um comportamento não linear gerado por uma trajetória fechada no espaço de fase com formato de espiral quando o tempo tende ao infinito. Na indústria do petróleo, este ciclo limite é chamado de golfada, escoamento intermitente, slugging ou slug flow e é constituído pelo deslocamento de ondas de massa de fluido nas linhas de produção, o que coloca as instalações em risco e reduz a capacidade produtiva dos poços. Muitas publicações sobre métodos de controle deste fenômeno têm discutido o problema desde a década de 1980, contudo muitos pontos permanecem em aberto visto a complexidade e diversidade de cenários possíveis. Além disso, poucas aplicações em campo são reportadas na literatura, sendo que a maior parte dos trabalhos práticos publicados apresenta descrições limitadas que dificultam a replicação das metodologias utilizadas. Portanto, esta tese objetiva explorar abordagens de controle por retroalimentação (controle ativo) para problemas de ciclo limite em poços de petróleo em águas profundas e ultraprofundas. Aspectos como controle preditivo, multivariável e não linear são discutidos e explorados no trabalho, culminando em duas diferentes aplicações de campo descritas em detalhes. Até onde se sabe, esta é a primeira vez que estratégias de controle preditivo e de controle não linear são apresentadas na literatura em aplicações reais de controle ativo de golfadas. Como resultado, foi possível minimizar os efeitos adversos das golfadas e aumentar a produção dos poços em cerca de 10% nas aplicações reais.Oil and gas production is characterized by the transport of fluids from the reservoir to the processing facilities, where the streams produced are treated and fitted to commercial, disposal or reinjection specifications. The fluid transport stage to the processing plant is governed by complex multiphase flow phenomena in long pipelines, especially when the production environment is marine. This combination of scenarios can induce the appearance of singularities in the flow stability, resulting in the formation of cyclic flow patterns. This phenomenon is classified as a stable limit cycle, which in system dynamics means a nonlinear behavior generated by a closed trajectory in the phase space with a spiral shape when time tends to infinity. In the oil industry, this limit cycle is called slugging, slug flow or intermittent flow and causes pressure and flow waves in the well, exposing the facilities to risk and reducing production capacity. Several publications on methods of controlling this phenomenon have discussed the problem since the 1980s, however many points remain open due to the complexity and diversity of possible scenarios. Furthermore, few field applications are reported in the literature, and most of the published works present poor descriptions that make it hard to replicate the methodologies deployed. Therefore, this thesis aims to explore feedback control approaches (active control) for limit cycle problems in oil wells in deep and ultra-deepwaters environment. Aspects such as predictive, multivariable and nonlinear control are discussed and explored in this work, resulting in two different field applications described in detail. As far as is known, this is the very first time that predictive control and nonlinear control strategies are presented in the literature to deal with slugging in actual applications. As a result, it was possible to minimize the adverse effects of the slug flow and increase the production of the wells by about 10% in actual deployments

A study of the impact of intelligent well technology on reservoir development

Abstract unavailable please refer to PDF

Transition to intervention-less gaslift surveillance: decision making and analysis process.

Decisions in the oil and gas industry determine the direction and course of millions of actions every year. Informally, decision-making can be defined as choosing the alternative that best fits a set of goals. Simple as it is, in the context of oil and gas operations, this statement requires complex analysis and structural methods to comply with. Good decision-making is a skill which, like any other skill, can be improved by learning and practice. This thesis aims to explore the framework of the decision-making process related to production optimization on aging gas-lifted brownfields. As these fields have reached the end of their primary recovery phase, decisions are required to implement efficient strategies to further optimize their operations. One of the challenges is the lack of quality subsurface and production data that can improve the decision-making process. In addition, different operational and organizational realities make the decision-making and implementation of those complex and demanding. Decisions with respect to production optimization are heavily dependent on company goals, financial and operational strategies, and facility constraints. This thesis will apply a multi-objective decision analysis theory to this decision-making process, and will provide a framework, trying to help engineers to navigate through this inherent multi-layer analytical and organizational structure. The key contribution of this study is to provide a basic understanding of the possibilities and limitations of utilizing different gaslift surveillance techniques. Additionally, the study will provide a decision-oriented methodology for implementing gaslift surveillance practices. Finally, the goal of this research is to provide a framework to inform process to any decision related to operation of oil and gas fields considering multiple objectives and stakeholders