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The shipping industry faces a significant challenge as it needs to significantly lower the amounts of Green House Gas emissions at the same time as it is expected to meet the rising demand. Traditionally, optimising the fuel consumption for ships is done during the ship design stage and through operating it in a better way, for example, with more energy-efficient machinery, optimising the speed or route. During the last decade, the area of machine learning has evolved significantly, and these methods are applicable in many more fields than before. The field of ship efficiency improvement by using Machine Learning methods is significantly progressing due to the available volumes of data from online measuring, experiments and computations. This amount of data has made machine learning a powerful tool that has been successfully used to extract information and intricate patterns that can be translated into attractive ship energy savings. This article presents an overview of machine learning, current developments, and emerging opportunities for ship efficiency. This article covers the fundamentals of Machine Learning and discusses the methodologies available for ship efficiency optimisation. Besides, this article reveals the potentials of this promising technology and future challenges.
- Transportation > Marine (1.00)
- Transportation > Freight & Logistics Services > Shipping (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Reinforcement Learning (0.69)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning > Regression (0.47)
A Hybrid Modeling Approach to Production Control Optimization Using Dynamic Mode Decomposition
Zalavadia, Hardikkumar (Texas A&M University) | Sankaran, Sathish (Anadarko Petroleum Corporation) | Kara, Mustafa (Anadarko Petroleum Corporation) | Sun, Wenyue (Anadarko Petroleum Corporation) | Gildin, Eduardo (Texas A&M University)
Abstract Model-based field development planning and optimization often require computationally intensive reservoir simulations, where the models need to be run several times in the context of input uncertainty or seeking optimal results. Reduced Order Modeling (ROM) methods are a class of techniques that are applied to reservoir simulation to reduce model complexity and speed up computations, especially for large scale or complex models that may be quite useful for such optimization problems. While intrusive ROM methods (such as proper orthogonal decomposition (POD) and its extensions, trajectory piece-wise linearization (TPWL), Discrete Empirical Interpolation Method (DEIM) etc.) have been proposed for application to reservoir simulation problems, these remain inaccessible or unusable for a large number of practical applications that use commercial simulators. In this paper, we describe a novel application of a non-intrusive ROM method, namely dynamic mode decomposition (DMD). We specifically look at reducing the time complexity involved in well control optimization problem, using a variant of DMD called DMDc (DMD with control). We propose a workflow using a training dataset of the wells and predict the state solution (pressure and saturation) for a new set of bottomhole pressure profiles encountered during the optimization runs. We use a novel strategy to select the basis dimensions to prevent unstable solutions. Since the objective function of the optimization problem is usually based on fluid production profiles, we propose a strategy to predict the fluid production rates from the predicted states from DMDc using machine learning techniques. The features for this machine learning problem are designed based on the physics of fluid flow through well perforations, which result in very accurate rate predictions. We compare the proposed methodology using another variant of DMD called ioDMD (input-ouput DMD) for system identification to predict output production flow rates. The methodology is demonstrated on a benchmark case and a Gulf of Mexico deepwater field that shows significant time reduction in production control optimization problem with about 30 โ 40 times speedup using the proposed DMDc workflow as compared to fine scale simulations, while preserving the accuracy of the solutions. The proposed "non-intrusive" method in this paper to reduce model complexity can substantially increase the range of application of ROM methods for practical field development and reservoir management.
Abstract The study presents a conceptual model that uses generative AI to automate project scheduling for complex oil and gas capital projects. The model uses historical project schedules and expert-built process maps to generate a full-scale schedule including dependencies, resources, and duration. The study highlights the limitations of traditional scheduling methods based solely on planner ability and discusses the potential benefits of using AI, including improved accuracy and efficiency. The conceptual model aims to address project schedule issues and the model begins by collecting data from past projects to create a historical database, which is used to train a generative AI algorithm to perfect the process maps. Process maps serve as a visual representation of the project schedule, detailing the steps and dependencies involved in a project, and are used to find potential issues or bottlenecks in the schedule and recommend solutions based on historical data. Text summarization and cataloging techniques are used to extract key information and categorize them based on project type, driver, size, stage, etc. After examining the available literature and conducting a market analysis, it was found that the potential solutions for oil and gas scheduling requirements were not enough. The quality of the project schedule can be affected by several factors, leading to associated integrity issues, project delays, cost overruns, and other negative consequences. Addressing these challenges upfront requires a robust and reliable method that incorporates historical data, process maps, and AI-driven analysis to create accurate and transparent project schedules. Observations revealed that the model's ability to learn from historical project schedules and expert knowledge was crucial to its success. The use of expert-built process maps supplied a comprehensive and accurate framework for generating project schedules, improving the accuracy and efficiency of the generative AI algorithm. The proposed model offers a streamlined approach to project scheduling that can help reduce the potential for human error and improve project outcomes. The use of generative AI for project scheduling has the potential to revolutionize the oil and gas industry by supplying a more efficient and accurate method for managing complex projects. Further research and development of this approach can lead to continued improvements in accuracy and efficiency, ultimately leading to better project outcomes.
- Asia > Middle East > UAE (0.46)
- North America > United States > Texas (0.28)
Abstract Over the past decade, Machine Learning or, more generally, Artificial Intelligence, has made a stelar entry into the O&G world and is today routinely used from exploration all the way up to retail. At the same time, physics-based tools are still being seen as key for value generation and the O&G community is increasingly looking at combining "traditional" with "new" technologies in what has come to be known as "hybrid" tools. In this paper we explain how we Baker Hughes, C3 and KBC are combining Physics and Machine Learning to create Hybrid Digital Twins that help operators improve their margins, reduce their emissions and, in general, position themselves for sustainable growth. Before they are in operation, plants (and oilfields alike) are typically simulated using physics-based tools. In practice, therefore, physics-based simulation models pre-date any plant data. Once plant data starts being produced, however, it becomes clear they do not match exactly the theoretical models that were used before operation. It also becomes clear that plants are operated in a very narrow range, due to quality, production and HSE requirements. In this paper we document a hybrid approach for a digital twin used to generate optimized targets to a Crude Unit operation: synthetic data has been generated to overcome the limitations of available plant data. These synthetic data have then been used as additional training input for ML models, complementing instrumentation data, while at the same time, instrumentation data is used for calibration of the physics-based model. This hybrid approach has been applied to a Crude Unit, in an optimization use case, with remarkable results. The final goal was to make energy optimization targets to operations through its machine learning algorithms which are based on two years of historical data optimized by a rigorous process simulator. The targets are manually downloaded to the unit multivariable controller by the operators. The hybrid approach has been found to combine the benefits of each type of technique: First Principles provides a sound bases for (limited) extrapolation, while Machine Learning ensures the First Principles models remain tuned to the reality of the process and complements the physics in areas where it is insufficient to model the reality (as is the case of equipment performance degradation/failure prediction). The range of what-if studies (and, correspondingly, optimization options) is thus radically extended. This paper shows incremental benefits in optimization cases illustrated by a Crude Unit use case, resulting from the application of a hybrid digital twin using both physics-based and machine-learning models. The hybrid approach allows optimizers to evaluate scenarios beyond the historical operating envelope and opens the door to incorporating equipment condition considerations.
Unlocking Completion Design Optimization Using an Augmented AI Approach
Ma, Zheren (Quantum Reservoir Impact LLC) | Davani, Ehsan (Quantum Reservoir Impact LLC) | Ma, Xiaodan (Quantum Reservoir Impact LLC) | Lee, Hanna (Quantum Reservoir Impact LLC) | Arslan, Izzet (Quantum Reservoir Impact LLC) | Zhai, Xiang (Quantum Reservoir Impact LLC) | Darabi, Hamed (Quantum Reservoir Impact LLC) | Castineira, David (Quantum Reservoir Impact LLC)
Abstract An Augmented AI approach has been developed to optimize completion design parameters and access the full potential of unconventional assets by leveraging big data sculpting, domain-induced feature engineering, and robust and explainable machine learning models with quantified uncertainty. This method unlocks the full potential of a well using completion design parameters optimization that considers all the factors that impact well performance, geological characteristics, well trajectory, spacing, etc. By leveraging basin-level knowledge captured by big data sculpting with the use of uncertainty quantification, Augmented AI can provide quick and science-based answers for completion optimization, and also assess the full potential of an asset in unconventional reservoirs. By leveraging computer vision and natural language processing techniques, unstructured data from various sources were deciphered, combined and organized into a structured database. Imputation techniques were used to fill the gaps of missing data. With the Augmented AI approach, the median accuracies of IP and EUR predictions for new drills is around 90%, which often outperforms industry-standard type curving methods. With the explainable machine learning (ML) model, the direct impact of completion design parameters on well performance is deconvoluted among other parameters, such as engineering and geological attributes. The prediction also comes with an 80% confidence interval to quantify the prediction uncertainties, which allows for better risk management and confident business decision making. With the ML model and given economic inputs and metrics, many sensitivity analyses are performed to evaluate optimized completion design parameters. The proposed Augmented AI approach has been deployed to Eagle Ford wells.
- Geology > Geological Subdiscipline (1.00)
- Geology > Petroleum Play Type > Unconventional Play > Shale Play (0.70)
- North America > United States > West Virginia > Appalachian Basin > Marcellus Shale Formation (0.99)
- North America > United States > Virginia > Appalachian Basin > Marcellus Shale Formation (0.99)
- North America > United States > Texas > Permian Basin > Yeso Formation (0.99)
- (36 more...)
- Information Technology > Artificial Intelligence > Machine Learning (1.00)
- Information Technology > Data Science > Data Mining > Big Data (0.55)