... environment. In recent years, multi-objective optimization algorithms, machine learning models, and intelligent optimization tools have been introduced into mooring system design, demonstrating their potential for addressing high-dimensional nonlinear problems. Integrated approaches combining neural networks...

... – originally proposed by the present authors for regional prediction of ocean waves – to the operational forecasting of the Loop Current and Loop Current Eddies (LC/LCEs) in the Gulf of Mexico (GoM). The approach consists of using an attention-based long short-term memory recurrent neural network to learn...

... neural network artificial intelligence reservoir surveillance production monitoring f1-score production control machine learning dataset detection anomaly detection anomaly generalized model tranad specialized model minmaxscaler anomaly type variability effectiveness non-anomalous data...

...) soils offshore New Jersey. A feedforward neural network (FNN) algorithm was adopted in the current exercise, which refers to the data moving into one direction from the input layer through the multiple hidden layers and finally to the output layer ( Figure 2 , Ibrahem et al. (2020) ). The primary goal...

... ). Many image processing methods have developed for the underwater applications and many have been transferred to the mobile computing devices with limited computational power [ 6 ]. Recently, advancements in deep learning have enabled deep neural networks (DNNs) to achieve remarkable success in solving...

... in reducing manual effort for engineers, and (iv) advancing towards a new DT functionality that derives an asset 3D representation, that is faithful to physical reality, through the automatic interpretation of point cloud scans. deep learning point cloud neural network artificial intelligence...

... Forest, and deep learning techniques such as Neural Networks. An ensemble model, trained on the outputs of these models, consolidates their predictions into a final assessment. This ensemble prediction informs the decision to undertake maintenance operations, either preemptive or corrective, based...

..., Propane, Isobutane) that are closely related to the Rate of Penetration (ROP). These parameters are then input into a deep neural network (DNN) for advanced feature extraction, capturing complex nonlinear relationships. The extracted features are fed into a Transformer model, which utilizes its self...

... detection and labeling. By understanding the physics behind tag plug and through plug events and leveraging advanced detection methods, coiled tubing operations can achieve enhanced efficiency and improved decision-making. deep learning neural network coiled tubing operations hydraulic...

... rates in single-phase wells. However, their inability to account for varying operational scenarios and field uncertainties presents significant limitations. This paper presents the application of a machine learning-based framework, specifically combination of Neural Network models, to predict the future...

... numerical simulations and data-based models. The available data at a point in the Santos Basin, Brazil, was: 5 deterministic forecast models and 80 members from probabilistic forecast simulations and in-situ wave buoy measurement. Ensemble means and neural networks forecasts were calculated and compared...

... practical solutions for improving overall operational efficiency of the process. geology geologist fracturing fluid neural network production logging data mining drilling operation machine learning completion installation and operations fracturing materials concentration hydraulic...

... conditions in ultra-deepwater environments pose significant challenges to the design of SCRs. To achieve efficient and reliable operation of SCRs under these extreme conditions, this study proposes an intelligent optimization design method based on enhanced Graph Neural Networks. Initially, considering...

... measurements like Rate of Penetration (ROP), gamma ray, formation cuttings, and mud logging. However, these measurements come with limitations such as high costs, manpower requirements, and time or depth lags. This study introduces an innovative alternative using Artificial Neural Networks (ANNs...

... and computer vision, deep learning (DL), particularly convolutional neural network (CNN) as well as its derivatives, has been widely adopted into automating the process of subsurface mapping for hydrocarbon exploration and production (E&P) and improving its quality (e.g., Wang et al., 2018 ; Abubakar et...

.... geologist sedimentology structural geology sedimentary geology reservoir simulation artificial intelligence depositional environment stratigraphy reservoir characterization neural network prediction interpretation cpt prediction offshore technology conference kriging machine learning...

... makes it possible to conduct more detailed and accurate forecasts, which can help to improve the safety and efficiency of CO 2 storage projects. air emission deep learning subsurface storage fluid dynamics reservoir simulation reservoir surveillance geologist neural network petroleum play...

... analysis. It iteratively refines the classification by strategically grouping data points based on their proximity in the dual-parameter domain. Then, a Convolutional Neural Networks (CNN) architecture is designed to synthesize the outcomes of the clustering analysis with the extensive dataset obtained...

... Networks A deep neural network (DNN) is composed of neurons grouped into an input layer, an output layer, and one or more hidden layers. Figure 2 shows the structure of a DNN. The input layer is fully connected to the first hidden layer, each hidden layer is fully connected to the next...

... artificial intelligence in general and particularly generative AI could transform the human-machine interaction in the oil and gas industry, covering specific examples in the fields of artificial lift and power generation. Statement of Theory and Definitions neural network artificial lift system...