... standards so that they meet both the requirements of the AI training system. Abyss has a mature AI training suite which utilizes supervised deep learning models. We may use conventional machine vision techniques as preprocessing steps in training as needed. As we train the first machine learning models, we...

... shift from mere technology deployment to strategic adaptation that delivers measurable financial benefits. drilling operation artificial intelligence operation trajectory machine learning engine workflow offshore operation optimization problem drilling automation offshore offshore...

... Abstract This study aims to present a novel approach for estimating relative permeability curves using Machine Learning (ML) and Deep Learning (DL) techniques based on production data. This method aims to overcome the shortage in core data availability, which is much needed for reservoir...

... with the availability of proven Artificial Intelligence (AI) and Machine Learning (ML) tools. Subsea tieback operational efficiency improvements are bottlenecked by human operator ability to process and respond in a timely manner to the overwhelming quantity of data collected by modern subsea monitoring and sensor...

... drilling, ensuring better decision-making in casing programs and drilling fluid design, ultimately contributing to more efficient and cost-effective well operations. geologist clastic rock neural network geology artificial intelligence machine learning reservoir geomechanics rock type...

... machine learning models devoted to sparse data, namely Geotechnical lasso (Glasso) and Gaussian Process Regression (GPR) from a Bayesian perspective considering prior knowledge of site information. This approach provides statistical information on soil parameters like undrained shear strength, supporting...

... Abstract Machine learning techniques offer the potential to revolutionize the provision of metocean forecasts critical to the safe and successful operation of offshore infrastructure, leveraging the asset-level accuracy of point-based observations in conjunction with the benefits...

... than traditional mooring monitoring techniques. mooring system mooring line 4 spar artificial intelligence algorithm alert hurricane mechanical engineer digital collection subsea system hull offshore technology conference gps position monitoring data integrity doi machine learning...

... a computational solution to help specialists interpret cement integrity logging data. Simultaneously, the developed tool aims to assist operators in optimizing the planning and management of decommissioning campaigns. The innovative software employs machine learning techniques that have exhibited significant...

... performance. As the industry continues to evolve, the integration of advanced AI technologies stands out as a pivotal strategy to ensure a safer and more sustainable future for drill floor operations. artificial intelligence implementation safety enhancement ai tool machine learning algorithm...

... problem subsea production equipment risk and uncertainty assessment manifold modeling & simulation subsea system risk management evolutionary algorithm flowrate layout conference moored fpso artificial intelligence machine learning variation positioning fpso diameter flowline...

... intelligence machine learning implementation operation module project management strategic planning and management platform operator maintenance risk management floating production systems representation application dynamic simulation dimension architecture scenario In either case, using...

... unit weight and undrained shear strength, recognized as primary indicators, are meticulously examined in the designated coring sites. The K-means algorithm, a unsupervised machine learning technique, is employed in this clustering analysis. It iteratively refines the classification by strategically...

... determined both offshore in a field laboratory and onshore in a geotechnical laboratory. Recently, artificial intelligence (AI), machine learning, and deep learning have been the subject of intense media hype and therefore it was deemed appropriate to develop a deep-learning algorithm (DLA) to predict V S...

..., or initial and boundary conditions. Recently, the combination of unprecedented large data volumes, computational power, and advances in machine learning algorithms offers opportunities to expand our knowledge of the oceanic system. In this context, the main objective of this study is to accurately predict...

... augmented predictive model efficiency friction reducer friction pressure loss machine learning technology conference prediction friction reducer concentration figure tortuosity Introduction Hydraulic fracturing, also known as fracking, has become one of the most critical aspects of well...

... accelerating offshore windfarm site characterization geophysics social responsibility machine learning abubakar impedance inversion information Introduction The target of net-zero carbon emissions by 2050 has significantly increased the demand for renewable energy, including solar and wind energy...

... multiphase flow simulations to characterize hydrate formation at desired conditions, however, there is no numerical method to assess the risk of a plug occurring from these results. Traditional machine learning models have shown reasonably accurate plugging risk classification and require just milliseconds...

... Abstract This paper delves into the framework of a new vessel motion prediction application that leverages advanced machine learning (ML) techniques in conjunction with metocean forecasts to predict vessel motions as well as thruster loads. The paper illustrates the validation process...

... to the proposed hydrogen production system. The energy balance between hydrogen production requirements and diesel efficiency benefits was estimated and evaluated. The study also employs thermoeconomic analysis using the SPECO methodology and utilizes machine learning models for predictive analysis and to develop...