... in a similar fashion as a multiphase subsea booster, to pump the homogeneous dense mixture. The main objectives of the test campaign were to validate the hydraulic performance prediction and assess the rotordynamic behavior of the pump when operated with a dense fluid. Additionally, a series of slug tests...

... lags. This study introduces an innovative alternative using Artificial Neural Networks (ANNs) to accurately predict formation tops. The ANNs model incorporates drilling mechanical parameters and ROP to address limitations in existing techniques, aiming to predict genuine ROP changes irrespective...

... control. Common design models can overpredict the JT cooling value. This leads to extra CAPEX and OPEX costs. This paper will use data from nine fields to demonstrate the accuracy of cubic EOS's ability to predict JT cooling out of the box and with tuning. The cubic EOS was modified to shift the enthalpy...

... of the extended coverage (both temporally and spatially) of numerical modelling and satellite remote sensing data. Here, we adapt and apply a promising framework – originally proposed by the present authors for the prediction of wave conditions on the European North West Shelf – to the waters of the Gulf...

... 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...

... The main objectives of this work are the following: Modelling the flow loop test based on various friction reducers and concentrations, and flow rate to provide real-time modelling of pipe pressure loss in the field. Prediction of the total friction loss based on integrated lab and field...

... these data, the ACM is able to deliver accurate feedback on the operation which may be compared to previous operations and used to check and/or predict wear and potential failures. Valve status is constantly verified by monitoring the resolver sensors in the motor assembly, which are constantly verified...

... to return an assessment. Despite this, there has been limited industry use due to concerns about the statistical nature of predictions and the sparsity of available training data. Deep neural networks (DNNs) are a purely data-driven machine learning model that require large quantities of labeled data...

... 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...

... chain is important for operators in the offshore oil and gas industry. While this topic has been a subject of several studies, actual fatigue consumption of a mooring chain due to corrosion, in-service loads and predicting the remaining fatigue life of the same chain in permanent floating structures...

... Addressing the complexities of VIV requires a more sophisticated approach, and in recent times artificial intelligence (AI) has emerged as a transformative tool in tackling this intricate problem. By harnessing the power of AI, researchers and engineers can achieve more accurate predictions...

... offshore stakeholders to plan and assess upcoming activities based on accurate prediction of vessel response with the objective of reduced waiting on weather without compromising safety. This paper introduces an enriched understanding of how the VMBC method is used in real life operations. A methodology...

... Abstract Predicting and optimizing the rate of penetration (ROP) is a crucial part of drilling optimization. While numerous machine learningbased ROP prediction methods exist, their practical utilization remains underexplored. This paper introduces an innovative approach, employing data...

... The significance of this study lies in its potential to streamline choke valve management particularly in the wells with choke valve erosion issues. The developed tool can be used by the production and operations teams accurately predicting the onset of erosion. This should help in minimizing...

... damage using spectral analysis methods.  Critical Examination of Fatigue Analysis Methods Uthamanthil and Drake (2021) critically examined the limitations inherent in spectral and stochastic approaches. They advocated for expanded research, particularly in comparing fatigue damage predictions...

... to achieve operationally) can result in nonproductive time with costs that exceed USD 100,000 or even USD 1,000,000. Precise shear values depend on the physical design, geometric tolerances, variation in material properties, and the operational environment. Predicting and achieving reliable fracture of shear...

... sets were used for this study. Every data set contained reservoir temperature (T R ), oil gravity (API), specific gas gravity (γ g ), and solution gas-oil ratio (R s ) as input parameters for this analysis to predict the bubble-point pressure (P b ) which served as the output parameter. Data...

... predominantly on Mercia Mudstone, this research introduces a novel approach in assessing pile drivability through rock formations. The SRD model, an advancement of the Stevens et al. (1982) method, incorporates modifications to better predict unit shaft and tip resistance in rock formations. The model's...

... injection datasets. The efficacy of the WNO model was evaluated through CO 2 forecasts on the SACROC dataset, while the trained FNO model on the SACROC dataset was "transferred" to make predictions on the Sleipner dataset. The WNO-based ML method was accurate and efficient, such that the overall mean...

.... The system has two main functions: mechanical behavior prediction and mooring strategy adjustment. Combined with the monitoring data of environmental elements in the current operation area, the system can realize real-time prediction and calculation of platform movement, mooring lines tension and riser...