... drawbacks of these methods are the data and samples availability, high costs and time This paper presents an alternative technique of utilizing real-time drilling parameters and machine learning (ML) algorithm in the prediction of UCS thereby enabling timely drilling decisions. ML algorithm enables a system...

... model revalidation and relevance to pore pressure prediction in the Coastal Swamp , Niger Delta . Specialist Geosciences Team, Shell Petroleum Development Company of Nigeria Ltd . (Unpublished) . Gradstein , F.M , Ogg , J.G. , Schmitz , M.B. and Ogg , G.M. (Eds.), 2020...

... Abstract The worst-case discharge during a blowout is a major concern for the oil and gas industry. Various two-phase flow patterns are established in the wellbore during a blowout incident. One of the challenges for field engineers is accurately predicting the flow pattern and, subsequently...

... of about 6kbwpd and cumulative of >2MMbbl disposed into the target reservoir. The initial reservoir souring prediction assessment carried out at the commissioning of the PWD facility showed a high potential of H 2 S proliferation in the reservoir, although with no chance of H 2 S release to surface...

...-driven approach based on machine learning (ML) algorithms to create high-performing models for predicting the dead oil and bubblepoint oil viscosity of Nigerian crudes. Random Forest (RF) and Gradient Boosting (GB) ensembles, as well as Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF...

... that can predict permeability based on well-log data. This study aims to determine the most effective machine learning models for predicting permeability based on available well-log data. The study covers a detailed explanation of the data-gathering and pre-processing techniques used. Four input features...

... level. Results from this approach can also be used in the absence of a reliable well test. nigeria government artificial intelligence africa government prediction formula upstream oil & gas algorithm engineering equation wellhead machine learning approach accuracy ml algorithm...

... learning algorithm (Levenberg-Marquardt) to train the network was adopted in model development. 75% (880 data points) of the data was set aside for the training of the model at its developmental stage, 10% for test data set and 15% for the validation data set. From the models’ prediction, it was observed...

... performance indicator africa government project management operation correlation machine learning accuracy well construction intervention time prediction benchmarking estimation nigeria government data quality outlier dependency spreadsheet correlation coefficient strategic planning...

... variance of 0.26, accuracy of 91.87%, and MAPE of 8.13%. The SVR model was more accurate in predicting energy consumption, as it showed better accuracy on the test set with lower MAPE and higher R-squared value. Both models outperformed a relatively complex and computationally expensive model in a previous...

... Networks were developed for the real-time prediction of thermal conductivity of rocks in geothermal wells. Data being obtained from Utah Forge field project included drilling data, thermal conductivity data and other necessary information from the field. With real-time sensor drilling data such as Rate...

... of oil production facilities require adequate prediction of the behavior of two-phase flow of hydrocarbon in pipes with different operating conditions. It would be desirable to apply a more unified model to predict the onset of liquid loading in gas wells. This is very important to operators because...

... to be guided by science and data rather than a trial-and-error approach of changing the operating conditions of the well to determine the corresponding production response. Well performance models are often used to predict well behavior at different operating conditions but one of the disadvantages...

... uncertainty in fluid contact and there was also inability to map intra-reservoir faults and shales around the proposed landing depth of the drain hole. Uncertainty in the size of the gas pocket also led to the inability to accurately predict the timing of the blowdown. Prudent reservoir management enabled...

... Abstract The ability to accurately predict natural gas prices asides being beneficial to stakeholders of the natural gas market also have positive economic impacts on energy management and environmental sustainability. This paper explores the application of machine learning algorithms...

... Reservoirs. However, a major setback in applying the decline curve is its inability to adapt predictions to different past operational scenarios and uncertainties. With the emergence of big data and increasing computational power, machine learning techniques are increasingly being used to solve problems like...

... in the chemical and petroleum industries. Many multiphase flow models apply a number of closure relationships; one of such is the drift velocity. Empirical correlations, with varying range of applicability and predictive capability, are typically relied upon by researchers to predict this parameter. This work...

... Abstract Accurate determination of drift velocity is required, as drift velocity is an important parameter in multiphase flow modelling. There are existing correlations for determining this parameter, but some of these models contain some degree of errors in their predictions. Recently, Machine...

...RESULTS Well model predictions indicates that wax appearance temperature (WAT) for MERO-006T was predicted to occur at 172.5°F. The wax appearance depth corresponding to this depth was estimated at 2529 ftah however considering expected late life production profile (increasing watercut...

... at each time step. Regression Algorithms were applied to develop a fit for a model to predict the hydrate risk level given thermodynamic conditions alongside the flow rate. The developed hydrate model was also applied to study the performance in flow operations. The ridge regression model showed the best...