... models for crude oil production forecasting. Artificial intelligence (AI) and machine learning (ML) techniques have gained attention in this field, as they can handle large amounts of data and provide reliable predictions. ML algorithms such as Random Forest (RF), Artificial Neural Network (ANN), Long...

... series ML-based Recurrent Neural Network's Long Short-Term Memory (LSTM) model. The study results showed that the LSTM model outperformed the traditional exponential model, with an RMSE of 80.12 compared to 107.41 for reservoir K3, 30.24 to 141.52 for reservoir VII, and 80.56 to 169.81 for reservoir K5...

... and acceptable accuracy. equation completion installation and operations drillstem testing neural network reservoir surveillance production control drawdown production monitoring dataset prediction drillstem/well testing artificial intelligence machine learning application society university...

... entails creating a model with the remaining characteristics ( Vidhya, 2023 ). Recurrent Neural Networks Recurrent Neural Networks (RNNs) are a class of machine learning algorithms well-suited for time series prediction. They possess internal memory mechanisms that allow them to capture...

... the Random Forest Regressor and XGB Regressor, showcased robust performance in predicting ECD. The feature importance analysis underscored key variables influencing ECD calculation, providing reliable insights to enhance drilling techniques and safety measures. geologist geology neural network...

... Mutual Information (MI) scores. Various algorithms, including Convolutional Neural Networks (CNN), Feed-Forward Neural Network (FNN), Linear Regression (LR), XGBOOST, and Long Short-Term Memory (LSTM) Networks, were tested for training and evaluating the predictive performance of the model. The LSTM...

... suitable for adequate training using the maximum-minimum normalization approach. Three multiple-input multiple-output (MIMO) machine learning methods, namely artificial neural network (ANN), decision tree (DT) and random forest (RF), were used to train the datasets. Five performance metrics, coefficient...

...: X n = normalized value X min = minimum of original values, X max = maximum of original values X = original value This made sure that duplicates and empty rows were removed throughout the data cleaning process, facilitating the data training process for the artificial neural network...

...), and stability index (SI)) and an artificial neural network (ANN) were coupled. The collected data was preprocessed and subsequently explored for its statistical features. The data was split in a 70:30 ratio for model training and testing. The model performance was optimized via hyperparameter tuning...

... neural network dataset africa government machine learning nigeria government artificial intelligence simulation mmscf condensate reservoir ga condensate available hydrocarbon journal upstream oil & gas reservoir natural gas condensate production condensate artificial neural...

... leakages across the pipeline. Machine learning algorithms which include Recurrent Neural Networks, and K-nearest neighbourhood are built and trained with operational data to derive the optimal learning model. Also, each model's performance metrics were evaluated to measure the model's accuracy...

...Objectives Develop a neural network model between gas flare volume, composition and temperature and gaseous pollutants. Use the model to predict gas flare volume, temperature and composition for an independent flowstation. Scope The paper focuses on the Niger delta regions...

... thermal conductivity of rocks which is cost effective, and practically achievable. Model development During the model development stage of this study, 3 machine learning algorithms alongside Artificial Neural Networks were employed in the prediction of thermal conductivity of geothermal rocks...

... regression (MNLR), neural network (NN), support vector machine (SVM), and the group method of data handling (GMDH) techniques were used to develop several correlations. Results show that the GMDH method yielded the best correlation while the MNLR is the least accurate. The root means square error (RMSE...

... for the purpose of accurately predicting monthly natural gas spot prices. Henry Hub natural gas spot price data from January 2001 to November 2021 were utilized alongside four machine learning algorithms namely; Artificial Neural Networks (ANN), Support Vector Regression (SVR), Random Forest Regressor...

... ties to mathematical optimization, which provides the field with methods, theory, and application domains. algorithm artificial neural network deep learning machine learning architecture upstream oil & gas artificial intelligence learning algorithm dataset reservoir neural network...

... machine learning algorithm neural network algorithm classifier clustering support vector machine voting classifier Introduction In the past few years’ lithology classification has been speculative to the industry, and accurate prediction of formation tops with lithology identification is now...

... dataset hydrate risk management cross-correlation plot neural network machine learning plot regression pressure ridge regression Machine Learning Modelling Machine learning model development is a data-driven approach for solving specific problems ranging from exposition to robust...

... of determination of 0.989% after GA optimization. artificial intelligence rop algorithm upstream oil & gas evolutionary algorithm dataset neural network machine learning prediction correlation driven model genetic algorithm drilling artificial neural network application accuracy...

.... As a tilt from the conventional methodology of forecasting involving use of curve fitting techniques, and multi-level computational analysis, data driven approaches can be employed. This study presents the applications of data driven approaches to forecast production. Deep learning neural network algorithm...