... AI algorithms such as Logistic Regression, Artificial Neural Networks and Decision Tree were analyzed for their contributions to geophysical and geochemical data analysis in geothermal exploration. The review focused on the identification of hidden patterns in large datasets using AI algorithms...

...   ×   P u p +   w 1 i , 4   ×   P d +   w 1 i , 5   ×   γ +   b 1 i ) ) − 1 ) Artificial Neural Networks are computational models inspired by the structure and function of the human brain. They consist of interconnected layers...

... into the prediction algorithm. By leveraging multivariate regression, lumped reaction mechanisms, and artificial neural networks, the model utilizes feedstock properties such as con-carbon, density, distillation, sulfur/nitrogen content, and catalyst properties like surface area, unit cell size, and acidity...

.... This paper presents a novel approach using artificial neural networks (ANNs) to predict the discharge pressure of electrical submersible pumps. The proposed model will enable early detection of possible failures and reduce downtime. Also, the effectiveness of the ANN model will be compared against...

... of MLAS for predicting and mitigating downhole vibrations, highlighting its potential benefits and key components. A Machine Learning (ML) approach, specifically an Artificial Neural Network (ANN), was employed to predict downhole vibrations through drilling horizontal sections. Artificial Neural Network...

... drillstem/well testing saudi arabia government drillstem testing machine learning production control hole pressure algorithm forecasting static bottom hole pressure efficiency artificial neural network production monitoring pressure prediction accuracy upstream oil & gas artificial...

... as the wireline logging tools). In addition to the T 2 relaxation time measurements, gas porosity and permeability experiments were performed in all samples as conventional methods to validate the outputs. ML techniques include five different types of Artificial Neural Networks (ANN) such as feed-forward...

... using artificial neural network. Significance of applied technology is well-proved on field development. Using reservoir-related technology (e.g. 4D seismic, Enhanced Oil Recovery (EOR), Conformance techniques… etc.) or well-related technology (e.g. smart wells, stimulations, artificial lifting… etc...

... the density and sonic logs. Moreover, rock mineralogical content (carbonate, quartz, clay) has been incorporated for model development which is strongly correlated to the PEF. At the next stage of this study, architecture of artificial neural networks (ANN) was developed and optimized to predict the PEF from...

... an artificial neural network (ANN) technique. In this study, the improvement in hydrocarbon productivity due to applying acid fracturing treatment is estimated, and the interactions between the natural fractures and the induced ones are considered. More than 3000 scenarios of reservoir properties and treatment...

... of various dual-permeability systems were used to generate the dataset for the machine learning model, an artificial neural network. A second-order reaction scheme for gel formulation was used to describe the chemical reaction between the polymer, polyacrylamide, and the cross-linker component, chromium...

... ( Gooneratne et al. 2020 ). Although many studies have suggested the use of ML models (i.e., artificial neural networks, logistic regression, among others) to imputing logging data, the inputs of these models are the data from other log types. Therefore, the existing methods still require running logs...

... using real-time drilling sensors. The two methods of AI are functional networks (FN) and artificial neural networks (ANN). Well (A) was utilized to build the two AI models by dividing their data into training and testing. Then, well (B) was utilized to validate the developed AI models. A high accuracy...

... predictive and optimization model for ROP that utilizes a hybrid artificial intelligence model based on an improved genetic algorithm (IGA) and artificial neural network (ANN) for further optimization of drilling processes. Real field drilling datasets such as the bit type, bit drilling time, rotation per...

... Abstract The fourth industrial revolution and its vision for developing and governing the technologies supported artificial intelligence (AI) applications in the different petroleum industry disciplines. Therefore, the objective of this paper is to use the artificial neural network (ANN...

..., which are relatively expensive. Therefore, AI can play an important role in developing models to predict relative permeability accurately without extensive lab procedures. Accordingly, this work presents application of two AI algorithms namely, Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy...

... with most published studies is that there is no simple model currently available to guarantee the ROP prediction. The main objective of this study is to further improve ROP prediction using two predictive methods, multiple regression analysis (MRA) and artificial neural networks (ANNs). A field case in SE...

... of data being utilize in the specific field only. machine learning log analysis neural network clonne model Artificial Intelligence optimization Upstream Oil & Gas Exhibition Pilot Test operation artificial neural network well logging calibration prediction application Baram Delta...

... using non-linear diffusion filters for capturing attenuation and time lag, and constituted the data set for machine learning. An artificial neural network (ANN) is then generated and trained to simulate connect relations between producers and its surrounding injectors. Genetic algorithm (GA) is also...

... and deviated wells using artificial neural networks (ANNs) and a support vector machine (SVM). The proposed models were examined using a few examples of real stick pipe cases from the field. The results of the analysis have revealed that both ANNs and SVM approaches can be of great use, with the SVM results...