... drilling fluid advisory system artificial intelligence hyperparameter petroleum engineer model training data mining advisory system ml pipeline dataset experiment machine learning algorithm accuracy composition configuration drilling fluid management & disposal pipeline optimization...

... spending. An Artificial Intelligent (AI) with the integrated reservoir characterization, new high seismic data and model are used for AI reservoir model in mature and complex reservoirs. The suitable AI algorithm has been evaluated for reliable sweet spot reservoir characterization in the effective time...

... in the dataset were measured at 150 °F with a rotational viscometer. The dataset's quality and reliability are enhanced by eliminating irrelevant, noisy, or inconsistent information. The outlier detection was performed using the Quartiles method with a threshold factor 1.5. Three feature selection algorithms, F...

... in cybersecurity, especially within the complex and critical infrastructure of the oil and gas industry. AI algorithms are designed to monitor network traffic and system behaviors in real-time, establishing a baseline of normal activity. This baseline is continuously refined through machine learning, which enables...

... this challenge, this study proposes a method for predicting the recovery factor of complex fault-block reservoirs based on machine learning algorithms, using the ZY oilfield in China as an example. This study first selects various factors affecting the recovery ratio of complex fault-block reservoirs from both...

... discrepancies in the literature and shedding light on the complex interactions among CO 2 , water, and coal. To overcome the limitations of conventional experimental methods, this study employed advanced machine learning and deep learning techniques—including tree-based algorithms and neural networks...

... strategies based on an improved genetic algorithm. Building upon the traditional genetic algorithm, adaptive mutation strategies and elite preservation mechanisms are integrated to significantly enhance the algorithm's global search capability and convergence speed. The adaptive mutation strategy dynamically...

... in geological formations. To address the aforementioned issues, this study focuses on the Penglai gas area in the Sichuan Basin. By employing the XGBoost algorithm, three well logging parameters, namely acoustic time difference, compensating density, and natural gamma, are selected to classify the strata...

... structural geology seismic model machine learning node inversion algorithm volcano caspian sea seabed velocity modeling technique turkmenistan picking tomography inversion cheleken psa area deep learning reservoir characterization imaging first-break picking figure 4 Introduction...

... downhole properties which can then be correlated to the behavior of cements and the change in their mechanical properties over time using machine learning algorithms. Laboratory evaluations showed varying mechanical properties for oilwell cement at different temperatures and degradation over time. Overall...

... of the reservoir-wellbore system. In this study, efficient algorithms are proposed to automatically optimize the parameters in real-time to remove the liquid efficiently and maintain gas production, through which the recovery factor can be extended. Firstly, we build a comprehensive model that couples...

..., biostratigraphic zone and saturations were extracted and integrated with the UBCTD legacy dataset, including measured gas rates, productivity index and well trajectories. A machine learning (ML) algorithm was trained on this integrated dataset to predict the measured productivity gained per foot from the G&G...

...-learning (ML) analysis techniques such as partial least squares (PLS) and advanced modern PLS methods were applied to XRF and FTIR combined data to analyse these complex multicomponent samples. In this paper, a detailed discussion of the data workflows will cover the spectra preprocessing, ML algorithm...

... Strength (UCS) within the technical limit of operational performance across the different Lithologies. Supervised learning algorithm with 70:30 data splits using four ML algorithms, including Random Forest, support vector regression, artificial neural network (ANN), and Categories boost, was utilized...

... and holdout sets with a fivefold cross validation and considering feature importance, utilizing seven regression algorithms and a neural net. Exploratory data analysis led to some data cleaning based on missing values and data QC, leaving data from 448 wells. This final dataset was then subjected to cross...

... (30-90%), and surfactant concentrations (0.1-0.5 wt.%) at shear rates between 100-1450 s −1 . A total of 5,552 data points were used as primary data for developing supervised ML regression models. Machine learning algorithms from the Scikit-learn library, such as K-Nearest Neighbors (KNN), Random...

... penetration of net pay thickness as inputs. This model is trained utilizing total skin factor acquired from conventional well test analysis, serving as the model's outputs. Nine distinct machine learning algorithms (Gradient Boosting, AdaBoost, Random Forest, Support Vector Machines (SVMs), Decision Trees, K...

.... geologist geology carbonate rock complex reservoir carbonate reservoir decision tree learning sedimentary rock reservoir well logging reservoir characterization node porosity tight carbonate reservoir significant discovery reservoir surveillance production control evaluation algorithm...

... mudstone accuracy dataset real-time identification sedimentary rock deep learning artificial intelligence machine learning precision application reliability geologist neural network prediction geological subdiscipline algorithm computer vision bedding interpretation formation micro...

.... In this work, we applied various AI methods (three black box algorithms and two White-box algorithms) to train a model using a multi-dimensional dataset with over 700 rows of data. Moreover, two robust correlations will be developed that can be used for a wide range of parameters. The dataset has 20 variables...