... value. inflow performance well intervention fluid dynamics completion installation and operations artificial intelligence flow in porous media production monitoring well performance production control production enhancement downhole intervention formation damage machine learning...

... design layouts. Machine learning models are used to predict condensate RVP value and visualization tool is furnished using PI system. Subsequently, in 2022, Nopparat et al. have shared success in part of company's digital transformation initiatives which leverages Multivariable Predictive Control...

... type climate change sedimentary rock geology artificial intelligence air emission mineral co2 storage katterbauer lower wilcox reservoir aquifer storage complex co2 storage kemper county characteristic driven carbon storage well connectivity coal seam machine learning similarity...

... comprehend the CO2 trapping processes in these formations, reliable methods must be established to evaluate the efficiency of CO2 trapping. In this project, 4 machine learning techniques (Decision Tree, Random Forest, Gradient Boosting and Stacked Generalization) were applied to develop a robust model...

... and tackle this downhole accident, while the early detection and proactive management are critical for effectively preventing this issue at its early stage. This paper presents the development and implementation of a novel digital Twin system, which amalgamates physics-based modeling and machine learning...

.... The implementation of this approach includes the development of a web-based predictive analytic algorithm and a corresponding dashboard. These tools are designed to predict the formation and behaviors of freespan along the pipeline for specific years by combining machine learning capabilities and historical data...

... of rupture pressure is the most obvious among the six important geomechanical factors affecting the occurrence of well leakage. geologist artificial intelligence optimization problem geology reservoir geomechanics algorithm machine learning leakage prediction accuracy particle swarm algorithm...

... environmental impact. detection accuracy neural network emission deep learning machine learning footage deep learning image processing model estimation architecture algorithm video footage deployment methane emission artificial intelligence leak leak detection tdlp-ng Introduction...

... artificial intelligence geological subdiscipline bacteria sustainability storage social responsibility journal machine learning hydrogen microorganism geologist deep learning subsurface storage sustainable development climate change katterbauer mckee formation cavern investigation sequence...

... operations. This paper presents a comprehensive description of the ECC correction algorithm's design, implementation, and evaluation, and demonstrates its effectiveness in improving bond index accuracy. The integration of machine learning tools to provide reliable real-time cement bond logging without...

..., making it more efficient by utilizing continuously measured parameters data and employing machine learning techniques. This paper provides a useful reference for future studies to forecast Remaining Useful Life (RUL) of choke valve. The method presented in this study has the potential for expansion...

.... The downhole losses, partial or total, in many cases associated with karst, fractures, caverns or large voids, which cannot be identified at the well design stage with standard practices. The manuscript provides the detailed approach how the machine learning has been used to predict the loss circulation events...

... physics-based models with machine learning models. First, with the historical temperature and pressure data collected from the wellheads and the PDGs, a physics-based model was built on the basis of the wellbore multi-phase flow theory to depict the gas-liquid pipe flow behavior. Afterward, machine...

... step for decision-making, particularly beneficial for operators without a background in machine learning. The transformation of existing oil and gas wells into geologic carbon storage sites, as outlined in this paper, not only aligns with environmental sustainability goals but also presents...

.... geology neural network machine learning cable artificial intelligence umbilical optimization ultra-deepwater umbilical cable curvature controls and umbilicals south china sea platform geologist subsea system umbilical cable bayesian optimization algorithm iteration configuration...

... matrix Score Accuracy 89% Specificity 77% Precision 82% F1-score 90% MCC 79% The results shows that among nine machine learning algorithms, Bayesian optimization identified the Ensemble as the optimal model. Based on Table 1 , the Ensemble model shows an accuracy of 89...

... From the above testing use cases results, we can conclude that this digital machine learning pore pressure prediction model is proved accurate and reliable as compared to actual downhole pressure test results and engineer’ manual interpretation results. One more significant advantage...

... in this paper. geologist well logging learning module reservoir characterization geology log analysis prediction petronas carigali sdn bhd artificial intelligence world coreai dataset pioneering machine learning platform algorithm artificial intelligence machine learning ermai...

.... With the rapid development of artificial intelligence (AI) technology, using machine learning algorithms to optimize well locations may be a fast and reliable solution. The research started with data processing. Firstly, data related to well location optimization parameters are collected and pre-processed...

... storage artificial intelligence geoscience australia social responsibility sustainable development workflow climate change geologist subsurface storage geological storage saline aquifer rcsi machine learning offshore technology conference prediction driven approach estimate potential co2...