... Abstract In this paper, we present an automated data-driven workflow using Machine Learning (ML) for gas lift optimization in unconventional fields. This workflow integrates a ML model that accurately forecasts the Gas Lift Performance Curve, and a Bayesian Optimization Framework to solve...

... 2 in brine formation, the mean squared error is 5 × 10 −5 and the R-squared is 0.96. in general, the Gradient Boosting Model ranks as the most accurate machine learning algorithm among the algorithms studied in this study. Introduction Reactive gases, like ozone and nitrous substances...

... recovery multiphase flow sos log analysis complex reservoir flow in porous media reservoir surveillance production monitoring pvt measurement equation of state production logging well logging production control machine learning fraction frequency accuracy resolution wavenumber...

... testing modeling & simulation drillstem/well testing geology rock type reservoir geomechanics mudstone scenario configuration urtec unconventional resource technology conference application inter-well interference unconventional reservoir geometry machine learning dtof seg...

... 1. Discovery: Review current control and surveillance technologies to identify opportunities for AI-based enhancement. 2. Assessment: Gather key data for model training and evaluate integration compatibility with existing systems. 3. Development: Develop and calibrate AI / machine learning (ML...

... testing, were collected from these tests to generate time-series plots or analytics that can inform operators of downhole conditions. Predictive modeling based on reservoir simulation and machine learning was then conducted to rapidly forecast future performance for operators to compare against observed...

... Abstract A prediction method for the EUR (Estimated Ultimate Recovery) of tight sandstone gas reservoirs based on a composite machine learning approach was proposed and applied in the M tight sandstone reservoir in Canada. This method extrapolated geological parameters such as porosity...

... geology complex reservoir reservoir surveillance forecasting urtec unconventional resource technology conference adversarial transfer learning deep learning production forecasting production monitoring unconventional reservoir technology conference discrepancy machine learning conference...

... drilling fluid formulation drilling fluid property geochemistry geological subdiscipline artificial intelligence well control geochemical characterization drilling fluids and materials drilling fluid selection and formulation machine learning geologist complex reservoir drilling fluid...

... gas oil shale rock type complex reservoir shale oil geology mudrock log analysis water-filled porosity conductivity physics-informed statistical modeling mudstone saturation comprehensive approach accuracy well logging artificial intelligence machine learning urtec unconventional...

... in the vertical wells and its implications for future use in deciding frac stage placement. geologist mudrock sedimentary rock complex reservoir artificial intelligence machine learning urtec field development structural geology petroleum geology clastic rock reservoir characterization rock...

... to uncertainties when utilizing seismic and adjacent well data to anticipate drilling risks and optimize well trajectories. This study aims to mitigate these uncertainties by calibrating drilling risks using borehole-side seismic data. Additionally, a machine learning model is used to predict the 3D spatial...

... for multiphase flow—Hybrid Sparse Nonlinear Regression (Hybrid SNR). This innovative model integrates recent advancements in machine learning and sparsity techniques. Unlike conventional methods, Hybrid SNR is suited to discern the intricate governing equations that dictate flow of phases in unconventional...

... the scalability and reliability for shale and tight reservoir forecasting. We construct generic reservoir models that can capture key first principles of unconventional well production mechanisms. PVT and pressure machine learning models are developed and incorporated into the reservoir models so...

... Abstract While it is generally understood that existing well depletion affects future well developments negatively, estimating the production impacts remains a challenge, particularly when newer developments occur in a different formation. This study seeks to use machine learning (ML) models...

... of microseismic data, there are multiple solutions for the inversion of fracture network, making it difficult to obtain the accurate distribution of hydraulic fractures. With the rapid advancement of artificial intelligence, machine learning methods have been applied in the oil and gas development, including...

... caney shale correlation complex reservoir shale gas log analysis machine learning static young oklahoma structural geology mudrock well logging rhob ductile phin dyn av mudstone young poisson xgboost caney shale static mechanical property random forest URTeC: 4038060 Integrating...

... reservoir geomechanics thermal method hysteresis proxy complex reservoir flow in porous media machine learning co 2 constraint geologist sagd enhanced recovery economic geology injection conference simulation coefficient optimization problem artificial intelligence geological...

... sedimentary rock geology natural language generative ai history gas production forecasting modeling & simulation energy economics time sery transformer application houston production forecasting machine learning urtec unconventional resource technology conference permian basin artificial...

... connectivity analysis, resource volume estimation, forecasting, and flood optimization. However, the model's accuracy and predictability can be further enhanced with data-driven approaches. Here, our aim is to improve the quality of the RGNet model using machine learning (ML). We propose to use an ML-enhanced...