... parameters are generated. The correlation heat maps are populated with Pearson's correlation coefficient for density and IFT prediction. The relative importance of input parameters with IFT data of H 2 -brine systems is determined by Pearson, Spearman, and Kendall models. Evaluation of the machine learning...

.... production logging drilling operation reservoir surveillance directional drilling production control flow in porous media multistage fracturing production monitoring hydraulic fracturing upstream oil & gas input parameter temperature modelling fiziko-matematicheskoi literatury inflow atm...

... petroleum exhibition & conference pef reservoir characterization neural network correlation application petroleum engineer prediction khan dataset synthetic photoelectric log log analysis interpretation deep learning input parameter artificial neural network mineralogical content model...

... part of data integration is to find the most relevant input parameters, which can be used by the optimization system, and provide recommendations to the end user. A hierarchical control system is added in parallel to data import, providing a long-term and easily sustainable data set for evaluation...

... such as oceanic currents and wind patterns. The data derived from these models can be fed as input parameters into a stratigraphic forward model. The geological process incorporated in the SFM can be tailored to fit the study's objective, regional framework and geological units of interest. The resulting models...

... associated with its occurrence will be reduced. transfer function Artificial Intelligence non-pipe sticking Upstream Oil & Gas gaussian kernel function input parameter artificial neural network different structure neural network pipe Drilling machine learning accuracy kernel function...

... is required where it is implemented for ROP prediction. ANNs appear to be more conservative in predicting ROP than MRA as indicated by a higher value R 2 (0.96) and lower value MSE (1.89) of the ANN model. Considering the input parameters, the obtained results showed that TVD, WOB, RPM, SPP, and ECD had...

...-darcy effect input parameter diffusion non-darcy flow constant matrix fracture fracture permeability Upstream Oil & Gas recovery flow regime gas recovery equation permeability hydraulic fracturing gas production berawala transition non-darcy flow apparent permeability...

... on thousands of real time data points recorded from selected wells in a specific depth interval. Typical drilling parameters such as weight on bit, rotary speed, bit hydraulics, lithological properties, and dogleg severity were the input parameters chosen in the model to generate ROP. Once the model...

... machine learning objective function input parameter software configuration algorithm uncertain parameter production data optimization algorithm proxy model Introduction In the next twenty years the global energy demand is expected to grow by 30% and in the same period, the oil market...

... prediction method became rather important to safeguard the drilling of subsequent wells. BSP geomechanics team developed an geomechanical approach to tackle the FG predictions. The approach integrates the classic Kirsch equations with the improved procedures to derive the required input parameters...

... assembly (BHA). The machine learning techniques depend only on the selection of input parameters. Machine learning methods such as the artificial neural network (ANN), support vector machines (SVM) genetic algorithms, and fuzzy logic can learn the trend or behavior of experimental or real data and thus...

... input parameter Saudi Arabia Upstream Oil & Gas packstone facies stratigraphic modeling deposit geologic modeling Artificial Intelligence mudstone organic matter sensitivity analysis wave energy sediment proportion lithology Introduction Forward stratigraphic modeling...

... the input parameters and the target parameter. The objective of this study is to develop a robust and an accurate model for static Young's modulus based on the wireline logs as an input using three artificial intelligence tools (artificial neural network, adaptive neuro fuzzy inference system and support...

... diffusion model TSNL production monitoring long string input parameter radial flow equation drillstem testing polygon software boundary model drillstem/well testing Spectral Noise society of petroleum engineers Reservoir Surveillance flow unit Formation Pressure Evaluation noise noise power...

... rate drilling operation input parameter diameter Directional Drilling productivity index wellbore diameter well length frictional pressure loss reservoir horizontal well optimal well length Upstream Oil & Gas viscosity productivity perforation drawdown sensitivity analysis...

... intervention production enhancement Well Intervention slot recovery Asset Life Extension operation milling head submerged globular particle size distribution milling milling efficiency brine environment input parameter milling head recovery non-contact approach Efficiency particle milling...

... add-in, this scheduling tool enables the timely use of probabilistic Monte Carlo simulation for estimating the impact of uncertain input parameters on CO 2 flood performance from multiple reservoirs. A numerical optimization algorithm searches for the best development schedule by optimizing the start...

... recovery input parameter uncertainty analysis uncertainty analysis reduction of residual oil saturation carbonate reservoir log analysis equation sorw porosity sor iteration upstream oil & gas simulation result well logging uncertainty analysis sig12 20 petroleum engineer sigl1 sigw2 6...