... testing production monitoring stabilization coefficient enhance gas well testing exploring petroleum engineer society dataset deliverability upstream oil & gas artificial intelligence machine learning application regression metric Introduction The history of gas well...

... Regression as introduced by ( McCain, 1998 ) using alternating conditional expectations (ACE) for selecting multiple regression transformations 2), Genetic programing and heuristic modeling using Symbolic Regression (SR) and cross validation for model automatic tuning 3) Machine learning predictive model...

... Abstract Automated pattern recognition with basic, high-level coding can be readily applied to petroleum production surveillance to reduce the impact of equipment failure. Machine learning is an application of regression techniques that range in complexity from simple linear regressions...

... regressed to match the historical performance. At a specified interval, historical production is updated in the models and regression is performed automatically to keep the multiple models updated. This solution can address challenges related to historical well allocation given by lack of remote sensing...

... of equations are good and can be applied to any new well targeting the Mungaroo Formation in the Carnarvon Basin. log analysis machine learning well logging upstream oil & gas structural geology reservoir characterization flow in porous media artificial intelligence regression facies...

... in modeling. The proposed model predicts the bubble point pressure ( P b ) and the oil formation volume factor at bubble point pressure ( B ob ) as a function of oil and gas specific gravity, solution gas-oil ratio, and reservoir temperature by a boosted decision tree regression (BDTR) predictive modeling...

... Regression (continuous) and Classification (categorical) methods. Post exploratory data analysis, multivariate regression along with Multicollinearity/Variation Inflation Factor and outlier tests are applied to reduce the predictor variable list. Thereafter, classification models e.g. Gradient Boosting...

... of permeability values. In a homogenous sand unit, the conventional model can be used up to a certain degree of accuracy. machine learning pressure transient testing petrophysical facies model pressure transient analysis flow in porous media regression Reservoir Characterization Upstream Oil...

... architecture regression Introduction CO 2 injection is an EOR project with the most amount of interest. This has been the trend since 2002. Currently, CO 2 and thermal are the two EOR methods most frequently implemented around the world ( Surguchev et al. 2005 ) and contribute to most significant...

.... After generating a screening process, the next step is to build reliable forecasting methods for future NZBP job. Therefore, we utilize the equation resulted from previous multi-variable linear regression and 2 nd degree polynomial regression to estimate oil gain. This process is currently in place...

... for PL analysis in a 3-phase well. The results of the statistical analysis indicate that gas rate, oil rate, and water cut are critical for flow correlation selection. production control production logging gas liquid slippage reservoir surveillance regression machine learning artificial...

... is achieved. This happens where rock skeleton is very stiff. Where rock framework is loose, estimated AI is strongly affected by the presence of hydrocarbons. In this study, important observation is at the time one applies the linear regression to inversion data. The resulting porosity volume is severely...

... as a nonlinear regression method to develop transformation between the selected well logs and core measurements. The technique is demonstrated with an application to the well data in offshore Korea. The results show that the technique can make more accurate and reliable reservoir properties estimation compared...

.... The benefits of using cutting samples are; measurement of vector permeability, cutting faces are not damaged hence, more realistic measurement, and this measurement is made instantly in the field. permeability measurement permeability regression fluid dynamics permeameter flow in porous media...

... point pwf regression two-rate test straight line drillstem/well testing semi-log plot coefficient flow rate log-log plot storage capacity ratio cartesian plot reservoir upstream oil & gas drillstem testing skin factor interporosity flow coefficient fractured reservoir equation...

... to constrain layering definition are classified. Principles and guidelines on using different sources of information to define and refine the layering are recommended. An advanced regression technology is used to get optimized horizontal and vertical permeabilities of reservoir layers. The key is to find...

... Valley are adapted to operations in Sumatra. well logging temperature log multiple regression oil saturation log analysis pulsed neutron capture capture cross section kern river field saturation calculation equation saturation kern river duri steam saturation california upstream oil...

... and the same physical properties of each pseudo-component while the mole fractions of pseudo-components reflect the overall distribution of C 7+ mixtures. In addition, a regression technique has been developed to fit the PVT data and phase behavior for single or multiple samples of reservoir fluids...

... characterization effective porosity indonesia society spe 36969 seismic data characteristic basin interpretation regression petroleumengheer upstream oil & gas rantau field reservoir geology model gco regression zone 600 structural geology geological subdiscipline reservoir property geologist...

... production logging water regression INTRODUCTION The oil industry requires a multiphase flow meter to determine the flow rates of oil, water and gas in pipelines from oil wells. The MFM must be compact and mounted directly onto each well pipeline. It must continuously determine the flow rates to 5...