... PVT behavior and potentially eliminating need for rigorous fluid sampling using multivariate statistical methods. Artificial Intelligence machine learning regression model multivariate statistical method reservoir pressure GOR reservoir fluid composition fluid sample Upstream Oil...

... and machine learning to tackle this problem from a truly multivariable standpoint. The insights developed are widely applicable and may provide best practices for a varied range of challenges in EUR prediction. machine learning Reservoir Surveillance Artificial Intelligence switch point regime...

... and the results show that the new formulae predict the fracture closure pressure with a relative absolute error less than 5% compared to those estimated from the G-function analysis for both the shale and the tight formations cases which are not used in developing respective deviation terms. machine...

... interpretation methods across the industry. The purpose of this study is to demonstrate an automated process to identify accurate and consistent ISIP events in a high-frequency time-series data set using machine learning algorithms. This study is based on the analysis of metered high-frequency fracturing...

... natural fractures). Artificial Intelligence machine learning optimization problem hydraulic fracturing Simulation tighter cluster stress anisotropy History hydraulic fracture history matching misfit fracture geometry shale gas Upstream Oil & Gas well 1 well 2 injection treatment...

... given to the composition of chemicals being used in the completion design. This paper focuses on quantifying the true impact of these chemicals used in completion design by using machine learning to solve this multivariable problem and creates value by providing a framework to help completion engineers...

... dramatically. The potential and limitation of using a gradient method to solve this kind of problem are also shown in this paper. optimization problem hydraulic fracturing history matching Upstream Oil & Gas Artificial Intelligence reservoir simulation machine learning complex reservoir...

... evaluated with the physics-based models. machine learning proppant ace model functional form hydraulic fracturing cumulative oil production regression transformation average proppant concentration simulated dataset predictor variable Artificial Intelligence Upstream Oil & Gas optimal...

... Abstract Frac hits are a form of fracture-driven interference (FDI) that occur when newly drilled wells communicate with existing wells during completion, and which may negatively or positively affect production. An analytics and machine-learning approach is presented to characterize and aid...

... management reservoir simulation Project economics field development optimization and planning machine learning project valuation Upstream Oil & Gas Artificial Intelligence regression recovery reservoir factor Scenario complex reservoir optimization problem well density unconventional...

... networks made random forests an attractive option for such a study. drillstring design drilling process machine learning Upstream Oil & Gas drilling operation Artificial Intelligence predictor MSE WOB heat map flow rate ROP random forest rop model surface parameter ROP model...

... MEM production history asymmetry core data machine learning History geometry fracture permeability Midland Basin vertical well parent well fracture geometry artificial intelligence Introduction The Permian Basin (PB) is one of the largest and more structurally complex regions...

... installation and operations machine learning Introduction The Eagle Ford Shale (EFS) was deposited in the Late Cretaceous Period in a marginal to open marine setting ( Pessagno 1969 ; Surles 1987 ). The Lower Cretaceous part can be divided into two second-order transgressive-regressive cycles...

... performance, and improve the quality of reserves estimates. machine learning production monitoring production forecasting prediction Artificial Intelligence intervention Engineer operation production behavior Reservoir Surveillance Upstream Oil & Gas History workover Well Intervention...

... Permeability hydraulic fracturing drillstem/well testing correlation machine learning pressure transient testing pore pressure empirical equation reservoir property shut-in pressure g-function analysis injection overburden pressure fracture pressure new empirical equation permeability porosity...

... Abstract The objective of this work is to accurately determine horizontal shale well EUR for an area integrating geology, machine learning, pattern recognition and statistical analysis using various parameters of nearby producing horizontal shale wells, as inputs. This work utilizes local...

... of pyrite effect. machine learning Reservoir Characterization well logging Artificial Intelligence shale gas Upstream Oil & Gas concentration Eagle Ford shale porosity Pyrolysis new model kerogen petrophysical model organic carbon pyrite organic content organic porosity shale...

... the degree of interference. shale oil machine learning Artificial Intelligence complex reservoir regression communication analytical well interference model fracture society of petroleum engineers hydraulic fracturing Upstream Oil & Gas fractured stage interference analytical model...

... decline curve methodology based on the type of reservoir fluid. production control machine learning Artificial Intelligence cumulative production diagnostic plot production rate condensate production equation production monitoring production forecasting Duong Reservoir Surveillance...