Abstract The application of machine learning methods is aimed at providing efficiency and avoiding subjectivity in estimating reservoir porosity data. This study proposes the eXtreme Gradient Boost (XGBoost) algorithm which known to be effective accurate predictions a short time for porosity. model was optimized using GridSearchCV (GS) module, then applied 7 wells from Damar field, Indonesia wi...

an improved model of mud dispersion has been introduced in this work. the advantages of this model consist of a new analytical correlation for dispersivity by using resistivity log data and using a new aspect of capacitance dispersion model. mathematical formulations were expressed, solved by numerical model taking advantage of actual log and formation data. achieved results yielded reasonable ...

Machine learning has a significant advantage for many difficulties in the oil and gas industry, especially when it comes to resolving complex challenges reservoir characterization. Permeability is one of most difficult petrophysical parameters predict using conventional logging techniques. Clarifications work flow methodology are presented alongside comprehensive models this study. The purpose ...

reservoir permeability is a critical parameter for characterization of the hydrocarbon reservoirs. in fact, determination of permeability is a crucial task in reserve estimation, production and development. traditional methods for permeability prediction are well log and core data analysis which are very expensive and time-consuming. well log data is an alternative approach for prediction of pe...

Porosity is considered as an important petrophysical parameter in characterizing reservoirs, calculating in-situ oil reserves, and production evaluation. Nowadays, using intelligent techniques has become a popular method for porosity estimation. Support vector machine (SVM) a new intelligent method with a great generalization potential of modeling non-linear relationships has been introduced fo...

porosity is one of the fundamental petrophysical properties that should be evaluated for hydrocarbon bearing reservoirs. it is a vital factor in precise understanding of reservoir quality in a hydrocarbon field. log data are exceedingly crucial information in petroleum industries, for many of hydrocarbon parameters are obtained by virtue of petrophysical data. there are three main petrophysical...

Seismic inversion is a method that extracts acoustic impedance data from the seismic traces. Source wavelets are band-limited, and thus seismic traces do not contain low and high frequency information. Therefore, there is a serious problem when the deterministic seismic inversion is applied to real data and the result of deterministic inversion is smooth. Low frequency component is obtained fro...