Although the random sum distribution has been well-studied in probability theory, inference for the mean of such distribution is very limited in the literature. In this paper, two approaches are proposed to obtain inference for the mean of the Poisson-Exponential distribution. Both proposed approaches require the log-likelihood function of the Poisson-Exponential distribution, but the exact for...

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...

Rock selection in modeling and simulation studies is usually based on two techniques; routinely defined rock types and those defined by special core analysis (SCAL). The challenge in utilizing these two techniques is that they are frequently assumed to be the same, but in practice, static rock-types (routinely defined) are not always representative of dynamic rock-types (SCAL defined) in the re...

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 ...

the spatial distribution of petrophysical properties within the reservoirs is one of the most importantfactors in reservoir characterization. flow units are the continuous body over a specific reservoirvolume within which the geological and petrophysical properties are the same. accordingly, anaccurate prediction of flow units is a major task to achieve a reliable petrophysical description of a...