One of the most difficult tasks in reservoir simulations is reliable characterization of fractured subsurfaces. A typical situation in petroleum engineering employs dynamic data integrations such as the oil production history to be matched with simulated responses associated with a set of porosity and/or permeability fields. Among the challenges found in practice are proper mathematical modelin...

a numerical investigation of forced convection in a channel with hot solid block inside a square porous block mounted on a bottom wall was carried out. the lattice boltzmann method was applied for numerical simulations. the fluid flow in the porous media was simulated by brinkman-forchheimer model. the effects of parameters such as porosity and thermal conductivity ratio over flow pattern and t...

In this paper we present a method to integrate well test, production, shut-in pressure, log, core, and geological data to obtain a reservoir description for the Pagerungan field, offshore Indonesia. The method computes spatial distributions of permeability and porosity and generates a pressure response for comparison to field data. This technique produced a good match with well-test data from t...

Rock classification or assigning a type or class to a specific rock sample based on petrophysical characteristics is a fundamental technique to reduce the uncertainty in prediction of reservoir properties due to heterogeneity. One of the popular approaches is to classify a reservoir rock from the fundamentals of geology and the physics of flow at pore network scale. In this approach, rocks of s...

It is widely recognized that groundwater flow and solute transport in natural media are largely controlled by heterogeneities. In the last three decades, many studies have examined the effects of heterogeneous hydraulic conductivity fields on flow and transport processes, but there has been much less attention to the influence of heterogeneous porosity fields. In this study, we use porosity and...

In this paper we study the problem of simulating shared memory on the distributed memory machine (DMM). Our approach uses multiple copies of shared memory cells, distributed among the memory modules of the DMM via universal hashing. The main aim is to design strategies that resolve contention at the memory modules. Extending results and methods from random graphs and very fast randomized algori...

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...

We present a series of numerically calculated plate height and flow resistance data obtained for an idealized chromatographic support mimic with variable bed porosity (0.3 </= epsilon </= 0.9), yielding a unique insight into how the main chromatographic performance parameters can be expected to vary with the external bed porosity, unbiased by any differences in molecular diffusivity or retentio...