Porosity Prediction Model Research Articles

Porosity model and its application in tight gas sandstone reservoir in the southern part of West Depression, Liaohe Basin, China

Abstract The Liaohe basin is a Tertiary rift developed on a paleohigh, characterized by active fault subsidence. Western Depression, one of the most productive areas in Liaohe basin, has been a long-term depositional and subsidence center since Cenozoic. The Shahejie Formation (Es) in the Western Depression are not only the most important source rocks responsible for the major hydrocarbon accumulations, they are also the major reservoir rocks. The third member of Shahejie Formation (Es3) mainly develops sublacustrine fan tight gas sandstones and its sandstones are mainly lithic arkoses, texturally and compositionally immature. The article has developed an origin-based porosity model relying on geologic models and concepts, based on the correlation between sedimentary facies and diagenesis. The porosity prediction model is established on the analysis of the following steps: (1) establish diagenesis index ( ID ), the function of temperature, Ro, quartz overgrowth, I/S, depth, and time; (2) calculate sedimentary facies index ( IF ), which is established based on the distribution of porosities in different sedimentary facies during eogenetic stage, and is higher in the sedimentary facies with higher porosity; and (3) build the porosity model based on the relationship between the exponential porosity function of ID and the compaction curve of IF and depth by the linear regression method. This model not only predicts the present-day porosity but also reconstructs the porosity history. The results show that the present-day porosity decreases with increasing diagenetic degree from basin boundary to basin center. And the reservoirs in braided channel and its lateral and front edges of middle fan have higher porosity. The results show that from the early period of deposition to the end of Es32 deposition, Es33 sandstones were in the eogenetic stage with the porosity approximately varied from 35.6% to 14% by mechanical compaction. During the deposition of Es31, sandstones were mainly in the mesogenetic stage A1–A2 undergoing the continuous compaction along with quartz and carbonate cementation. The porosity of Es33 sandstone decreased from 14% to 8%. Moreover, a large amount of secondary porosity was formed with the porosity approximately increased by 2.45%. Up to present-day, Es33 sandstones are mainly in the mesogenetic stage B during the slow subsidence. Most parts of Es33 sandstones are becoming tight with the porosity fell to 4.8% by late compaction and cementation. The improved porosity model not only predict deep reservoir-quality distribution at a more detailed field scale, but also is widely used to other tight sandstone fields that have similar geological background and to identify sweet spots for production.

Porosity prediction of calcium phosphate cements based on chemical composition.

The porosity of calcium phosphate cements has an impact on several important parameters, such as strength, resorbability and bioactivity. A model to predict the porosity for biomedical cements would hence be a useful tool. At the moment such a model only exists for Portland cements. The aim of this study was to develop and validate a first porosity prediction model for calcium phosphate cements. On the basis of chemical reaction, molar weight and density of components, a volume-based model was developed and validated using calcium phosphate cement as model material. 60 mol% β-tricalcium phosphate and 40 mol% monocalcium phosphate monohydrate were mixed with deionized water, at different liquid-to-powder ratios. Samples were set for 24 h at 37°C and 100% relative humidity. Thereafter, samples were dried either under vacuum at room temperature for 24 h or in air at 37 °C for 7 days. Porosity and phase composition were determined. It was found that the two drying protocols led to the formation of brushite and monetite, respectively. The model was found to predict well the experimental values and also data reported in the literature for apatite cements, as deduced from the small absolute average residual errors (<2.0%). In conclusion, a theoretical model for porosity prediction was developed and validated for brushite, monetite and apatite cements. The model gives a good estimate of the final porosity and has the potential to be used as a porosity prediction tool in the biomedical cement field.

Support vector regression for porosity prediction in a heterogeneous reservoir: A comparative study

In wells with limited log and core data, porosity, a fundamental and essential property to characterize reservoirs, is challenging to estimate by conventional statistical methods from offset well log and core data in heterogeneous formations. Beyond simple regression, neural networks have been used to develop more accurate porosity correlations. Unfortunately, neural network-based correlations have limited generalization ability and global correlations for a field are usually less accurate compared to local correlations for a sub-region of the reservoir. In this paper, support vector machines are explored as an intelligent technique to correlate porosity to well log data. Recently, support vector regression (SVR), based on the statistical learning theory, have been proposed as a new intelligence technique for both prediction and classification tasks. The underlying formulation of support vector machines embodies the structural risk minimization (SRM) principle which has been shown to be superior to the traditional empirical risk minimization (ERM) principle employed by conventional neural networks and classical statistical methods. This new formulation uses margin-based loss functions to control model complexity independently of the dimensionality of the input space, and kernel functions to project the estimation problem to a higher dimensional space, which enables the solution of more complex nonlinear problem optimization methods to exist for a globally optimal solution. SRM minimizes an upper bound on the expected risk using a margin-based loss function ( ε-insensitivity loss function for regression) in contrast to ERM which minimizes the error on the training data. Unlike classical learning methods, SRM, indexed by margin-based loss function, can also control model complexity independent of dimensionality. The SRM inductive principle is designed for statistical estimation with finite data where the ERM inductive principle provides the optimal solution (the empirical risk approaches the expected risk) only for asymptotic (large sample data). The SRM principle matches model complexity to the available data through controlling the tradeoff between complexity of the model and quality of fitting the data. It is this difference which equips support vector machines (SVM) with a greater ability to generalize beyond the training data. Here, a SVR-based porosity prediction model is developed for a heterogeneous sandstone reservoir. The SVR method has been compared to multilayer perceptron, General Regression Neural Networks, and Radial Basis Function Neural Networks. The results reveal that the SVR method exhibits superior accuracy and robustness with respect to these neural network methods especially with respect to accuracy when generalizing to previously unseen porosity data.

97/01751 The Fischer-Tropsch reaction with supported ruthenium catalysts: Modeling and evaluation of the reaction rate equation for a fixed bed reactor

The Liaohe basin is a Tertiary rift developed on a paleohigh, characterized by active fault subsidence. Western Depression, one of the most productive areas in Liaohe basin, has been a long-term depositional and subsidence center since Cenozoic. The Shahejie Formation (Es) in the Western Depression are not only the most important source rocks responsible for the major hydrocarbon accumulations, they are also the major reservoir rocks. The third member of Shahejie Formation (Es3) mainly develops sublacustrine fan tight gas sandstones and its sandstones are mainly lithic arkoses, texturally and compositionally immature. The article has developed an origin-based porosity model relying on geologic models and concepts, based on the correlation between sedimentary facies and diagenesis. The porosity prediction model is established on the analysis of the following steps: (1) establish diagenesis index (ID), the function of temperature, Ro, quartz overgrowth, I/S, depth, and time; (2) calculate sedimentary facies index (IF), which is established based on the distribution of porosities in different sedimentary facies during eogenetic stage, and is higher in the sedimentary facies with higher porosity; and (3) build the porosity model based on the relationship between the exponential porosity function of ID and the compaction curve of IF and depth by the linear regression method. This model not only predicts the present-day porosity but also reconstructs the porosity history. The results show that the present-day porosity decreases with increasing diagenetic degree from basin boundary to basin center. And the reservoirs in braided channel and its lateral and front edges of middle fan have higher porosity. The results show that from the early period of deposition to the end of Es32 deposition, Es33 sandstones were in the eogenetic stage with the porosity approximately varied from 35.6% to 14% by mechanical compaction. During the deposition of Es31, sandstones were mainly in the mesogenetic stage A1–A2 undergoing the continuous compaction along with quartz and carbonate cementation. The porosity of Es33 sandstone decreased from 14% to 8%. Moreover, a large amount of secondary porosity was formed with the porosity approximately increased by 2.45%. Up to present-day, Es33 sandstones are mainly in the mesogenetic stage B during the slow subsidence. Most parts of Es33 sandstones are becoming tight with the porosity fell to 4.8% by late compaction and cementation. The improved porosity model not only predict deep reservoir-quality distribution at a more detailed field scale, but also is widely used to other tight sandstone fields that have similar geological background and to identify sweet spots for production.

Multiphysics modelling of the metals casting process

Metals casting is a process governed by the interaction of a range of physical phenomena. Most computational models of this process address only what are conventionally regarded as the primary phenomena-heat conduction and solidification. However, to predict the formation of porosity (a factor of key importance in cast quality) requires the modelling of the interaction of the fluid flow, heat transfer, solidification and the development of stress-deformation in the solidified part of a component. In this paper, a model of the casting process is described which addresses all the main continuum phenomena involved in a coupled manner. The model is solved numerically using novel finite volume unstructured mesh techniques, and then applied to both the prediction of shape deformation (plus the subsequent formation of a gap at the metal-mould interface and its impact on the heat transfer behaviour) and porosity formation in solidifying metal components. Although the porosity prediction model is phenomenologically simplistic it is based on the interaction of the continuum phenomena and yields good comparisons with available experimental results. This work represents the first of the next generation of casting simulation tools to predict aspects of the structure of cast components.