In this study, an attempt is made to predict EFFECTIVE POROSITY in one of the oil fields in the Persian Gulf by designing a probablistic neural network (PNN) and simultanusely making use of seismic attributes and EFFECTIVE POROSITY logs in the reservoir window. This was done by deriving a multiattribute transformation between an optimum subset of seismic attributes and the EFFECTIVE POROSITY logs.The geophysical data used in this study consist of 3D seismic pre-stack time migrated (PSTM) data with 12.5*12.5 m grid size and a 4 ms sampling rate. The length of the seismic traces are two seconds. Well logs of five vertical wells in the study area, including Sonic (DT), Density (RHOB), EFFECTIVE POROSITY (PHIE) and Seismic Well Velocity Surveys (Check Shots), were used. The reservoir layer is a Mishrif member of the Sarvak formation with Cretaceous age, which is common in oil reservoirs in the Persian Gulf. The top of the Mishrif is adjusted with the Middle Turonian Unconformity and covered with shaley Laffan formation. The Mishrif Reservoir in study area contains two reservoir zones. The lower zone with higher clay content is separate from the upper zone. The upper zone consists of clean limesone with better reservoir properties. Seismic traces close to the well locations were used to generate seismic attributes. EFFECTIVE POROSITY logs at the reservoir area were the target logs in this study.The designed neural network consists of one input layer, one hidden layer with four processing units (neuron), and one output layer with one neuron. In order to prepare training samples for the neural network, PHIE logs were converted to time domain using a time-depth relationship calculated from the DT logs and check shot curves for each well location. Subsequently, these logs were filtered (using a Hanning filter with 4 ms length) and resampled with seismic sampling rate (4 ms). Finally, a set of seismic attributes, including sixteen sample-based seismic attributes, were generated using HRS software. Training samples in this study consisted of 57 samples (selected seismic attributes and their related EFFECTIVE POROSITY from PHIE logs in the time domain). For training the network, the samples were divided into three data sets: the training samples, cross validation samples and testing samples. The training data were used for adjusting the weights of the network; the cross validation data were used to prevent overtraining theneural network; and the testing data were used to ensure generalizabillity of the network output.A forward stepwise regression process was used to determine an optimum subset of attributes for use in the training of the neural networks. The optimum subset of attributes in this study consists of the Dominant Frequency, Amplitude Weighted Frequency, Integrated Absolute Amplitude and Filter 45-60 Hz.After the network was trained using training and cross validation data sets, it was used to predict the testing data. The results show a good correlation between real and predicted data, with 92% correlation. Finally, in order to attain a better generalization of the network, testing data sets were inserted to trained data and the network was trained again. This network was then used to predict EFFECTIVE POROSITY in well locations which increased the correlation coefficient to 95%. This study shows the ability of the PNN networks to predict EFFECTIVE POROSITY even with a paucity of training examplares.

Hydraulic conductivity and EFFECTIVE POROSITY are the most important parameters in determining drain spacing. These properties have temporal and spatial variation and estimating average values for them is difficult and costly. In this study, one dimensional differential equation of unsteady flow towards drainage was numerically solved using the control volume approach. Then by selecting a proper optimization algorithm, an inverse model was developed, calibrated and verified. In addition to numerical model, Glover-Dumm analytical solution was also used for the development of an inverse model. Then saturated hydraulic conductivity and EFFECTIVE POROSITY were estimated using these numerical and analytical inverse models. Results indicated, that using values of hydraulic conductivity and EFFECTIVE POROSITY obtained from numerical inverse model compared to experimental ones, resulted in a more accurate prediction of water table by proposed numerical method. Also, the efficiency of the proposed numerical model ( 0.93 ) is higher than the analytical ones (0.75).

An essential work for reservoir modeling specifically fractured reservoirs and development of oil and gas fields is the delineation of geological and geomechanical attributes such as rock type, e.g., EFFECTIVE POROSITY and permeability. The purpose of this investigation is delineation of EFFECTIVE POROSITY zones in an oilfield reservoir at the southern Iran for detection of fractured reservoir rocks. First, this parameter was estimated in 3D environment of the Asmari reservoir. Next, fractal number-size (N-S) and concentration-volume (C-V) models were utilized for categorization of the EFFECTIVE POROSITY zones. The C-V fractal model represents three EFFECTIVE POROSITY zones, and the N-S fractal analysis shows six zones with an index multifractal feature for the EFFECTIVE POROSITY data. The correlation between the results obtained by these fractal models presented that the obtained zones have a proper overlap together. High value porous zones based on these fractal models are commenced from 11% and 12.5%, respectively. Fractal modeling indicates that the porous zones happened in the SE and NW parts of the study oil field that presents the fractured part of the Asmari reservoir rock. Main faults from this oilfield are correlated with the porous zones derived via fractal modeling

Identification and delineation of different zones in oil fields are among the fundamental tasks in petroleum explorations. Fractal methods are useful tools for such purposes. The aim of this paper is to conduct a comparative study of Concentration-Area (C-A) and Number-Size (N-S) fractal models to separate EFFECTIVE porous and permeable zones based on core logging samples collected from one of the oilfields in southern Iran. However, permeability and POROSITY threshold values were calculated based on the C-A and N-S log-log plots. A comparison between the C-A and N-S fractal results showed that the C-A method is more compatible with reality, and it is capable of separating permeable and porous zones in this oilfield.

Determination of the EFFECTIVE POROSITY and water saturation of a hydrocarbon reservoir plays a very important role in the petroleum upstream industry, especially in the economic success of reservoir development and this task is yet encountered with many technical and economical problems. Artificial neural network (ANN) is one of the latest techniques for modeling and simulation that can also be used for prediction of petrophysical parameters. In this study, two separate “error back-propagation” ANNs are used for prediction of EFFECTIVE POROSITY and water saturation from just wireline logs data. The EFFECTIVE POROSITY ANN is a three-layer network using sonic, density, neutron POROSITY, gamma ray and LLD (resistivity log in uninvaded zone or deep resistivity log) logs as input with 8 neurons in hidden layer and “Sigmoid” activation function for both hidden layer and output layer. The water saturation ANN is also a three-layer network using sonic, density, gamma ray, MSFL (resistivity log in flushed zone), LLS (resistivity log in transition zone or shallow resistivity log) and LLD logs as input with 10 neurons in hidden layer and “Hyperbolic Tangent” activation function for hidden layer and “Sigmoid” activation function for output layer. This study was performed on Sarvak Formation, an important oil reservoir, in the Zagros Basin, southern Iran and the results show that EFFECTIVE POROSITY and water saturation can be estimated with high accuracy using ANN.

Summary: In this paper, by combination of seismic data and well log data, the EFFECTIVE POROSITY in the space between wells is estimated. One of the important petroleum reservoir features is EFFECTIVE POROSITY that engineers are always looking to find an appropriate model for distribution of this parameter in the reservoir. The petrophysical properties of petroleum reservoirs are very complex. In the last few decades, the EFFECTIVE POROSITY estimation procedures have become one of the hot topics in the industry to evaluate these procedures or methods. In the current research, by integration of petrophysical, seismic data and seismic attributes classification using Adaboost algorithm, it is tried to estimate the EFFECTIVE POROSITY in a twodimensional seismic cross section of the block F3 Dutch sector of the North Sea. ...

In this study, a simple and novel supercritical dryer system was designed to drying of wet gel, aiming for the removal of the high energy-consumption equipment such as pump and strong compressor. The system is capable to provide the supercritical state in the fluid, only using the control of thermodynamic conditions. To ensure the proper efficiency of the developed dryer system, the silica aerogel was successfully derived from tetraethyl orthosilicate as starting material of silica. The amorphous phase of silica was identified by XRD analysis. The specific surface area and average pore size were measured at about 659 m2/g and 22 nm, respectively, via the BET method. The N2 adsorption-desorption isotherm curve shows the type IV isotherm, indicating the mesoporous structure with cylindrical and capillary pores. The bulk density and POROSITY were measured at about 0.11 g/cm3 and 95%, respectively. The TEM and FESEM micrographs indicate porous and interconnected structures along with open mesopores in the range of 10-30 nm. These results confirm the proper efficiency and performance of the designed supercritical dryer system.

One of the ways to deal with water crisis is the use of unconventional, such as subsurface brackish water mixed with a proper crop management. The use of such water may affect the saturated hydraulic conductivity. A direct measurement of saturated hydraulic conductivity requires the expenditure of a lot of time and money. A lysimeter experiment was conducted to investigate the effect of water table salinity and depth management on saturated hydraulic conductivity and EFFECTIVE POROSITY and as well, to determine the relationship among them. The statistical design was a split-split plot arrangement of a randomized complete block design of three replicates for each treatment. Treatments included 3 levels of groundwater salinity (main plot; S1=<4, S2=8 and S3=12 dS/m) and 2 levels of water table depths (sub plot; D1=60 and D2=90 cm) and 2 levels of soil surface cover as sub plots (M1=no mulch and M2=date palm leaves mulch). Prior to the start of the experiments and as well 15 months past of the treatments, saturated hydraulic conductivity as well as EFFECTIVE POROSITY were recorded. The treatments indicated no effect on these parameters at 5% level. But mulch treated samples led to a decrease of soil salinity increasing seedling growth as well as root volume. As a result, these treatments showed to increase the saturated hydraulic conductivity. The relationship between saturated hydraulic conductivity and EFFECTIVE POROSITY was investigated using regression analysis. The best fit between Ln (KS) and fe values was concluded by a sigmoidal function (R2=0.73).

Monolithic aerogels of high molecular weight polyethylene (Mw=3×106- 6×106 g/mol) have been prepared by solvent extraction with supercritical carbon dioxide from thermoreversible gels prepared in decalin. These low density and highly porous aerogels present an apparent POROSITY up to 90%. The aerogel morphology observed by scanning electron microscopy (SEM) is characterized by spherulitic structures being interconnected by fibers. The X-ray diffraction experiments show that PE aerogels are highly crystalline with a degree of crystallinity of c.a.80% and PE chains being packed into the typical orthorombic unit cell. The combined SEM and N2 sorption investigations show that PE aerogels are essentially macroporous with a small amount of mesopores. The oil-sorption performance of polyethylene aerogels has been also evaluated in this study in order to assess a possible use of these materials for oil spillage recovery and results show that aerogel macropores allow a very fast sorption kinetics with a 100% oil weight uptake obtained in less than 1 min.