Comparison of Different Univariate and Multivariate Geostatistical Methods by Porosity Modeling of an Iranian Oil Field

Abstract

Geostatistical methods are grouped in two main divisions: univariate and multivariate. When there is adequate amount of primary data, univariate methods such as kriging and SGS give a good representation of property distribution in the reservoir, but practical difficulties appear when there is no sufficient data. In such a case it is necessary to choose multivariate geostatistical methods in which some covariables are contributed to model the primary variable. Multivariate geostatistics is a broad term that encompasses all geostatistical methods that utilize more than one variable to predict some physical property of the earth. Bivariate geostatistics is obviously the simplest subset of the multivariate techniques and thus the standard cokriging technique can be called multivariate geostatistics. Cokriging estimates the unsampled value using the primary and secondary variable values from all primary variable sampled locations and also the covariable at an unsampled location, thus there is a need to construct the secondary and cross covariograms. Hence, it is a time-consuming approach. Collocated cokriging is a valuable alternative to full cokriging, which overcomes these problems. It only requires the covariable sample to be available at every location where the principal variable must be estimated. The authors used this in the framework of sequential simulation to produce various realizations. They compare moving average, kriging, and SGS as univariate methods to collocated cokriging and collocated cosimulation as multivariate methods by porosity modeling of an Iranian oil field, although there were only 7 wells available.     

The Semi-Analytical Modeling and Simulation of the VAPEX Process of “Kuh-e-Mond” Heavy Oil Reservoir

Abstract

The vapor extraction process (or VAPEX) uses vaporized solvents injected into a horizontal well to form a vapor chamber within the reservoir. Vapor dissolves in the oil and enhances the oil production by decreasing the oil viscosity in heavy oil reservoirs. To evaluate the process we conduct a simulation study on an Iranian heavy oil reservoir called Kuh-e-Mond. In addition, a semi-analytical investigation of the VAPEX process has been performed. The idea is to perform VAPEX simulation for a laboratory model and find a methodology to compare the results of the simulator with the semi-analytical Butler's model. In particular, a semi-analytical dimensionless correlation for production rate that incorporates all involved physical parameters in the VAPEX process is developed. Also, we performed a sensitivity analysis on the proposed correlation to obtain its adjustable parameters and optimize using available experimental data.     

Well Placement Optimization Using Hybrid Optimization Technique Combined with Fuzzy Inference System

Decision on the location of new wells through infill drilling projects is a complex problem that depends on the reservoir rock and fluid properties, well and surface facilities specifications, and economic measures. Conventional approach to address this is a direct optimization that uses the numerical flow simulation. However, this is computationally very extensive. In this study the authors use a hybrid genetic algorithm (HGA) optimization technique based on the genetic algorithm (GA) with helper functions based on the polytope algorithm and the neural network. This hybridization introduces hill-climbing into the stochastic search and makes use of proxies created and calibrated iteratively throughout the run. It is emphasized that the numerical models are constructed based on scarce data, hence the simulation forecasts are uncertain and consequently the deterministic global solution is not achievable. To resolve this the authors used a Fuzzy Inference System (FIS). For economic evaluation they use net present value (NPV). Therefore, the FIS output is incorporated into the NPV, and a new objective function called corrected NPV (CNPV) is constructed. The authors validate the method by optimizing the placement of water injection wells in a section of an oil reservoir located in the west of Iran by maximizing the CNPV. It was observed that the number of simulations required to find the optimal well configurations were reduced significantly by using HGA.     

FIVE-SPOT INJECTION/PRODUCTION WELL LOCATION DESIGN BASED ON FRACTURE GEOMETRICAL CHARACTERISTICS IN HEAVY OIL FRACTURED RESERVOIRS DURING MISCIBLE DISPLACEMENT: AN EXPERIMENTAL APPROACH

Mapping fracture characteristics by using seismic acquisition and processing is important not only to identify sweet spots, but also to optimize production, especially for unconventional heavy oil reservoirs. In this experimental work we used five-spot micromodels initially saturated with heavy oil to find the optimum well locations during first-contact miscible displacement. The experiments were performed at a fixed injection rate on fractured micromodels with various patterns. The optimum location for injection/production wells was found in the pattern where fractures make an angle of 45° with the mean flow direction. Moreover, oil recovery was increased with the density, length, level of scattering, and discontinuity of fractures. The analysis of the experimentally measured recovery curve revealed that there are three distinct stages for each displacement. The efficiency of the first stage was found to be dominated by dispersion and diffusion. However, the recovery of the second stage was significantly affected by the fracture orientation. The displacement efficiency of the third stage was controlled by solvent dispersion, which is at maximum for the pattern with higher density, length, scattering, and discontinuity of fractures. Saturation monitoring showed that the fracture geometrical characteristics strongly affected the splitting, spreading, and shielding of the produced fingers and solvent front shape and consequently affected the recovery factor. As a result, five-spot micromodels can be used to investigate the optimum location of injection/production wells during miscible displacements in fractured heavy oil reservoirs.     

The Estimation of Formation Permeability in a Carbonate Reservoir Using an Artificial Neural Network

Abstract

Reservoir permeability is an important parameter that its reliable prediction is necessary for reservoir performance assessment and management. Although many empirical formulas are derived regarding permeability and porosity in sandstone reservoirs, these correlations cannot be accurately depicted in carbonate reservoir for the wells that are not cored and for which there are no welltest data. Therefore, having a framework for estimation of these parameters in reservoirs with neither coring samples nor welltest data is crucial. Rock properties are characterized by using different well logs. However, there is no specific petrophysical log for estimating rock permeability; thus, new methods need to be developed to predict permeability from well logs. One of the most powerful tools that we applied by the authors is artificial neural network (ANN), whose advantages and disadvantages have been discussed by several authors. In particular, 767 data sets were used from five wells of Bangestan reservoir in a southwestern field of Iran. Depth, Neutron (NPHI), Density (RHOB), Sonic (DT) logs, and evaluated total porosity (PHIT) from log data were used as the input data and horizontal permeability obtained by coring was as target data. Sixty percent of these data points were used for training and the remaining for predicting the permeability (i.e., validation and testing). An appropriate ANN was developed and a correlation coefficient (R) of 0.965 was obtained by comparing permeability predictions and the actual measurements. As a result, the neural science can be used effectively to estimate formation permeability from well log data.     

An Improvement of the Matrix-fracture Transfer Function in Free Fall Gravity Drainage

The simulation of fractured reservoir is conventionally performed by using dual porosity formulation in which the type of transfer function may be critical. Over the past few years, various models with their strength and weakness have been developed to account for matrix-fracture interporosity flow. However, some of them are unable to simulate some mechanisms like gravity drainage. In this work, the most well-known transfer functions have been examined for simulation of the gravity drainage in a single block model and an improvement has been introduced to modify them. The validation of the developed approach have been done by using fine grid simulation.     

Rosette-forming cells during immune response to Toxoplasma gondii in mice

The rosette-forming cell (RFC) response of mice immunized with varying doses of Toxoplasma gondii was studied by immunocytoadherence (ICA). The specificity of ICA in the present system was tested by passive sensitization with hyperimmune serum in vivo and in vitro. A slight increase in RFC was observed with the latter. Prior treatment of spleen cells from immunized animals with rabbit anti-mouse immunoglobulin resulted in total inhibition of ICA. During the primary and the secondary response after 10 days, the number of RFC rose rapidly to reach the peak on the 3rd day. With secondary immunization 30 days later, the peak shifted to the 2nd day. Mice infected with a lower dose of Toxoplasma had a greater number of RFC during the secondary response after 10 days than with a larger dose.