Research

The research area of PermLab is summarized in the following table:


1- Enhanced Oil Recovery by Polymer flooding

Polymer flooding is a well-known commercial method among enhanced oil recovery (EOR) methods for conformance control purposes. Different smart polymers have been investigated in the Permlab research team. For example, nanometric smart-covered polymer particle (SCPP) to reduce conventional polymer flooding drawbacks (Ashrafizadeh et al. 2012, 2017), preformed particle gels (PPG) to improve conformance control in mature waterflooded reservoirs (Farasat et al. 2017 a, b) and pH-sensitive polymers (Younesian & Sadeghnejad 2018). Among them, the later method is  the focus of the current research in Permlab. Implementing pH-sensitive polymer is an efficient way to improve conformance control of petroleum formations. pH-sensitive polymer solutions have high mobility in low pH conditions, while, due to their high swelling capacity in higher pH values, their viscosity increases with pH increment. Therefore, they can fill high permeability zones/strata and consequently improve the enhanced oil recovery efficiency.

Different steps of pH-triggered polymer flooding. a) Water breakthrough during conventional water flooding process because of its flowing through high permeability areas; b) acid pre-flush in order to decrease the pH of the high permeable areas; c) Injection of pH-triggered polymer that has low viscosity in an acidic environment; d) Post water flooding with the  higher sweep efficiency in comparison to (a) (Younesian & Sadeghnejad 2018)


2-Pore Network Modeling of Carbonate Rocks

Predicting petrophysical properties in carbonate reservoirs is a challenging task due to the nature of the deposition and diagenesis, which depends on pore-scale features and heterogeneity at multiple length scales (Sok et al. 2010). The pore/throat size distribution and morphology of a carbonate rock strongly influence its petrophysical properties (e.g., porosity, permeability, capillary pressure, etc). The presence of multiple length scales in pore-size distribution (and the interconnectivity of pores) of carbonates is what fundamentally differentiates them from sandstones (Prodanović et al. 2015). Pore Network Modeling (PNM) is an approach for depicting flow behaviors in complex pore space morphology, which can help in clarifying the rock properties that are difficult to get using the present experimental methods (e.g., SCAL). In the PNM approach, the porous domain is mapped onto a set of pore bodies of different sizes connected through variable diameter pore throats. This method tries to predict fluid transport properties as a function of porous structure (e.g., porosity, pore sizes, coordination number, distribution of pore sizes, tortuosity) with the relevant physics implemented on a pore-to-pore basis. In this field of study, a new computer-modeling algorithm to reconstruct bi-modal vuggy porous medium was introduced by coupling pore-network modeling approach with image-based network techniques (Sadeghnejad & Gostick 2018, Sadeghnejad & Gostick 2019). This approach implements image-processing techniques to generate a two-dimensional lattice-based network of secondary porosity (i.e., vugs) on top of an initial pore network model at the pore scale. The resulting multi-scale model can efficiently preserve vug-to-vug and vug-to-pore connectivity through generating throats with proper sizes and lengths.

 A sample vugular PNM. Only connecting throats are shown in this figure. Touching vugs (connected by green throats together), isolated vugs (connected by red throats to their neighbor micro-pores), and boundary vugs (those vugs intersecting the domain boundaries) are shown  (Sadeghnejad & Gostick 2018, Sadeghnejad & Gostick 2019).

A sample realization of a vuggy carbonate containing 59 vugs with radius from 3 to 100 μm (Sadeghnejad & Gostick 2019)


3-Reservoir Characterization
Well-to-well correlation plays an important role in the characterization of hydrocarbon formations. Recognition of geological boundaries to provide a 3-D geological model of a formation is an essential task. The common sets of data that are used to build a geological model include welllogs, cores, and seismic data. Available cored wells are usually limited and the entire area of a field is not surveyed geophysically; nevertheless, logging operations are widely performed in most wells. Therefore, well-logs due to their availability are a desirable source for stratigraphic correlation (Perez-Muñoz et al., 2013). Well-to-well correlation is usually performed manually and usually involves a large amount of visual and qualitative analysis on big data sets. Thus, there is a need to introduce automatic approaches that can assist interpreters during well-to-well correlations. To do so, we at Permlab, try to speed up the well-to-well correlation process by introducing an automated approach (Partovi & Sadeghnejad 2017). This algorithm automatically searches for similar depths associated with those geological boundaries in other wells. The fractal parameters of well-logs, which are calculated by wavelet transform, are considered as pattern recognition dimensions during the well-to-well correlation approach. To validate the proposed technique, it was implemented in several wells of Iranian fields. The results show the capability of the introduced automatic method in the detection of geological boundaries during the automated well-to-well correlations (Partovi & Sadeghnejad 2018).

Feature extraction in reference and observation wells using a moving window along the well depth (Partovi & Sadeghnejad 2017). 


4-Reservoir Connectivity Modeling

The connectivity and conductivity (permeability) of a porous medium control the performance of production (e.g., recovery factor). Different methods were implemented to estimate these key properties. Percolation theory is one of those methods, which has many applications in fluid flow in porous media. The importance of this methodology can be revealed when reliable input data for reconstruction of a detailed porous media is not available (e.g., during the initial production life of a hydrocarbon formation) (Sadeghnejad et al. 2011a, b, 2014). This theory implements some scaling functions to predict the behavior of porous media (Sadeghnejad et al. 2012, 2013a).

Percolation theory is implemented using different approaches, e.g., site and continuum percolation. The classic site percolation approach is composed of a square lattice system, wherein objects are randomly distributed throughout system sites that their locations are fixed; whereas, in continuum percolation, objects may overlap each other. The classic percolation theory is based on connectivity between the opposite sides the system (Sadeghnejad et al. 2013a, b). While, in a formation with injectors and producers, the connectivity is between two points (in 2-D) or two lines (in 3-D). Therefore, point-to-point (Sadeghnejad et al. 2016a, b; Sadeghnejad & Masihi 2016; Javaheri & Sadeghenjad 2017; Soltani & Sadeghnejad 2018) or line-to-line connectivity (Sadeghnejad & Masihi 2017) may provide better connectivity predictions.

Illustration of a formation during secondary recovery, composed of overlapping sandbodies, modelled by continuum percolation. Connected (i.e. dark green) and individual (i.e. other clusters) clusters are depicted


References

Ashrafizadeh, M., Ahmad, R. S., & Sadeghnejad, S. (2012). Improvement of polymer flooding using in-situ releasing of smart nano-scale coated polymer particles in porous media. Energy Exploration & Exploitation30(6), 915-939.

Ashrafizadeh, M., SA, A. R., & Sadeghnejad, S. (2017). Enhanced polymer flooding using a novel nano‐scale smart polymer: Experimental investigation. The Canadian Journal of Chemical Engineering95(11), 2168-2175.

Farasat, A., Sefti, M. V., Sadeghnejad, S., & Saghafi, H. R. (2017a). Mechanical entrapment analysis of enhanced preformed particle gels (PPGs) in mature reservoirs. Journal of Petroleum Science and Engineering157, 441-450.

Farasat, A., Sefti, M. V., Sadeghnejad, S., & Saghafi, H. R. (2017b). Effects of reservoir temperature and water salinity on the swelling ratio performance of enhanced preformed particle gels. Korean Journal of Chemical Engineering34(5), 1509-1516.

Hernandez-Martinez, E., Perez-Muñoz, T., Velasco-Hernandez, J. X., Altamira-Areyan, A., & Velasquillo-Martinez, L. (2013). Facies recognition using multifractal Hurst analysis: Applications to well-log data. Mathematical Geosciences45(4), 471-486.

Javaheri, P., & Sadeghnejad, S. (2017). Effect of Injection Pattern Arrangements on Formation Connectivity During Water Flooding. In SPE Europec featured at 79th EAGE Conference and Exhibition. Society of Petroleum Engineers.

Partovi, S. M. A., & Sadeghnejad, S. (2017). Fractal parameters and well-logs investigation using automated well-to-well correlation. Computers & Geosciences103, 59-69.

Partovi, S., Sadeghnejad, S. (2018). Reservoir Rock Characterization Using Wavelet Transform and Fractals Dimension. Iranian Journal of Chemistry and Chemical Engineering (IJCCE).

Prodanović, M., Mehmani, A., & Sheppard, A. P. (2014). Imaged-based multiscale network modelling of microporosity in carbonates. Geological Society, London, Special Publications406, SP406-9.

Sadeghnejad, S. & Gostick, J. (2018). Reconstruction of vugular carbonate rocks by pore network modeling and image-based network technique, 19th annual conference of IAMG2018, Olumouoc Czech Republic.

Sadeghnejad, S., & Gostick, J. (2019). Multi-scale reconstruction of vuggy carbonates by pore network modeling and image-based technique, SPE Journal (In Press).

Sadeghnejad, S., Masihi, M., King, P. R., Shojaei, A., & Pishvaie, M. (2011a). A reservoir conductivity evaluation using percolation theory. Petroleum science and technology29(10), 1041-1053.

Sadeghnejad, S., Masihi, M., Pishvaie, M., Shojaei, A., & King, P. R. (2011b). Utilization of percolation approach to evaluate reservoir connectivity and effective permeability: a case study on North Pars gas field. Scientia Iranica18(6), 1391-1396.

Sadeghnejad, S., Masihi, M., Shojaei, A., Pishvaie, M., & King, P. R. (2012). Field scale characterization of geological formations using percolation theory. Transport in porous media92(2), 357-372.

Sadeghnejad, S., Masihi, M., Pishvaie, M., & King, P. R. (2013a). Rock type connectivity estimation using percolation theory. Mathematical Geosciences45(3), 321-340.

Sadeghnejad, S., Masihi, M., & King, P. R. (2013b). Dependency of percolation critical exponents on the exponent of power law size distribution. Physica A: Statistical Mechanics and its Applications392(24), 6189-6197.

Sadeghnejad, S., Masihi, M., Pishvaie, M., Shojaei, A., & King, P. R. (2014). Estimating the connected volume of hydrocarbon during early reservoir life by percolation theory. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects36(3), 301-308.

Sadeghnejad, S., Masihi, M., & King, P. R. (2016a). Study the Connectivity of Good Sands between two Wells Represented by Two Points Using Percolation Theory. In 78th EAGE Conference and Exhibition 2016.

Sadeghnejad, S., Masihi, M., King, P. R., & Gago, P. A. (2016b). Study the effect of connectivity between two wells on secondary recovery efficiency using percolation approach. In ECMOR XV-15th European Conference on the Mathematics of Oil Recovery.

Sadeghnejad, S., & Masihi, M. (2016). Point to point continuum percolation in two dimensions. Journal of Statistical Mechanics: Theory and Experiment2016(10), 103210.

Sadeghnejad, S., & Masihi, M. (2017). Analysis of a more realistic well representation during secondary recovery in 3-D continuum models. Computational Geosciences21(5-6), 1035-1048.

Sok, R. M., Knackstedt, M. A., Varslot, T., Ghous, A., Latham, S., & Sheppard, A. P. (2010). Pore scale characterization of carbonates at multiple scales: Integration of Micro-CT, BSEM, and FIBSEM. Petrophysics51(06).

Soltani, A., & Sadeghnejad, S. (2018). Scaling and critical behavior of lattice and continuum porous media with different connectivity configurations. Physica A: Statistical Mechanics and its Applications.

Younesian-Farid, H. & Sadeghnejad S. (2018). Modeling and simulation of geochemical reactions dring acid pre-flush to improve conformance control of pH-sensitive polymer flooding, 19th annual conference of IAMG2018, Olumouoc Czech Republic.